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635283a50f9bdb641c45d0da | 191e06b04083279c | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
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should probably proofread and complete it, then remove this comment. -->
# BERT-Tiny fine-tuned on Enron Spam Detection
This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://hu... | null | [
"apache-2.0"
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<h4 align="center">
<p>
<b>English</b> |
<a href="https://huggingface.co/BAAI/AquilaChat2-7B/blob/main/README_zh.md">简体中文</a>
</p>
</h4>
We opensource our **Aquila2** series, now including **Aquila2**, the base language models, namely **Aquila2-7B** and **Aquila2-... | null | [
"other"
] | null | null | null | null | null | [
"AutoModelForCausalLM",
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"aquila",
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"text"
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}
],
"relation": "quantized"
} | 715,358 | 1,943,153 | False | 2025-08-06T06:51:43Z | 2025-09-17T06:57:02Z | transformers | 73 | 1 | null | text-generation | {"parameters": {"F16": 778330176, "F8_E4M3": 3633315840}, "total": 4411646016} | [
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"... | null | {"architectures": ["Qwen3ForCausalLM"], "model_type": "qwen3", "quantization_config": {"quant_method": "fp8"}, "tokenizer_config": {"bos_token": null, "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0].role == 'system' %}\n {{- messages[0].content + '\\n\\n' }}\n {%-... | {
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<a href="https://chat.qwen.ai" target="_blank" style="margin: 2px;">
<img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;"/>
</a>
## Highlights
We introduce the updated version of the **Q... | null | [
"apache-2.0",
"https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507-FP8/blob/main/LICENSE"
] | null | null | 4,411,646,016 | null | null | [
"AutoModelForCausalLM",
"Qwen3ForCausalLM",
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] | [
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"Replace",
"ByteFallback",
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] | 46 | augtoma/qCammel-70-x | augtoma | null | 1,019 | 158,909 | False | 2023-07-23T00:39:34Z | 2023-07-27T16:47:02Z | transformers | 28 | 0 | null | text-generation | null | [
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qCammel-70 is a fine-tuned version of Llama-2 70B model, trained on a distilled dataset of 15,000 instructions using QLoRA. This model is optimized for academic medical knowledge and instruction-following capabilities.
## Model Details
*Note: Use of this model is governed by the Meta license. In order to ... | null | [
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} | 1,302 | 235,529 | False | 2024-06-25T02:12:42Z | 2024-10-30T20:27:39Z | transformers | 57 | 0 | [{"name": "dolphin-2.9.3-mistral-7B-32k", "results": [{"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "IFEval (0-Shot)", "type": "HuggingFaceH4/ifeval", "args": {"num_few_shot": 0}}, "metrics": [{"type": "inst_level_strict_acc and prompt_level_strict_acc", "value": 41.26, "name": "s... | text-generation | {"parameters": {"BF16": 7248039936}, "total": 7248039936} | [
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} | 363 | 157,793 | False | 2023-08-09T08:49:03Z | 2025-11-01T17:05:44Z | transformers | 15 | 0 | null | fill-mask | null | [
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] | null | {"architectures": ["RobertaForMaskedLM"], "model_type": "roberta", "tokenizer_config": {"bos_token": {"__type": "AddedToken", "content": "<s>", "lstrip": false, "normalized": true, "rstrip": false, "single_word": false}, "cls_token": {"__type": "AddedToken", "content": "<s>", "lstrip": false, "normalized": true, "rstri... | {
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**SecureBERT+** is an enhanced version of [SecureBERT](https://huggingface.co/ehsanaghaei/SecureBERT), trained on a corpus **eight times larger** than its predecessor and leveraging the computational power of **8×A100 GPUs**.
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"text-generation",
"unsloth",
"conversational",
"zho",
"eng",
"fra",
"spa",
"por",
"deu",
"ita",
"rus",
"jpn",
"kor",
"vie",
"tha",
"ara",
"arxiv:2309.00071",
"arxiv:2407.10671",
"base_model:Qwen/Qwen2.5-32B-Instruct",
"base_model:q... | null | {"architectures": ["Qwen2ForCausalLM"], "model_type": "qwen2", "quantization_config": {"load_in_4bit": true, "load_in_8bit": false, "quant_method": "bitsandbytes"}, "tokenizer_config": {"bos_token": null, "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "Qwen/Qwen2.5-32B-Instruct", "datasets": null, "eval_results": null, "language": ["zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name":... | # Finetune Llama 3.1, Gemma 2, Mistral 2-5x faster with 70% less memory via Unsloth!
We have a Qwen 2.5 (all model sizes) [free Google Colab Tesla T4 notebook](https://colab.research.google.com/drive/1Kose-ucXO1IBaZq5BvbwWieuubP7hxvQ?usp=sharing).
Also a [Qwen 2.5 conversational style notebook](https://colab.resear... | null | [
"apache-2.0"
] | null | [
"zho",
"eng",
"fra",
"spa",
"por",
"deu",
"ita",
"rus",
"jpn",
"kor",
"vie",
"tha",
"ara"
] | 33,741,233,162 | null | null | [
"AutoModelForCausalLM",
"Qwen2ForCausalLM",
"qwen2"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
688759f5f736b02318247808 | f487970e887bdd39 | 84283ad17b83fac1 | 128,815 | BPE | 127,741 | true | true | true | true | 818 | Sequence | Sequence | ByteLevel | [] | [
"Split",
"Split",
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 808 | stepfun-ai/step3 | stepfun-ai | null | 103,180 | 463,564 | False | 2025-07-28T11:07:33Z | 2026-01-29T07:41:03Z | transformers | 166 | 0 | null | image-text-to-text | {"parameters": {"BF16": 320969912832}, "total": 320969912832} | [
".gitattributes",
"LICENSE",
"README.md",
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"figures/stepfun-logo.png",
"generation_config.json",
"model-00001.safetensors",
"model-00002.safetensors",
"model-00003.safetensors",... | e2d67fc936dc60f8f606f2429728f1dea503e10d | [
"transformers",
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"conversational",
"custom_code",
"arxiv:2507.19427",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["Step3VLForConditionalGeneration"], "auto_map": {"AutoConfig": "configuration_step3.Step3VLConfig", "AutoModelForCausalLM": "modeling_step3.Step3vForConditionalGeneration"}, "model_type": "step3_vl", "processor_config": {"chat_template": "{% macro render_content(content) %} {% if content is string %}... | {
"auto_model": "AutoModelForSeq2SeqLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": null
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "image-text-to-text", "tags": null} | null | null | [
"apache-2.0"
] | null | null | 320,969,912,832 | null | null | [
"AutoModelForSeq2SeqLM",
"step3_vl",
"Step3VLForConditionalGeneration"
] | [
"image-text-to-text",
"text-generation"
] | [
"text",
"multimodal"
] | [
"text",
"image"
] | [
"text"
] |
659bc0aef82b15d663b71c1b | 191e06b04083279c | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 26 | Cielciel/aift-model-review-multiple-label-classification | Cielciel | {
"models": [
{
"_id": "621ffdc036468d709f174347",
"id": "distilbert/distilbert-base-uncased"
}
],
"relation": "finetune"
} | 15 | 153,530 | False | 2024-01-08T09:30:22Z | 2024-02-19T18:06:15Z | transformers | 1 | 0 | [{"name": "aift-model-review-multiple-label-classification", "results": []}] | text-classification | {"parameters": {"F32": 66961931}, "total": 66961931} | [
".gitattributes",
".ipynb_checkpoints/Aift-review-multiple-label-classification-workflow-checkpoint.ipynb",
"Aift-review-multiple-label-classification-workflow.ipynb",
"README.md",
"config.json",
"custom_container/Dockerfile",
"custom_container/README.md",
"custom_container/scripts/train-cloud.sh",
... | 2a3440fcefb90ebe7c844bd77978ef1240540b9b | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"text-embeddings-inference",
"endpoints_compatible",
"re... | null | {"architectures": ["DistilBertForSequenceClassification"], "model_type": "distilbert", "tokenizer_config": {"cls_token": "[CLS]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"base_model": "distilbert-base-uncased", "datasets": null, "eval_results": [], "language": null, "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": "aift-model-review-multiple-label-classification", "pipeline_tag": null, "tags": ["generated_from_tr... | <!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# aift-model-review-multiple-label-classification
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingfa... | null | [
"apache-2.0"
] | null | null | 66,961,931 | null | null | [
"DistilBertForSequenceClassification",
"distilbert",
"AutoModelForSequenceClassification"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
64a57f1515ce88eb013f6bc8 | c97a6495c5454551 | f26f356cd728caa8 | 49,408 | BPE | 48,894 | true | true | true | true | 2 | Sequence | Sequence | ByteLevel | [
"NFC",
"Replace",
"Lowercase"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 2 | shunk031/aesthetics-predictor-v2-sac-logos-ava1-l14-linearMSE | shunk031 | null | 12,629 | 219,698 | False | 2023-07-05T14:32:53Z | 2024-10-25T00:07:49Z | transformers | 10 | 0 | null | feature-extraction | {"parameters": {"I64": 257, "F32": 304894177}, "total": 304894434} | [
".gitattributes",
"config.json",
"configuration_predictor.py",
"merges.txt",
"model.safetensors",
"modeling_v2.py",
"preprocessor_config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | 684098de3856fa4678bf800efc05635de5b6cde5 | [
"transformers",
"pytorch",
"safetensors",
"aesthetics_predictor",
"feature-extraction",
"custom_code",
"region:us"
] | null | {"architectures": ["AestheticsPredictorV2Linear"], "auto_map": {"AutoConfig": "configuration_predictor.AestheticsPredictorConfig", "AutoModel": "modeling_v2.AestheticsPredictorV2Linear"}, "model_type": "aesthetics_predictor", "tokenizer_config": {"bos_token": {"__type": "AddedToken", "content": "<|startoftext|>", "lstr... | {
"auto_model": "AutoModel",
"custom_class": "modeling_v2.AestheticsPredictorV2Linear",
"pipeline_tag": "feature-extraction",
"processor": null
} | null | null | null | null | null | null | 304,894,434 | null | null | [
"AestheticsPredictorV2Linear",
"AutoModel",
"aesthetics_predictor",
"modeling_v2.AestheticsPredictorV2Linear"
] | [
"feature-extraction"
] | [
"multimodal"
] | [
"text"
] | [
"embeddings"
] |
621ffdc136468d709f17d2a8 | de0f98cd504e94de | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 317 | knkarthick/MEETING-SUMMARY-BART-LARGE-XSUM-SAMSUM-DIALOGSUM-AMI | knkarthick | null | 143 | 154,677 | False | 2022-03-02T23:29:05Z | 2023-10-03T10:59:56Z | transformers | 17 | 0 | {"error": "Schema validation error. \"model-index[0].results[0].dataset.type\" with value \"cnndaily/newyorkdaily/xsum/samsum/dialogsum/AMI Meeting Corpus\" fails to match the required pattern: /^(?:[\\w-]+\\/)?[\\w-.]+$/"} | summarization | {"parameters": {"F32": 406340696}, "total": 406340696} | [
".gitattributes",
"README.md",
"config.json",
"merges.txt",
"model.safetensors",
"pytorch_model.bin",
"special_tokens_map.json",
"tf_model.h5",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | ca5fb03f26d69dda49dba4451a1defda4b5a4ea8 | [
"transformers",
"pytorch",
"tf",
"safetensors",
"bart",
"text2text-generation",
"seq2seq",
"summarization",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["BartForConditionalGeneration"], "model_type": "bart", "tokenizer_config": {"unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>", "sep_token": "</s>", "cls_token": "<s>", "pad_token": "<pad>", "mask_token": "<mask>"}} | {
"auto_model": "AutoModelForSeq2SeqLM",
"custom_class": null,
"pipeline_tag": "text2text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": "en", "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": ["rouge"], "model_name": null, "pipeline_tag": null, "tags": ["bart", "seq2seq", "summarization"], "widget": [{"text": "Hi, I'm David and I... | Model obtained by Fine Tuning 'facebook/bart-large-xsum'
## Usage
# Example 1
```python
from transformers import pipeline
summarizer = pipeline("summarization", model="knkarthick/MEETING-SUMMARY-BART-LARGE-XSUM-SAMSUM-DIALOGSUM-AMI")
text = '''The tower is 324 metres (1,063 ft) tall, about the same height as an 81-stor... | null | [
"apache-2.0"
] | null | [
"en"
] | 406,340,696 | null | [
"rouge"
] | [
"bart",
"BartForConditionalGeneration",
"AutoModelForSeq2SeqLM"
] | [
"text2text-generation",
"summarization"
] | [
"text"
] | [
"text"
] | [
"text"
] |
6685888b466755422254da95 | b09ab9fd6ed532ef | f26f356cd728caa8 | 49,408 | BPE | 48,894 | true | true | true | true | 2 | Sequence | Sequence | ByteLevel | [
"NFC",
"Replace",
"Lowercase"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 414 | yujiepan/clip-vit-tiny-random-patch14-336 | yujiepan | null | 8,591 | 186,416 | False | 2024-07-03T17:21:15Z | 2024-07-03T17:25:38Z | transformers | 0 | 0 | null | zero-shot-image-classification | {"parameters": {"F16": 407784}, "total": 407784} | [
".gitattributes",
"README.md",
"config.json",
"merges.txt",
"model.safetensors",
"preprocessor_config.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | 82c57db6206f96e3d968011703149d354f8a0be3 | [
"transformers",
"safetensors",
"clip",
"zero-shot-image-classification",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["CLIPModel"], "model_type": "clip", "tokenizer_config": {"bos_token": "<|startoftext|>", "eos_token": "<|endoftext|>", "pad_token": "<|endoftext|>", "unk_token": "<|endoftext|>"}} | {
"auto_model": "AutoModelForZeroShotImageClassification",
"custom_class": null,
"pipeline_tag": "zero-shot-image-classification",
"processor": "AutoProcessor"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": "transformers", "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": []} | # yujiepan/clip-vit-tiny-random-patch14-336
This model is intended for debugging.
## Usage
```python
from transformers import CLIPProcessor, CLIPModel, CLIPConfig
from PIL import Image
import requests
import torch
model_id = "yujiepan/clip-vit-tiny-random-patch14-336"
model = CLIPModel.from_pretrained(model_id).cuda... | null | null | null | null | 407,784 | null | null | [
"AutoModelForZeroShotImageClassification",
"CLIPModel",
"clip"
] | [
"zero-shot-image-classification"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"logits"
] |
6881b876b7892d81c7e82dd6 | 7428ef9ae5ef868f | 67da9c236fa81925 | 151,669 | BPE | 151,387 | true | true | true | true | 26 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 47 | QuantTrio/Qwen3-235B-A22B-Instruct-2507-AWQ | QuantTrio | {
"models": [
{
"_id": "687de260234339fed21e768a",
"id": "Qwen/Qwen3-235B-A22B-Instruct-2507"
}
],
"relation": "quantized"
} | 6,199 | 173,582 | False | 2025-07-24T04:37:10Z | 2025-08-19T13:50:01Z | transformers | 10 | 0 | null | text-generation | {"parameters": {"I32": 233798893568, "F16": 1294740992}, "total": 235093634560} | [
".gitattributes",
"LICENSE",
"README.md",
"config.json",
"configuration.json",
"generation_config.json",
"merges.txt",
"model-00001-of-00025.safetensors",
"model-00002-of-00025.safetensors",
"model-00003-of-00025.safetensors",
"model-00004-of-00025.safetensors",
"model-00005-of-00025.safetenso... | 96548f657d772187f16e9f04bc50aec78a9f9cdd | [
"transformers",
"safetensors",
"qwen3_moe",
"text-generation",
"Qwen3",
"AWQ",
"量化修复",
"vLLM",
"conversational",
"arxiv:2505.09388",
"base_model:Qwen/Qwen3-235B-A22B-Instruct-2507",
"base_model:quantized:Qwen/Qwen3-235B-A22B-Instruct-2507",
"license:apache-2.0",
"endpoints_compatible",
"... | null | {"architectures": ["Qwen3MoeForCausalLM"], "model_type": "qwen3_moe", "quantization_config": {"quant_method": "awq", "bits": 4}, "tokenizer_config": {"bos_token": null, "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0].role == 'system' %}\n {{- messages[0].content + '\... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": ["Qwen/Qwen3-235B-A22B-Instruct-2507"], "datasets": null, "eval_results": null, "language": null, "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": "https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507/blob/main/LICENSE", "metrics": null, "model_name": nu... | # Qwen3-235B-A22B-Instruct-2507-AWQ
Base model [Qwen/Qwen3-235B-A22B-Instruct-2507](https://www.modelscope.cn/models/Qwen/Qwen3-235B-A22B-Instruct-2507)
### 【VLLM Launch Command for 8 GPUs (Single Node)】
<i>Note: When launching with 8 GPUs, --enable-expert-parallel must be specified; otherwise, the expert tensors can... | null | [
"apache-2.0",
"https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507/blob/main/LICENSE"
] | null | null | 235,093,634,560 | null | null | [
"qwen3_moe",
"AutoModelForCausalLM",
"Qwen3MoeForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
66f231053a6d90d35ef2d292 | 53ecf467267e2824 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 0 | jxm/cde-small-v1 | jxm | null | 425 | 154,989 | False | 2024-09-24T03:24:53Z | 2025-05-13T23:58:52Z | sentence-transformers | 284 | 0 | [{"name": "cde-small-v1", "results": [{"dataset": {"config": "en", "name": "MTEB AmazonCounterfactualClassification (en)", "revision": "e8379541af4e31359cca9fbcf4b00f2671dba205", "split": "test", "type": "mteb/amazon_counterfactual"}, "metrics": [{"type": "accuracy", "value": 87.02985074626866, "verified": false}, {"ty... | feature-extraction | null | [
".gitattributes",
"LICENSE",
"README.md",
"config.json",
"config_sentence_transformers.json",
"misc.py",
"model.py",
"model.safetensors",
"modules.json",
"random_strings.txt",
"sentence_bert_config.json",
"sentence_transformers_impl.py",
"tokenizer.json",
"tokenizer_config.json",
"vocab.... | e151df18af0d7f1d1c37b074fee58406ececf19f | [
"sentence-transformers",
"safetensors",
"feature-extraction",
"mteb",
"transformers",
"custom_code",
"arxiv:2410.02525",
"model-index",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["DatasetTransformer"], "auto_map": {"AutoConfig": "misc.ContextualModelConfig", "AutoModel": "model.DatasetTransformer"}, "tokenizer_config": {"cls_token": "[CLS]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
"auto_model": "AutoModel",
"custom_class": "model.DatasetTransformer",
"pipeline_tag": "feature-extraction",
"processor": null
} | {"tags": ["mteb", "transformers", "sentence-transformers"], "new_version": "jxm/cde-small-v2", "model-index": [{"name": "cde-small-v1", "results": [{"task": {"type": "Classification"}, "dataset": {"name": "MTEB AmazonCounterfactualClassification (en)", "type": "mteb/amazon_counterfactual", "config": "en", "split": "tes... | # cde-small-v1
<div style="background-color: #f8f9fa; border-left: 6px solid #007bff; padding: 10px 20px; margin: 20px; font-family: Arial, sans-serif; line-height: 1.6;">
<p>The <strong>cde-small-v1</strong> model has been deprecated. We highly recommend transitioning to the improved <strong>cde-small-v2</strong>... | null | null | null | null | null | null | null | [
"AutoModel",
"model.DatasetTransformer",
"DatasetTransformer"
] | [
"feature-extraction"
] | [
"multimodal"
] | [
"text"
] | [
"embeddings"
] |
66056052dcacdaf5839376d2 | 5d253ce26c8dd572 | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 572 | SuperAnnotate/roberta-large-llm-content-detector | SuperAnnotate | null | 573 | 160,811 | False | 2024-03-28T12:19:30Z | 2024-05-17T07:46:19Z | transformers | 8 | 0 | null | text-classification | null | [
".gitattributes",
"Calibration_plot.png",
"LICENSE",
"README.md",
"SA_logo.png",
"config.json",
"merges.txt",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | 27dc31dbeab06f5c55e80c60e0711932c5d8c678 | [
"transformers",
"pytorch",
"roberta",
"generated_text_detection",
"llm_content_detection",
"AI_detection",
"text-classification",
"en",
"dataset:Hello-SimpleAI/HC3",
"dataset:tum-nlp/IDMGSP",
"license:other",
"deploy:azure",
"region:us"
] | null | {"tokenizer_config": {"bos_token": "<s>", "cls_token": "<s>", "eos_token": "</s>", "mask_token": "<mask>", "pad_token": "<pad>", "sep_token": "</s>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": null,
"processor": null
} | {"base_model": null, "datasets": ["Hello-SimpleAI/HC3", "tum-nlp/IDMGSP"], "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "other", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-classification", "tags": ["roberta", "generated_... | <p align="center">
<img src="SA_logo.png" alt="SuperAnnotate Logo" width="100" height="100"/>
</p>
<h1 align="center">SuperAnnotate</h1>
<h3 align="center">
LLM Content Detector<br/>
Fine-Tuned RoBERTa Large<br/>
</h3>
## Description
The model designed to detect generated/synthetic text. \
At the moment, such functi... | null | [
"other"
] | [
"Hello-SimpleAI/HC3",
"tum-nlp/IDMGSP"
] | [
"en"
] | null | null | null | [
"AutoModel"
] | [
null,
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
672daf2a472b65839de003e4 | 1e0a2493163c0c63 | 6622292dca275a2d | 128,256 | BPE | 280,147 | false | true | true | true | 256 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 18 | RLHFlow/Llama3.1-8B-PRM-Deepseek-Data | RLHFlow | null | 9,878 | 172,199 | False | 2024-11-08T06:26:50Z | 2025-05-10T16:51:28Z | transformers | 37 | 0 | null | text-generation | {"parameters": {"BF16": 8030261248}, "total": 8030261248} | [
".gitattributes",
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"generation_config.json",
"model-00001-of-00004.safetensors",
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"... | 51100bc635eff6ad6a3e3fdab87703491461a88b | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": "<|begin_of_text|>", "chat_template": "{{- bos_token }}\n{%- if custom_tools is defined %}\n {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message is defined %}\n {%- set tools_in_user_messag... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
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} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": "transformers", "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": []} | This is a process-supervised reward (PRM) from the project [RLHFlow/RLHF-Reward-Modeling](https://github.com/RLHFlow/RLHF-Reward-Modeling)
The model is trained from [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) on [RLHFlow/Deepseek-PRM-Data](https://huggingface.co/datasets... | null | null | null | null | 8,030,261,248 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
66a46427f48d07feabda3d18 | 1e0a2493163c0c63 | 6622292dca275a2d | 128,256 | BPE | 280,147 | false | true | true | true | 256 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 18 | aifeifei798/DarkIdol-Llama-3.1-8B-Instruct-1.2-Uncensored | aifeifei798 | null | 525 | 156,198 | False | 2024-07-27T03:06:15Z | 2024-07-29T20:26:42Z | transformers | 226 | 1 | null | text-generation | {"parameters": {"BF16": 8030261248}, "total": 8030261248} | [
".gitattributes",
"DarkIdol-Llama-3.1-8B-Instruct-1.2-Uncensored.png",
"OpenAnarchistLicense.md",
"README.md",
"Uncensored_Test/harmful_behaviors.csv",
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"config.json",
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"model-00001-of-00009.safetensors",
"model-00002-of-00009.safeten... | 9cf1bd291cc6ab32b58b9aa518fa2f90d2bea406 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"roleplay",
"llama3",
"sillytavern",
"idol",
"facebook",
"meta",
"pytorch",
"llama-3",
"conversational",
"en",
"de",
"fr",
"it",
"pt",
"hi",
"es",
"th",
"zh",
"ko",
"ja",
"arxiv:2204.05149",
"doi:10.57967/hf... | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": "<|begin_of_text|>", "chat_template": "{{ '<|begin_of_text|>' }}{% if messages[0]['role'] == 'system' %}{% set system_message = messages[0]['content'] %}{% endif %}{% if system_message is defined %}{{ '<|start_header_id|>sy... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en", "de", "fr", "it", "pt", "hi", "es", "th", "zh", "ko", "ja"], "library_name": null, "license": "llama3.1", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-generation", "tags": ["roleplay... | # DarkIdol-Llama-3.1-8B-Instruct-1.2-Uncensored
## "transformers_version" >= "4.43.1"
## Model Information
The module combination has been readjusted to better fu... | null | [
"llama3.1"
] | null | [
"en",
"de",
"fr",
"it",
"pt",
"hi",
"es",
"th",
"zh",
"ko",
"ja"
] | 8,030,261,248 | null | null | [
"AutoModelForCausalLM",
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"LlamaForCausalLM"
] | [
"text-generation"
] | [
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] | [
"text"
] | [
"text"
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"Split",
"ByteLevel"
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"ByteLevel"
] | 111 | mlabonne/Daredevil-8B-abliterated | mlabonne | {
"models": [
{
"_id": "665234731b1ce9f4a62cb162",
"id": "mlabonne/Daredevil-8B"
}
],
"relation": "finetune"
} | 7,957 | 193,176 | False | 2024-05-26T14:32:53Z | 2026-01-22T10:26:10Z | transformers | 58 | 0 | null | text-generation | {"parameters": {"BF16": 8030261248}, "total": 8030261248} | [
".gitattributes",
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"generation_config.json",
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"tokenizer.json",
"... | 6f3527c9adb10df019bee590ec935a572329ff79 | [
"transformers",
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"conversational",
"base_model:mlabonne/Daredevil-8B",
"base_model:finetune:mlabonne/Daredevil-8B",
"license:other",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": "<|begin_of_text|>", "chat_template": "{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|e... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": ["mlabonne/Daredevil-8B"], "datasets": null, "eval_results": null, "language": null, "library_name": "transformers", "license": "other", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | # Daredevil-8B-abliterated
Abliterated version of [mlabonne/Daredevil-8B](https://huggingface.co/mlabonne/Daredevil-8B) using [failspy](https://huggingface.co/failspy)'s notebook.
It based on the ... | null | [
"other"
] | null | null | 8,030,261,248 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
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"text"
] | [
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63d7cd3d9df3238d89443f12 | dcbc70293f391d8f | 394884b56d9ee7f7 | 50,104 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 809 | marcuskd/norbert2_sentiment_test1 | marcuskd | null | 9,284 | 194,664 | False | 2023-01-30T13:59:25Z | 2023-01-31T13:51:56Z | transformers | 0 | 0 | null | text-classification | null | [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"training_args.bin",
"vocab.txt"
] | 9bc880b67af4a2df21d644a54c666a9502ef2520 | [
"transformers",
"pytorch",
"bert",
"text-classification",
"no",
"nb",
"nn",
"dataset:marcuskd/reviews_binary_not4_concat",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["BertForSequenceClassification"], "model_type": "bert", "tokenizer_config": {"cls_token": "[CLS]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
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} | {"base_model": null, "datasets": ["marcuskd/reviews_binary_not4_concat"], "eval_results": null, "language": ["no", "nb", "nn"], "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": ["accuracy", "recall", "precision", "f1"], "model_name": null, "pipeline_tag": null, "tags": null} | # Model Card for Model ID
Sentiment analysis for Norwegian reviews.
# Model Description
This model is trained using a self-concatinated dataset consisting of Norwegian Review Corpus dataset (https://github.com/ltgoslo/norec) and a sentiment dataset from huggingface (https://huggingface.co/datasets/sepidmnorozy/Norwe... | null | null | [
"marcuskd/reviews_binary_not4_concat"
] | [
"no",
"nb",
"nn"
] | null | null | [
"accuracy",
"recall",
"precision",
"f1"
] | [
"BertForSequenceClassification",
"bert",
"AutoModelForSequenceClassification"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
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"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 810 | tanzuml/phi3-4k | tanzuml | null | 2 | 24 | False | 2024-05-02T10:47:20Z | 2024-05-03T18:36:34Z | transformers | 3 | 0 | null | text-generation | {"parameters": {"BF16": 3821079552}, "total": 3821079552} | [
".gitattributes",
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... | 5b4fd8ec2762f6699a6bdf6a55a64b01c489ba4c | [
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"conversational",
"custom_code",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
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] | null | {"architectures": ["Phi3ForCausalLM"], "auto_map": {"AutoConfig": "microsoft/Phi-3-mini-4k-instruct--configuration_phi3.Phi3Config", "AutoModelForCausalLM": "microsoft/Phi-3-mini-4k-instruct--modeling_phi3.Phi3ForCausalLM"}, "model_type": "phi3", "tokenizer_config": {"bos_token": "<s>", "chat_template": "{{ bos_token }... | {
"auto_model": "AutoModelForCausalLM",
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} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": "transformers", "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": []} | # Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
IMPORTANT NOTE: This is for ONGOING EXPERIMENTATION and is not meant for reuse.
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model t... | null | null | null | null | 3,821,079,552 | null | null | [
"AutoModelForCausalLM",
"phi3",
"Phi3ForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
66700b5c4ebacb9e5d075cbe | bbf7b199022a53cd | 63fd2c3f8bd33400 | 50,281 | BPE | 50,009 | true | true | true | true | 29 | NFC | ByteLevel | ByteLevel | [
"NFC"
] | [
"ByteLevel"
] | [
"ByteLevel"
] | 811 | HelpingAI/HELVETE-3B | HelpingAI | null | 923 | 156,315 | False | 2024-06-17T10:09:32Z | 2024-08-26T09:53:29Z | transformers | 206 | 0 | null | text-generation | {"parameters": {"F32": 2537553920, "F16": 257771520}, "total": 2795325440} | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
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"nsfw-3b-iq4_xs-imat.gguf",
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"tok... | 5c990cd99280ef4179e7ca8c6f27514c09ba33f2 | [
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"dataset:UnfilteredAI/DAN",
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] | {"total": 2795325440, "architecture": "stablelm", "context_length": 4096, "quantize_imatrix_file": "llama.cpp/imatrix.dat", "chat_template": "{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assis... | {"architectures": ["StableLmForCausalLM"], "model_type": "stablelm", "tokenizer_config": {"bos_token": "<|im_start|>", "chat_template": "{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\... | {
"auto_model": "AutoModelForCausalLM",
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"pipeline_tag": "text-generation",
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} | {"base_model": null, "datasets": ["UnfilteredAI/DAN"], "eval_results": null, "language": ["en"], "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": ["character"], "model_name": null, "pipeline_tag": "text-generation", "tags": ["not-for-all-audiences"]} | null | null | null | [
"UnfilteredAI/DAN"
] | [
"en"
] | 2,795,325,440 | 2,795,325,440 | [
"character"
] | [
"AutoModelForCausalLM",
"stablelm",
"StableLmForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
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667d66f2d21bdcf60a756f32 | 3f287b04362fff24 | a1f1bd381dcb9329 | 128,256 | BPE | 280,147 | false | true | true | true | 256 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 158 | m42-health/Llama3-Med42-70B | m42-health | null | 2,340 | 160,584 | False | 2024-06-27T13:19:46Z | 2024-08-20T05:11:30Z | transformers | 73 | 0 | null | text-generation | {"parameters": {"BF16": 70553706496}, "total": 70553706496} | [
".gitattributes",
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"model-0... | 867064e18aad7bf3f4795f20dcb25a1108952543 | [
"transformers",
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"llama",
"text-generation",
"m42",
"health",
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"clinical-llm",
"conversational",
"en",
"arxiv:2408.06142",
"license:llama3",
"text-generation-inference",
"deploy:azure",
"region:us"
] | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": "<|begin_of_text|>", "chat_template": "{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|e... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
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} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en"], "library_name": null, "license": "llama3", "license_name": "llama3", "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-generation", "tags": ["m42", "health", "healthcare", "clinical-llm"], "inference": false} | # **Med42-v2 - A Suite of Clinically-aligned Large Language Models**
Med42-v2 is a suite of open-access clinical large language models (LLM) instruct and preference-tuned by M42 to expand access to medical knowledge. Built off LLaMA-3 and comprising either 8 or 70 billion parameters, these generative AI systems provide... | null | [
"llama3",
"llama3"
] | null | [
"en"
] | 70,553,706,496 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
657c0c88cfe79b5bf69658dc | 02217ffa5907f57a | 8bd7d71767538b82 | 32,000 | BPE | 61,249 | true | false | true | true | 3 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 130 | luffycodes/vicuna-class-tutor-7b-ep3 | luffycodes | null | 808 | 155,632 | False | 2023-12-15T08:21:28Z | 2023-12-15T08:29:08Z | transformers | 0 | 0 | null | text-generation | null | [
".gitattributes",
"README.md",
"added_tokens.json",
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"pytorch_model-00001-of-00003.bin",
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"pytorch_model-00003-of-00003.bin",
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"special_tokens_map.json",
"tokenizer.json",
"tokenizer_confi... | 724cf8becd6dbb0b67070c34711ef6d60ad5f216 | [
"transformers",
"pytorch",
"llama",
"text-generation",
"arxiv:2305.13272",
"license:llama2",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": "<s>", "eos_token": "</s>", "pad_token": "<unk>", "unk_token": "<unk>", "use_default_system_prompt": true}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": "llama2", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | If you use this work, please cite:
CLASS Meet SPOCK: An Education Tutoring Chatbot based on Learning Science Principles
https://arxiv.org/abs/2305.13272
```
@misc{sonkar2023class,
title={CLASS Meet SPOCK: An Education Tutoring Chatbot based on Learning Science Principles},
author={Shashank Sonkar and Lucy ... | null | [
"llama2"
] | null | null | null | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
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6610c4115983d51627b68e1e | 88cd37e502d27a0a | 0e7bc0977f200d1c | 32,000 | BPE | 58,980 | true | false | true | true | 3 | Sequence | null | Sequence | [
"Prepend",
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] | null | [
"Replace",
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] | 88 | Kukedlc/NeuralSynthesis-7B-v0.1 | Kukedlc | null | 9,708 | 197,754 | False | 2024-04-06T03:40:01Z | 2024-09-22T19:10:49Z | transformers | 3 | 0 | [{"name": "NeuralSynthesis-7B-v0.1", "results": [{"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "AI2 Reasoning Challenge (25-Shot)", "type": "ai2_arc", "config": "ARC-Challenge", "split": "test", "args": {"num_few_shot": 25}}, "metrics": [{"type": "acc_norm", "value": 73.04, "name"... | text-generation | {"parameters": {"BF16": 7241732096}, "total": 7241732096} | [
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".conf... | 6c8a9552bff22ee156eed3afd160bbd42ab5d207 | [
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] | null | {"architectures": ["MistralForCausalLM"], "model_type": "mistral", "tokenizer_config": {"bos_token": "<s>", "eos_token": "</s>", "pad_token": "<unk>", "unk_token": "<unk>", "use_default_system_prompt": false}} | {
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"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"language": ["en", "es"], "license": "apache-2.0", "tags": ["merge", "mergekit", "lazymergekit"], "model-index": [{"name": "NeuralSynthesis-7B-v0.1", "results": [{"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "AI2 Reasoning Challenge (25-Shot)", "type": "ai2_arc", "config": "ARC-C... | # NeuralSynthesis-7B-v0.1
NeuralSynthesis-7B-v0.1 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
#... | null | [
"apache-2.0"
] | null | [
"en",
"es"
] | 7,241,732,096 | null | null | [
"AutoModelForCausalLM",
"mistral",
"MistralForCausalLM"
] | [
"text-generation"
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] | [
"text"
] | [
"text"
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669940f6689d18a2d8cb5714 | 06265116b726e1b5 | d831300fa2535eef | 32,005 | Unigram | null | true | true | true | true | 7 | Sequence | Metaspace | Metaspace | [
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 812 | abbasgolestani/ag-nli-DeTS-sentence-similarity-v4 | abbasgolestani | null | 9 | 150,899 | False | 2024-07-18T16:21:10Z | 2024-08-08T16:31:53Z | transformers | 1 | 0 | null | text-classification | null | [
".DS_Store",
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] | 8fc59114ecee8060c22d9c0665ca1030f72bbb18 | [
"transformers",
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"camembert",
"text-classification",
"feature-extraction",
"sentence-similarity",
"en",
"nl",
"de",
"fr",
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"dataset:multi_nli",
"dataset:pietrolesci/nli_fever",
"license:apache-2.0",
"text-embeddings-inference",
"endpoints_compatible",
"deploy:a... | null | {"architectures": ["CamembertForSequenceClassification"], "model_type": "camembert", "tokenizer_config": {"bos_token": "<s>", "cls_token": "<s>", "eos_token": "</s>", "mask_token": {"__type": "AddedToken", "content": "<mask>", "lstrip": true, "normalized": true, "rstrip": false, "single_word": false}, "pad_token": "<pa... | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["multi_nli", "pietrolesci/nli_fever"], "eval_results": null, "language": ["en", "nl", "de", "fr", "it", "es"], "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-classification", "tags": ... | # Cross-Encoder for Sentence Similarity
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.
## Training Data
This model was trained on 6 different nli datasets. The model will predict a score between 0 (not... | null | [
"apache-2.0"
] | [
"multi_nli",
"pietrolesci/nli_fever"
] | [
"en",
"nl",
"de",
"fr",
"it",
"es"
] | null | null | null | [
"AutoModelForSequenceClassification",
"camembert",
"CamembertForSequenceClassification"
] | [
"sentence-similarity",
"feature-extraction",
"text-classification"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
6442a3e4f8b647fa4f50ea1f | e2ac7c1c8f085acd | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 6 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 813 | transformer3/H2-keywordextractor | transformer3 | null | 28 | 151,344 | False | 2023-04-21T14:55:32Z | 2023-04-21T15:15:03Z | transformers | 47 | 0 | null | summarization | null | [
".gitattributes",
"README.md",
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"merges.txt",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
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] | f6ba71771d073f4a69ea2949f8827f8704df043e | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain",
"summarization",
"unk",
"dataset:transformer3/autotrain-data-finance6",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["BartForConditionalGeneration"], "model_type": "bart", "tokenizer_config": {"bos_token": "<s>", "cls_token": "<s>", "eos_token": "</s>", "mask_token": "<mask>", "pad_token": "<pad>", "sep_token": "</s>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModelForSeq2SeqLM",
"custom_class": null,
"pipeline_tag": "text2text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["transformer3/autotrain-data-finance6"], "eval_results": null, "language": ["unk"], "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["autotrain", "summarization"], "widget": [{"text": ... | # Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 51355121740
- CO2 Emissions (in grams): 0.0329
## Validation Metrics
- Loss: 1.406
- Rouge1: 29.067
- Rouge2: 19.200
- RougeL: 26.900
- RougeLsum: 26.940
- Gen Len: 20.000
## Usage
You can use cURL to access this model:
```
$ curl -X POST -... | null | null | [
"transformer3/autotrain-data-finance6"
] | [
"unk"
] | null | null | null | [
"bart",
"BartForConditionalGeneration",
"AutoModelForSeq2SeqLM"
] | [
"text2text-generation",
"summarization"
] | [
"text"
] | [
"text"
] | [
"text"
] |
621ffdc136468d709f17ae9d | 336c2005a03d2a38 | f74d2fa1431f2349 | 256,008 | Unigram | null | true | true | true | true | 11 | Sequence | Metaspace | Metaspace | [
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 415 | facebook/xglm-7.5B | facebook | null | 1,618 | 162,733 | False | 2022-03-02T23:29:05Z | 2023-01-24T16:35:48Z | transformers | 60 | 0 | null | text-generation | null | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
"pytorch_model.bin",
"sentencepiece.bpe.model",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | 732d59308a844004bd9a4def972cc7c3896a38e0 | [
"transformers",
"pytorch",
"xglm",
"text-generation",
"multilingual",
"en",
"ru",
"zh",
"de",
"es",
"fr",
"ja",
"it",
"pt",
"el",
"ko",
"fi",
"id",
"tr",
"ar",
"vi",
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"bg",
"ca",
"hi",
"et",
"bn",
"ta",
"ur",
"sw",
"te",
"eu",
"my",
"ht",
"qu"... | null | {"architectures": ["XGLMForCausalLM"], "model_type": "xglm", "tokenizer_config": {"bos_token": "<s>", "eos_token": "</s>", "sep_token": "</s>", "cls_token": "<s>", "unk_token": "<unk>", "pad_token": "<pad>"}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["multilingual", "en", "ru", "zh", "de", "es", "fr", "ja", "it", "pt", "el", "ko", "fi", "id", "tr", "ar", "vi", "th", "bg", "ca", "hi", "et", "bn", "ta", "ur", "sw", "te", "eu", "my", "ht", "qu"], "library_name": null, "license": "mit", "license_... | # XGLM-7.5B
XGLM-7.5B is a multilingual autoregressive language model (with 7.5 billion parameters) trained on a balanced corpus of a diverse set of languages totaling 500 billion sub-tokens. It was introduced in the paper [Few-shot Learning with Multilingual Language Models](https://arxiv.org/abs/2112.10668) by Xi Vi... | null | [
"mit"
] | null | [
"multilingual",
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"fr",
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"pt",
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"ca",
"hi",
"et",
"bn",
"ta",
"ur",
"sw",
"te",
"eu",
"my",
"ht",
"qu"
] | null | null | null | [
"AutoModelForCausalLM",
"xglm",
"XGLMForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
66aa30a4583f023de440978f | 8313d236041dc15b | d9a402e557e08381 | 50,256 | BPE | 49,995 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 814 | RUSpam/spam_deberta_v4 | RUSpam | null | 1,336 | 162,097 | False | 2024-07-31T12:40:04Z | 2024-09-07T10:59:31Z | transformers | 8 | 0 | [{"name": "spam_deberta_v4", "results": [{"task": {"name": "\u041a\u043b\u0430\u0441\u0441\u0438\u0444\u0438\u043a\u0430\u0446\u0438\u044f \u0442\u0435\u043a\u0441\u0442\u0430", "type": "text-classification"}, "dataset": {"name": "RUSpam/russian_spam_dataset", "type": "RUSpam/russian_spam_dataset"}, "metrics": [{"name"... | text-classification | {"parameters": {"F32": 82113026}, "total": 82113026} | [
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"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | cc71fc1164f24a908350f794bba61758954787de | [
"transformers",
"pytorch",
"safetensors",
"deberta",
"text-classification",
"spam-detection",
"russian",
"ru",
"dataset:RUSpam/spam_dataset_v4",
"license:mit",
"model-index",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["DebertaForSequenceClassification"], "model_type": "deberta", "tokenizer_config": {"bos_token": "<s>", "cls_token": "<s>", "eos_token": "</s>", "mask_token": "<mask>", "pad_token": "<pad>", "sep_token": "</s>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"datasets": ["RUSpam/spam_dataset_v4"], "language": "ru", "license": "mit", "metrics": ["F1"], "tags": ["spam-detection", "text-classification", "russian"], "model-index": [{"name": "spam_deberta_v4", "results": [{"task": {"type": "text-classification", "name": "\u041a\u043b\u0430\u0441\u0441\u0438\u0444\u0438\u043a\u... | # RUSpam/spam_deberta_v4
## Описание
Это модель определения спама, основанная на архитектуре Deberta, дообученная на русскоязычных данных о спаме. Она классифицирует текст как спам или не спам.
## Использование
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
model... | null | [
"mit"
] | [
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"F1"
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] | [
"text"
] | [
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"BertPreTokenizer"
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"WordPiece"
] | 0 | research-dump/bert-large-uncased_wikistance_v1 | research-dump | null | 3 | 3 | False | 2024-07-27T13:59:27Z | 2024-07-27T14:00:07Z | transformers | 0 | 0 | null | text-classification | {"parameters": {"F32": 335145988}, "total": 335145988} | [
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<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated... | null | null | null | null | 335,145,988 | null | null | [
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6833fd2a4f3c406a19b5f8e9 | 7428ef9ae5ef868f | 67da9c236fa81925 | 151,669 | BPE | 151,387 | true | true | true | true | 26 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 47 | optimum-internal-testing/tiny-random-qwen3 | optimum-internal-testing | null | 3,957 | 177,384 | False | 2025-05-26T05:33:30Z | 2025-05-26T05:37:11Z | transformers | 0 | 0 | null | text-generation | {"parameters": {"F32": 9798080}, "total": 9798080} | [
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"conversational",
"arxiv:1910.09700",
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] | null | {"architectures": ["Qwen3ForCausalLM"], "model_type": "qwen3", "tokenizer_config": {"bos_token": null, "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0].role == 'system' %}\n {{- messages[0].content + '\\n\\n' }}\n {%- endif %}\n {{- \"# Tools\\n\\nYou may call o... | {
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<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated... | null | null | null | null | 9,798,080 | null | null | [
"AutoModelForCausalLM",
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"qwen3"
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"text-generation"
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"Metaspace"
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"Replace",
"ByteFallback",
"Fuse"
] | 533 | sbintuitions/modernbert-ja-30m | sbintuitions | null | 28,274 | 208,752 | False | 2025-02-19T10:27:20Z | 2025-05-01T03:42:55Z | transformers | 5 | 0 | null | fill-mask | {"parameters": {"F32": 36873728}, "total": 36873728} | [
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"model.safetensors",
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"tokenizer.json",
"tokenizer.model",
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"arxiv:2412.13663",
"arxiv:2104.09864",
"arxiv:2404.10830",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["ModernBertForMaskedLM"], "model_type": "modernbert", "tokenizer_config": {"bos_token": "<s>", "cls_token": "<cls>", "eos_token": "</s>", "mask_token": "<mask>", "pad_token": "<pad>", "sep_token": "<sep>", "unk_token": "<unk>", "use_default_system_prompt": false}} | {
"auto_model": "AutoModelForMaskedLM",
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"pipeline_tag": "fill-mask",
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} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["ja", "en"], "library_name": "transformers", "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "fill-mask", "tags": null} | # ModernBERT-Ja-30M
This repository provides Japanese ModernBERT trained by [SB Intuitions](https://www.sbintuitions.co.jp/).
[ModernBERT](https://arxiv.org/abs/2412.13663) is a new variant of the BERT model that combines local and global attention, allowing it to handle long sequences while maintaining high computat... | null | [
"mit"
] | null | [
"ja",
"en"
] | 36,873,728 | null | null | [
"ModernBertForMaskedLM",
"modernbert",
"AutoModelForMaskedLM"
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"fill-mask"
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"text"
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"text"
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"logits"
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"Split",
"Split",
"Split",
"Digits",
"ByteLevel"
] | [
"ByteLevel"
] | 475 | RedHatAI/DeepSeek-Coder-V2-Lite-Instruct-FP8 | RedHatAI | null | 51,339 | 353,036 | False | 2024-07-17T18:33:42Z | 2024-07-18T19:05:40Z | transformers | 11 | 0 | null | text-generation | {"parameters": {"BF16": 422964736, "F8_E4M3": 15283519488}, "total": 15706484224} | [
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"BertPreTokenizer"
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"WordPiece"
] | 815 | Rifky/Indobert-QA | Rifky | {
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} | 476 | 152,845 | False | 2022-03-02T23:29:04Z | 2025-01-16T18:17:00Z | transformers | 15 | 0 | null | question-answering | {"parameters": {"I64": 512, "F32": 109969154}, "total": 109969666} | [
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"vocab.txt"
] | 395df78eaa9dcb4dd6f0264a5d075bd603162617 | [
"transformers",
"pytorch",
"safetensors",
"bert",
"question-answering",
"id",
"dataset:rajpurkar/squad_v2",
"base_model:indolem/indobert-base-uncased",
"base_model:finetune:indolem/indobert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["BertForQuestionAnswering"], "model_type": "bert", "tokenizer_config": {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}} | {
"auto_model": "AutoModelForQuestionAnswering",
"custom_class": null,
"pipeline_tag": "question-answering",
"processor": "AutoTokenizer"
} | {"base_model": ["indolem/indobert-base-uncased"], "datasets": ["rajpurkar/squad_v2"], "eval_results": null, "language": ["id"], "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": ["f1"], "model_name": null, "pipeline_tag": null, "tags": null} | [Github](https://github.com/rifkybujana/IndoBERT-QA)
*Notice of Attribution Clarification*
This is to clarify that *Muhammad Fajrin Buyang Daffa* is not, and has never been, a part of this project. They have made no contributions to this repository, and as such, shall not be given any attribution in relation to this ... | null | [
"apache-2.0"
] | [
"rajpurkar/squad_v2"
] | [
"id"
] | 109,969,666 | null | [
"f1"
] | [
"AutoModelForQuestionAnswering",
"bert",
"BertForQuestionAnswering"
] | [
"question-answering"
] | [
"text"
] | [
"text"
] | [
"text"
] |
6800ced69ff7a46b49a413b3 | b647996d6543f730 | 7ab62ce4e32550df | 151,674 | BPE | 151,387 | true | true | true | true | 31 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 172 | OpenGVLab/InternVL3-8B-AWQ | OpenGVLab | {
"models": [
{
"_id": "67f7f4715ede5f7cfc203287",
"id": "OpenGVLab/InternVL3-8B"
}
],
"relation": "quantized"
} | 506 | 163,000 | False | 2025-04-17T09:50:14Z | 2025-09-11T12:50:20Z | transformers | 8 | 0 | null | image-text-to-text | null | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"configuration_intern_vit.py",
"configuration_internvl_chat.py",
"conversation.py",
"generation_config.json",
"inputs_stats.pth",
"merges.txt",
"modeling_intern_vit.py",
"modeling_internvl_chat.py",
"outputs_stats.pth",
"pr... | 020820e08d63c09e25ade36b191f195ac6da8a77 | [
"transformers",
"pytorch",
"internvl_chat",
"feature-extraction",
"internvl",
"custom_code",
"image-text-to-text",
"conversational",
"multilingual",
"dataset:OpenGVLab/MMPR-v1.2",
"arxiv:2312.14238",
"arxiv:2404.16821",
"arxiv:2412.05271",
"arxiv:2411.10442",
"arxiv:2504.10479",
"arxiv... | null | {"architectures": ["InternVLChatModel"], "auto_map": {"AutoConfig": "configuration_internvl_chat.InternVLChatConfig", "AutoModel": "modeling_internvl_chat.InternVLChatModel", "AutoModelForCausalLM": "modeling_internvl_chat.InternVLChatModel"}, "model_type": "internvl_chat", "tokenizer_config": {"bos_token": null, "chat... | {
"auto_model": "AutoModel",
"custom_class": "modeling_internvl_chat.InternVLChatModel",
"pipeline_tag": "feature-extraction",
"processor": null
} | {"base_model": ["OpenGVLab/InternVL3-8B"], "datasets": ["OpenGVLab/MMPR-v1.2"], "eval_results": null, "language": ["multilingual"], "library_name": "transformers", "license": "other", "license_name": "qwen", "license_link": "https://huggingface.co/Qwen/Qwen2.5-72B-Instruct/blob/main/LICENSE", "metrics": null, "model_na... | # InternVL3-8B
[\[📂 GitHub\]](https://github.com/OpenGVLab/InternVL) [\[📜 InternVL 1.0\]](https://huggingface.co/papers/2312.14238) [\[📜 InternVL 1.5\]](https://huggingface.co/papers/2404.16821) [\[📜 InternVL 2.5\]](https://huggingface.co/papers/2412.05271) [\[📜 InternVL2.5-MPO\]](https://huggingface.co/paper... | null | [
"other",
"qwen",
"https://huggingface.co/Qwen/Qwen2.5-72B-Instruct/blob/main/LICENSE"
] | [
"OpenGVLab/MMPR-v1.2"
] | [
"multilingual"
] | null | null | null | [
"AutoModel",
"internvl_chat",
"InternVLChatModel",
"modeling_internvl_chat.InternVLChatModel"
] | [
"image-text-to-text",
"feature-extraction"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"text",
"embeddings"
] |
66d9c23428520589aa7f78fa | d9f2939febca4c51 | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 816 | ibm-granite/granite-guardian-hap-38m | ibm-granite | null | 1,893 | 158,120 | False | 2024-09-05T14:37:40Z | 2024-12-19T19:54:01Z | transformers | 44 | 0 | null | text-classification | {"parameters": {"F32": 38455682}, "total": 38455682} | [
".gitattributes",
"38m_comparison_a.png",
"38m_comparison_b.png",
"38m_latency.png",
"README.md",
"config.json",
"merges.txt",
"model.safetensors",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | faf9291163c7363d4a7ba7fc1dc72243214af931 | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"text-classification",
"en",
"license:apache-2.0",
"text-embeddings-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["RobertaForSequenceClassification"], "model_type": "roberta", "tokenizer_config": {"bos_token": {"__type": "AddedToken", "content": "<s>", "lstrip": false, "normalized": true, "rstrip": false, "single_word": false}, "cls_token": {"__type": "AddedToken", "content": "<s>", "lstrip": false, "normalized"... | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | ## Granite-Guardian-HAP-38m
## Model Summary
This model is IBM's lightweight, 4-layer toxicity binary classifier for English. Its latency characteristics make it a suitable guardrail for any large language model. It can also be used for bulk processing of data where high throughput is needed. It has been trained on se... | null | [
"apache-2.0"
] | null | [
"en"
] | 38,455,682 | null | null | [
"roberta",
"AutoModelForSequenceClassification",
"RobertaForSequenceClassification"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
663c2d84a2da73482b7ca081 | 6e6a83dc4b556c95 | a1f1bd381dcb9329 | 128,256 | BPE | 280,147 | false | true | true | true | 256 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 111 | QuixiAI/Llama-3-8B-Instruct-abliterated-v2 | QuixiAI | null | 8,124 | 188,125 | False | 2024-05-09T01:57:24Z | 2024-05-12T17:50:18Z | transformers | 29 | 0 | null | text-generation | {"parameters": {"BF16": 8030261248}, "total": 8030261248} | [
".gitattributes",
"README.md",
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"... | c99bd5b5e73f1d24504d1145b8a8dfd66c0a2378 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"license:llama3",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": "<|begin_of_text|>", "chat_template": "{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|e... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": "transformers", "license": "llama3", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | null | null | [
"llama3"
] | null | null | 8,030,261,248 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
65cc3a3d188a421444881951 | 83bf28f8b6767a2f | ae88e903e2b563a1 | 50,295 | BPE | 50,000 | false | true | true | true | 39 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 85 | NingLab/eCeLLM-S | NingLab | null | 11,608 | 219,718 | False | 2024-02-14T03:57:49Z | 2024-07-08T15:08:16Z | transformers | 1 | 0 | null | text-generation | {"parameters": {"F16": 2779683840}, "total": 2779683840} | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"generation_config.json",
"merges.txt",
"model-00001-of-00002.safetensors",
"model-00002-of-00002.safetensors",
"model.safetensors.index.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json... | 35a17d861c51f968a54a010c5c414b72b0c233fa | [
"transformers",
"safetensors",
"phi",
"text-generation",
"custom_code",
"dataset:NingLab/ECInstruct",
"license:cc-by-4.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["PhiForCausalLM"], "auto_map": {"AutoConfig": "microsoft/phi-2--configuration_phi.PhiConfig", "AutoModelForCausalLM": "microsoft/phi-2--modeling_phi.PhiForCausalLM"}, "model_type": "phi", "tokenizer_config": {"bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "unk_token": "<|endoftext|>"}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["NingLab/ECInstruct"], "eval_results": null, "language": null, "library_name": null, "license": "cc-by-4.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | # eCeLLM-S
This repo contains the models for "eCeLLM: Generalizing Large Language Models for E-commerce from Large-scale, High-quality Instruction Data"
## eCeLLM Models
Leveraging ECInstruct, we develop eCeLLM by instruction tuning general-purpose LLMs (base models).
The eCeLLM-S model is instruction-tuned from the ... | null | [
"cc-by-4.0"
] | [
"NingLab/ECInstruct"
] | null | 2,779,683,840 | null | null | [
"AutoModelForCausalLM",
"phi",
"PhiForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
66e97b7ca02c7e0f6b563790 | 848b60b6cd304b65 | ffc911e785abe60e | 151,665 | BPE | 151,387 | true | true | true | true | 22 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 15 | Qwen/Qwen2.5-7B-Instruct-GPTQ-Int8 | Qwen | {
"models": [
{
"_id": "66e81cbcd683a3f4e5291bbf",
"id": "Qwen/Qwen2.5-7B-Instruct"
}
],
"relation": "quantized"
} | 15,665 | 211,798 | False | 2024-09-17T12:52:12Z | 2024-10-18T02:57:10Z | transformers | 18 | 0 | null | text-generation | {"parameters": {"F16": 1090328064, "I32": 6525288448}, "total": 7615616512} | [
".gitattributes",
"LICENSE",
"README.md",
"config.json",
"generation_config.json",
"merges.txt",
"model-00001-of-00003.safetensors",
"model-00002-of-00003.safetensors",
"model-00003-of-00003.safetensors",
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"tokenizer.json",
"tokenizer_config.json",
"vocab.json"... | 6711f2b6fc95545efe6018c469eca6832e28cefd | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"chat",
"conversational",
"en",
"arxiv:2309.00071",
"arxiv:2407.10671",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:quantized:Qwen/Qwen2.5-7B-Instruct",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatibl... | null | {"architectures": ["Qwen2ForCausalLM"], "model_type": "qwen2", "quantization_config": {"bits": 8, "quant_method": "gptq"}, "tokenizer_config": {"bos_token": null, "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "Qwen/Qwen2.5-7B-Instruct", "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": "https://huggingface.co/Qwen/Qwen2.5-7B-Instruct-GPTQ-Int8/blob/main/LICENSE", "metrics": null, "model_name": null, "pipel... | # Qwen2.5-7B-Instruct-GPTQ-Int8
## Introduction
Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters. Qwen2.5 brings the following improvements upon Qwen2:
- Significantly *... | null | [
"apache-2.0",
"https://huggingface.co/Qwen/Qwen2.5-7B-Instruct-GPTQ-Int8/blob/main/LICENSE"
] | null | [
"en"
] | 7,615,616,512 | null | null | [
"AutoModelForCausalLM",
"Qwen2ForCausalLM",
"qwen2"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
64013e3ddf246dca23fd6a94 | 58967ba68263f0aa | 2f8d5857ae5fa330 | 50,277 | BPE | 50,009 | true | true | true | true | 25 | NFC | ByteLevel | ByteLevel | [
"NFC"
] | [
"ByteLevel"
] | [
"ByteLevel"
] | 86 | togethercomputer/GPT-NeoXT-Chat-Base-20B | togethercomputer | null | 1,000 | 154,376 | False | 2023-03-03T00:24:29Z | 2023-03-30T21:00:24Z | transformers | 694 | 0 | null | text-generation | null | [
".gitattributes",
"README.md",
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"tokenizer.... | d386708e84d862a65f7d2b4989f64750cb657227 | [
"transformers",
"pytorch",
"gpt_neox",
"text-generation",
"en",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["GPTNeoXForCausalLM"], "model_type": "gpt_neox", "tokenizer_config": {"bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "unk_token": "<|endoftext|>"}} | {
"auto_model": "AutoModelForCausalLM",
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"pipeline_tag": "text-generation",
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} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en"], "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | ***<p style="font-size: 24px">Feel free to try out our [OpenChatKit feedback app](https://huggingface.co/spaces/togethercomputer/OpenChatKit)!</p>***
# GPT-NeoXT-Chat-Base-20B-v0.16
> TLDR: As part of OpenChatKit (codebase available [here](https://github.com/togethercomputer/OpenChaT)),
> GPT-NeoXT-Chat-Base-20B-v0.1... | null | [
"apache-2.0"
] | null | [
"en"
] | null | null | null | [
"gpt_neox",
"AutoModelForCausalLM",
"GPTNeoXForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
63d8ca58649b0868ef2af7c9 | 53ecf467267e2824 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 0 | markusbayer/CySecBERT | markusbayer | null | 1,582 | 153,555 | False | 2023-01-31T07:59:20Z | 2024-10-28T09:15:07Z | transformers | 19 | 0 | null | fill-mask | {"parameters": {"I64": 512, "F32": 109514298}, "total": 109514810} | [
".gitattributes",
"README.md",
"all_results.json",
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"train_results.json",
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] | c9e04e355a2bc0690b6e55833a9895d7591afcd0 | [
"transformers",
"pytorch",
"safetensors",
"bert",
"fill-mask",
"Cybersecurity",
"Cyber Security",
"Information Security",
"Computer Science",
"Cyber Threats",
"Vulnerabilities",
"Vulnerability",
"Malware",
"Attacks",
"en",
"arxiv:2212.02974",
"license:apache-2.0",
"endpoints_compat... | null | {"architectures": ["BertForMaskedLM"], "model_type": "bert", "tokenizer_config": {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}} | {
"auto_model": "AutoModelForMaskedLM",
"custom_class": null,
"pipeline_tag": "fill-mask",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en"], "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["Cybersecurity", "Cyber Security", "Information Security", "Computer Science", "... | # Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
CySecBERT is a domain-adapted version of the BERT model tailored for cybersecurity tasks.
It is based on a [Cybersecurity Dataset](https://github.com/PEASEC/cybersecurity_dataset) consisting of 4.3 million entries of Twitter, Blogs,... | null | [
"apache-2.0"
] | null | [
"en"
] | 109,514,810 | null | null | [
"AutoModelForMaskedLM",
"bert",
"BertForMaskedLM"
] | [
"fill-mask"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
621ffdc136468d709f17c745 | 5ed002b98254ad6d | 1f9430a5bdcfa504 | 57,526 | Unigram | null | true | true | true | false | 9 | Precompiled | Sequence | null | [
"Precompiled"
] | [
"WhitespaceSplit",
"Metaspace"
] | null | 817 | hyunwoongko/asian-bart-ecjk | hyunwoongko | null | 335 | 150,143 | False | 2022-03-02T23:29:05Z | 2021-04-01T07:36:52Z | transformers | 7 | 0 | null | null | null | [
".DS_Store",
".gitattributes",
"config.json",
"pytorch_model.bin",
"sentencepiece.bpe.model",
"tokenizer.json"
] | a9da2204e42df8afa450e8228255b1e109bc5c63 | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["MBartForConditionalGeneration"], "model_type": "mbart"} | {
"auto_model": "AutoModelForSeq2SeqLM",
"custom_class": null,
"pipeline_tag": "text2text-generation",
"processor": "AutoTokenizer"
} | null | null | null | null | null | null | null | null | null | [
"AutoModelForSeq2SeqLM",
"MBartForConditionalGeneration",
"mbart"
] | [
"text2text-generation"
] | null | null | null |
6661e6f4cb9f38e879707cbe | 34093184d10f76a9 | 35b001f3a491f9f9 | 151,646 | BPE | 151,387 | true | true | true | true | 3 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 53 | unsloth/Qwen2-1.5B-Instruct-bnb-4bit | unsloth | null | 2,466 | 160,568 | False | 2024-06-06T16:42:28Z | 2024-09-03T03:45:17Z | transformers | 2 | 0 | null | text-generation | {"parameters": {"F32": 133280, "BF16": 233518592, "U8": 1351206702}, "total": 1584858574} | [
".gitattributes",
"README.md",
"added_tokens.json",
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"merges.txt",
"model.safetensors",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | 696d3f5d7409ab03fcbf6e1b96473d2670660f32 | [
"transformers",
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"unsloth",
"conversational",
"en",
"license:apache-2.0",
"text-generation-inference",
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"4-bit",
"bitsandbytes",
"region:us"
] | null | {"architectures": ["Qwen2ForCausalLM"], "model_type": "qwen2", "quantization_config": {"load_in_4bit": true, "load_in_8bit": false, "quant_method": "bitsandbytes"}, "tokenizer_config": {"bos_token": null, "chat_template": "{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_star... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["unsloth", "transformers", "qwen2"]} | # Finetune Mistral, Gemma, Llama 2-5x faster with 70% less memory via Unsloth!
We have a Google Colab Tesla T4 notebook for Qwen2 7b here: https://colab.research.google.com/drive/1mvwsIQWDs2EdZxZQF9pRGnnOvE86MVvR?usp=sharing
And a Colab notebook for [Qwen2 0.5b](https://colab.research.google.com/drive/1-7tjDdMAyeCueyL... | null | [
"apache-2.0"
] | null | [
"en"
] | 1,584,858,574 | null | null | [
"AutoModelForCausalLM",
"Qwen2ForCausalLM",
"qwen2"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
67a4e360ab1785f83390dcc0 | 848b60b6cd304b65 | ffc911e785abe60e | 151,665 | BPE | 151,387 | true | true | true | true | 22 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 15 | RedHatAI/Qwen2.5-VL-7B-Instruct-FP8-Dynamic | RedHatAI | {
"models": [
{
"_id": "6795ffcd88cd7c0294702a72",
"id": "Qwen/Qwen2.5-VL-7B-Instruct"
}
],
"relation": "quantized"
} | 1,238 | 326,285 | False | 2025-02-06T16:29:20Z | 2025-04-03T15:52:38Z | transformers | 5 | 0 | null | image-text-to-text | {"parameters": {"BF16": 1768268800, "F8_E4M3": 6525288448}, "total": 8293557248} | [
".gitattributes",
"README.md",
"added_tokens.json",
"chat_template.json",
"config.json",
"generation_config.json",
"merges.txt",
"model-00001-of-00003.safetensors",
"model-00002-of-00003.safetensors",
"model-00003-of-00003.safetensors",
"model.safetensors.index.json",
"preprocessor_config.json... | 51ba7614d109476f6a217017d9a270fc3578d94f | [
"transformers",
"safetensors",
"qwen2_5_vl",
"image-text-to-text",
"vllm",
"vision",
"fp8",
"conversational",
"en",
"base_model:Qwen/Qwen2.5-VL-7B-Instruct",
"base_model:quantized:Qwen/Qwen2.5-VL-7B-Instruct",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"co... | null | {"architectures": ["Qwen2_5_VLForConditionalGeneration"], "model_type": "qwen2_5_vl", "quantization_config": {"quant_method": "compressed-tensors"}, "processor_config": {"chat_template": "{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first an... | {
"auto_model": "AutoModelForImageTextToText",
"custom_class": null,
"pipeline_tag": "image-text-to-text",
"processor": "AutoProcessor"
} | {"base_model": "Qwen/Qwen2.5-VL-7B-Instruct", "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": "https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md", "metrics": null, "model_nam... | null | null | [
"apache-2.0",
"https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md"
] | null | [
"en"
] | 8,293,557,248 | null | null | [
"qwen2_5_vl",
"AutoModelForImageTextToText",
"Qwen2_5_VLForConditionalGeneration"
] | [
"image-text-to-text"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"text"
] |
621ffdc136468d709f17f021 | e387f2f9dec84ea9 | ef4806e2ade9022f | 32,100 | Unigram | null | true | true | true | true | 103 | Precompiled | Sequence | Metaspace | [
"Precompiled"
] | [
"WhitespaceSplit",
"Metaspace"
] | [
"Metaspace"
] | 516 | prithivida/passive_to_active_styletransfer | prithivida | null | 9 | 149,070 | False | 2022-03-02T23:29:05Z | 2021-06-23T13:45:25Z | transformers | 5 | 0 | null | null | null | [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"spiece.model",
"tokenizer.json",
"tokenizer_config.json",
"training_args.bin"
] | 81b7ef1c02f244ac9030d928f2c5a01a037c2430 | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["T5ForConditionalGeneration"], "model_type": "t5", "tokenizer_config": {"eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>"}} | {
"auto_model": "AutoModelForSeq2SeqLM",
"custom_class": null,
"pipeline_tag": "text2text-generation",
"processor": "AutoTokenizer"
} | null | ## This model belongs to the Styleformer project
[Please refer to github page](https://github.com/PrithivirajDamodaran/Styleformer) | null | null | null | null | null | null | null | [
"t5",
"T5ForConditionalGeneration",
"AutoModelForSeq2SeqLM"
] | [
"text2text-generation"
] | null | null | null |
689ecb9afc79f2b3ec739847 | 7428ef9ae5ef868f | 67da9c236fa81925 | 151,669 | BPE | 151,387 | true | true | true | true | 26 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 47 | AIDC-AI/Ovis2.5-9B | AIDC-AI | null | 3,483 | 177,180 | False | 2025-08-15T05:54:34Z | 2026-02-13T03:17:32Z | transformers | 304 | 0 | null | image-text-to-text | {"parameters": {"BF16": 9174807784}, "total": 9174807784} | [
".gitattributes",
"LICENSE",
"NOTICE",
"README.md",
"added_tokens.json",
"chat_template.json",
"config.json",
"configuration_ovis2_5.py",
"generation_config.json",
"merges.txt",
"model-00001-of-00004.safetensors",
"model-00002-of-00004.safetensors",
"model-00003-of-00004.safetensors",
"mod... | d73b2283ae2a930b7762f8d7b8b8a3f0f3b5c3bd | [
"transformers",
"safetensors",
"ovis2_5",
"text-generation",
"MLLM",
"image-text-to-text",
"conversational",
"custom_code",
"en",
"zh",
"dataset:AIDC-AI/Ovis-dataset",
"arxiv:2508.11737",
"arxiv:2405.20797",
"license:apache-2.0",
"region:us"
] | null | {"architectures": ["Ovis2_5"], "auto_map": {"AutoConfig": "configuration_ovis2_5.Ovis2_5_Config", "AutoModelForCausalLM": "modeling_ovis2_5.Ovis2_5"}, "model_type": "ovis2_5", "processor_config": {"chat_template": "{%- for message in messages %}{{- '<|im_start|>' + message.role + '\n'}}{%- if message.role == 'system' o... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": "modeling_ovis2_5.Ovis2_5",
"pipeline_tag": "text-generation",
"processor": null
} | {"base_model": null, "datasets": ["AIDC-AI/Ovis-dataset"], "eval_results": null, "language": ["en", "zh"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "image-text-to-text", "tags": ["MLLM"]} | # Ovis2.5-9B
<div align="center">
<img src=https://cdn-uploads.huggingface.co/production/uploads/637aebed7ce76c3b834cea37/3IK823BZ8w-mz_QfeYkDn.png width="30%"/>
</div>
<p align="center">
<a href="https://arxiv.org/abs/2508.11737"><img src="https://img.shields.io/badge/📖_Technical_Report-Ovis2.5-b31b1b.svg" alt="... | null | [
"apache-2.0"
] | [
"AIDC-AI/Ovis-dataset"
] | [
"en",
"zh"
] | 9,174,807,784 | null | null | [
"AutoModelForCausalLM",
"Ovis2_5",
"modeling_ovis2_5.Ovis2_5",
"ovis2_5"
] | [
"image-text-to-text",
"text-generation"
] | [
"text",
"multimodal"
] | [
"text",
"image"
] | [
"text"
] |
667d3345722fe6b05ea686b5 | 34093184d10f76a9 | 35b001f3a491f9f9 | 151,646 | BPE | 151,387 | true | true | true | true | 3 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 53 | MaziyarPanahi/calme-2.8-qwen2-7b | MaziyarPanahi | {
"models": [
{
"_id": "665f181fb9e194a20c5e8d44",
"id": "Qwen/Qwen2-7B"
}
],
"relation": "finetune"
} | 82 | 148,858 | False | 2024-06-27T09:39:17Z | 2024-07-25T14:51:14Z | transformers | 6 | 0 | [{"name": "Qwen2-7B-Instruct-v0.8", "results": [{"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "IFEval (0-Shot)", "type": "HuggingFaceH4/ifeval", "args": {"num_few_shot": 0}}, "metrics": [{"type": "inst_level_strict_acc and prompt_level_strict_acc", "value": 27.75, "name": "strict ... | text-generation | {"parameters": {"BF16": 7615616512}, "total": 7615616512} | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"generation_config.json",
"merges.txt",
"model-00001-of-00004.safetensors",
"model-00002-of-00004.safetensors",
"model-00003-of-00004.safetensors",
"model-00004-of-00004.safetensors",
"model.safetensors.index.json",
"qwen2-fine... | 2c81c1ce77195960e6ac6e7a8f8759a698dd0599 | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"chat",
"qwen",
"finetune",
"chatml",
"OpenHermes-2.5",
"HelpSteer2",
"Orca",
"SlimOrca",
"conversational",
"en",
"dataset:nvidia/HelpSteer2",
"dataset:teknium/OpenHermes-2.5",
"dataset:microsoft/orca-math-word-problems-200k"... | null | {"architectures": ["Qwen2ForCausalLM"], "model_type": "qwen2", "tokenizer_config": {"bos_token": null, "chat_template": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "Qwen/Qwen2-7B", "datasets": ["nvidia/HelpSteer2", "teknium/OpenHermes-2.5", "microsoft/orca-math-word-problems-200k", "Open-Orca/SlimOrca"], "language": ["en"], "library_name": "transformers", "license": "apache-2.0", "pipeline_tag": "text-generation", "tags": ["chat", "qwen", "qwen2", "finetune", "chat... | <img src="./qwen2-fine-tunes-maziyar-panahi.webp" alt="Qwen2 fine-tune" width="500" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
# MaziyarPanahi/calme-2.8-qwen2-7b
This is a fine-tuned version of the `Qwen/Qwen2-7B` model. It aims to improve the base model across all benchmarks.
# ⚡ Quantized GGU... | null | [
"apache-2.0"
] | [
"nvidia/HelpSteer2",
"teknium/OpenHermes-2.5",
"microsoft/orca-math-word-problems-200k",
"Open-Orca/SlimOrca"
] | [
"en"
] | 7,615,616,512 | null | null | [
"AutoModelForCausalLM",
"Qwen2ForCausalLM",
"qwen2"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
621ffdc136468d709f1812a6 | d82c716f01e57f52 | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 62 | uw-madison/yoso-4096 | uw-madison | null | 1,436 | 154,290 | False | 2022-03-02T23:29:05Z | 2022-01-12T13:36:04Z | transformers | 0 | 0 | null | fill-mask | null | [
".gitattributes",
"README.md",
"config.json",
"merges.txt",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | 51a64263ef39d9e633f821a13ec9fafe5711061f | [
"transformers",
"pytorch",
"yoso",
"fill-mask",
"arxiv:2111.09714",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["YosoForMaskedLM"], "model_type": "yoso", "tokenizer_config": {"unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>", "sep_token": "</s>", "cls_token": "<s>", "pad_token": "<pad>", "mask_token": "<mask>"}} | {
"auto_model": "AutoModelForMaskedLM",
"custom_class": null,
"pipeline_tag": "fill-mask",
"processor": "AutoTokenizer"
} | null | # YOSO
YOSO model for masked language modeling (MLM) for sequence length 4096.
## About YOSO
The YOSO model was proposed in [You Only Sample (Almost) Once: Linear Cost Self-Attention Via Bernoulli Sampling](https://arxiv.org/abs/2111.09714) by Zhanpeng Zeng, Yunyang Xiong, Sathya N. Ravi, Shailesh Acharya, Glenn Fun... | null | null | null | null | null | null | null | [
"AutoModelForMaskedLM",
"YosoForMaskedLM",
"yoso"
] | [
"fill-mask"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
6431fa5c7d795d8e7033fde8 | 8a8c94e1a59f5186 | ef4806e2ade9022f | 32,100 | Unigram | null | true | true | true | true | 103 | Precompiled | Sequence | Metaspace | [
"Precompiled"
] | [
"WhitespaceSplit",
"Metaspace"
] | [
"Metaspace"
] | 11 | pszemraj/long-t5-tglobal-base-sci-simplify-elife | pszemraj | {
"models": [
{
"_id": "625aa30ce58e3083f52e0c64",
"id": "google/long-t5-tglobal-base"
}
],
"relation": "quantized"
} | 23 | 148,198 | False | 2023-04-08T23:35:56Z | 2025-12-29T03:43:03Z | transformers | 5 | 0 | null | summarization | {"parameters": {"F32": 247587456}, "total": 247587456} | [
".gitattributes",
"README.md",
"config.json",
"evals-outputs/GAUNTLET.md",
"generation_config.json",
"model.onnx",
"model.safetensors",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | b47c4e592f6662e8276699d631b9d5a80814cd05 | [
"transformers",
"pytorch",
"onnx",
"safetensors",
"longt5",
"text2text-generation",
"lay summaries",
"paper summaries",
"biology",
"medical",
"summarization",
"en",
"dataset:pszemraj/scientific_lay_summarisation-elife-norm",
"base_model:google/long-t5-tglobal-base",
"base_model:quantized... | null | {"architectures": ["LongT5ForConditionalGeneration"], "model_type": "longt5", "tokenizer_config": {"eos_token": "</s>", "pad_token": "<pad>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModelForSeq2SeqLM",
"custom_class": null,
"pipeline_tag": "text2text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "google/long-t5-tglobal-base", "datasets": ["pszemraj/scientific_lay_summarisation-elife-norm"], "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "summarizati... | # long-t5-tglobal-base-sci-simplify: elife subset
<a href="https://colab.research.google.com/gist/pszemraj/37a406059887a400afc1428d70374327/long-t5-tglobal-base-sci-simplify-elife-example-with-textsum.ipynb">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>
Exploring ho... | null | [
"apache-2.0"
] | [
"pszemraj/scientific_lay_summarisation-elife-norm"
] | [
"en"
] | 247,587,456 | null | null | [
"AutoModelForSeq2SeqLM",
"longt5",
"LongT5ForConditionalGeneration"
] | [
"text2text-generation",
"summarization"
] | [
"text"
] | [
"text"
] | [
"text"
] |
68724241d6bfaa161639eb7e | 8854901668a735b1 | 079cb2304319f387 | 73,448 | BPE | 104,297 | true | false | true | true | 92 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 818 | openbmb/MiniCPM-V-4 | openbmb | null | 102,661 | 504,488 | False | 2025-07-12T11:08:49Z | 2025-09-15T03:27:10Z | transformers | 462 | 0 | null | image-text-to-text | null | [
".gitattributes",
"README.md",
"config.json",
"configuration_minicpm.py",
"generation_config.json",
"image_processing_minicpmv.py",
"model-00001-of-00002.safetensors",
"model-00002-of-00002.safetensors",
"model.safetensors.index.json",
"modeling_minicpmv.py",
"modeling_navit_siglip.py",
"prepr... | 0968cf95dd0ed5584b5a50d0ded4ce29421674f7 | [
"transformers",
"safetensors",
"minicpmv",
"feature-extraction",
"minicpm-v",
"vision",
"ocr",
"multi-image",
"video",
"custom_code",
"image-text-to-text",
"conversational",
"multilingual",
"dataset:openbmb/RLAIF-V-Dataset",
"license:apache-2.0",
"region:us"
] | null | {"architectures": ["MiniCPMV"], "auto_map": {"AutoConfig": "configuration_minicpm.MiniCPMVConfig", "AutoModel": "modeling_minicpmv.MiniCPMV", "AutoModelForCausalLM": "modeling_minicpmv.MiniCPMV"}, "model_type": "minicpmv", "tokenizer_config": {"bos_token": "<s>", "chat_template": "{% for message in messages %}{{'<|im_s... | {
"auto_model": "AutoModel",
"custom_class": "modeling_minicpmv.MiniCPMV",
"pipeline_tag": "feature-extraction",
"processor": null
} | {"base_model": null, "datasets": ["openbmb/RLAIF-V-Dataset"], "eval_results": null, "language": ["multilingual"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "image-text-to-text", "tags": ["minicpm-v", "vision"... | <h1>A GPT-4V Level MLLM for Single Image, Multi Image and Video on Your Phone</h1>
[GitHub](https://github.com/OpenBMB/MiniCPM-o) | [Demo](http://211.93.21.133:8889/)</a>
## MiniCPM-V 4.0
**MiniCPM-V 4.0** is the latest efficient model in the MiniCPM-V series. The model is built based on SigLIP2-400M and MiniCPM4... | null | [
"apache-2.0"
] | [
"openbmb/RLAIF-V-Dataset"
] | [
"multilingual"
] | null | null | null | [
"AutoModel",
"MiniCPMV",
"minicpmv",
"modeling_minicpmv.MiniCPMV"
] | [
"image-text-to-text",
"feature-extraction"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"text",
"embeddings"
] |
6575e5b614bfb7d56ff9ec78 | 88cd37e502d27a0a | 0e7bc0977f200d1c | 32,000 | BPE | 58,980 | true | false | true | true | 3 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 88 | Deci/DeciLM-7B | Deci | null | 1,663 | 160,198 | False | 2023-12-10T16:22:14Z | 2024-02-21T16:04:09Z | transformers | 226 | 0 | null | text-generation | {"parameters": {"BF16": 7043551232}, "total": 7043551232} | [
".gitattributes",
"README.md",
"benchmark_hf_model.py",
"config.json",
"configuration_decilm.py",
"model-00001-of-00003.safetensors",
"model-00002-of-00003.safetensors",
"model-00003-of-00003.safetensors",
"model.safetensors.index.json",
"modeling_decilm.py",
"special_tokens_map.json",
"tokeni... | c3c9f4226801dc0433f32aebffe0aac68ee2f051 | [
"transformers",
"safetensors",
"deci",
"text-generation",
"conversational",
"custom_code",
"en",
"license:apache-2.0",
"region:us"
] | null | {"architectures": ["DeciLMForCausalLM"], "auto_map": {"AutoConfig": "configuration_decilm.DeciLMConfig", "AutoModelForCausalLM": "modeling_decilm.DeciLMForCausalLM"}, "model_type": "deci", "tokenizer_config": {"bos_token": "<s>", "eos_token": "</s>", "pad_token": null, "unk_token": "<unk>", "use_default_system_prompt":... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": "modeling_decilm.DeciLMForCausalLM",
"pipeline_tag": "text-generation",
"processor": null
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en"], "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | # DeciLM-7B
DeciLM-7B is a 7.04 billion parameter decoder-only text generation model, released under the Apache 2.0 license. At the time of release, DeciLM-7B is the top-performing 7B base language model on the Open LLM Leaderboard. With support for an 8K-token sequence length, this highly efficient model uses variabl... | null | [
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} | {"base_model": null, "datasets": ["LDJnr/Capybara", "LDJnr/LessWrong-Amplify-Instruct", "LDJnr/Pure-Dove", "LDJnr/Verified-Camel"], "eval_results": null, "language": ["eng"], "library_name": null, "license": ["mit"], "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, ... | ## **Nous-Capybara-7B V1**
**MUCH BETTER MISTRAL BASED VERSION IS OUT NOW AS CAPYBARA V1.9**
The Capybara series is made by fine-tuning on data that is created by Nous with our novel data synthesis technique called Amplify-instruct, the seed distribution and synthesis method are comprised of a synergistic combination... | null | [
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"Replace"
] | [
"Split"
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"Replace",
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"Fuse"
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}
],
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} | 2,873 | 174,509 | False | 2025-03-13T13:26:48Z | 2025-04-11T02:43:03Z | transformers | 4 | 0 | null | image-text-to-text | {"parameters": {"F32": 231968, "BF16": 1453195632, "U8": 2935922890}, "total": 4389350490} | [
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"ar... | null | {"architectures": ["Gemma3ForConditionalGeneration"], "model_type": "gemma3", "quantization_config": {"load_in_4bit": true, "load_in_8bit": false, "quant_method": "bitsandbytes"}, "tokenizer_config": {"bos_token": "<bos>", "eos_token": "<eos>", "pad_token": "<pad>", "unk_token": "<unk>", "use_default_system_prompt": fa... | {
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<p style="margin-bottom: 0; margin-top: 0;">
<strong>See <a href="https://huggingface.co/collections/unsloth/gemma-3-67d12b7e8816ec6efa7e4e5b">our collection</a> for all versions of Gemma 3 including GGUF, 4-bit & 16-bit formats.</strong>
</p>
<p style="margin-bottom: 0;">
<em>Unsloth's <a href="htt... | null | [
"gemma"
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"en"
] | 4,389,350,490 | null | null | [
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6419eedf3a524ff07ef8873b | 2d0341bd3a73bae6 | ef4806e2ade9022f | 32,100 | Unigram | null | true | true | true | true | 103 | Sequence | Metaspace | Metaspace | [
"Precompiled",
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"Metaspace"
] | [
"Metaspace"
] | 32 | declare-lab/flan-alpaca-large | declare-lab | null | 224 | 149,360 | False | 2023-03-21T17:52:31Z | 2023-08-21T06:49:29Z | transformers | 48 | 0 | null | null | {"parameters": {"F32": 783150080}, "total": 783150080} | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
"model.safetensors",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
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"safetensors",
"t5",
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"dataset:tatsu-lab/alpaca",
"arxiv:2308.09662",
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"text-generation-inference",
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"region:us"
] | null | {"architectures": ["T5ForConditionalGeneration"], "model_type": "t5", "tokenizer_config": {"eos_token": "</s>", "pad_token": "<pad>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModelForSeq2SeqLM",
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} | {"base_model": null, "datasets": ["tatsu-lab/alpaca"], "eval_results": null, "language": null, "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | ## 🍮 🦙 Flan-Alpaca: Instruction Tuning from Humans and Machines
📣 Introducing **Red-Eval** to evaluate the safety of the LLMs using several jailbreaking prompts. With **Red-Eval** one could jailbreak/red-team GPT-4 with a 65.1% attack success rate and ChatGPT could be jailbroken 73% of the time as measured on Dange... | null | [
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{
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{
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"id": "Cas-Warehouse/Lla... | 49 | 147,641 | False | 2024-07-10T08:17:33Z | 2024-07-12T02:07:45Z | transformers | 32 | 0 | null | text-generation | {"parameters": {"BF16": 8030261248}, "total": 8030261248} | [
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Image by ろ47
# Merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
The goal of this m... | null | null | null | null | 8,030,261,248 | null | null | [
"AutoModelForCausalLM",
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"LlamaForCausalLM"
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] | [
"Split",
"ByteLevel"
] | [
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}
],
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} | 63,404 | 285,436 | False | 2025-04-18T12:52:15Z | 2025-04-23T14:12:56Z | transformers | 9 | 0 | null | image-text-to-text | {"parameters": {"BF16": 7944373760}, "total": 7944373760} | [
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"base_model:fi... | null | {"architectures": ["InternVLForConditionalGeneration"], "model_type": "internvl", "tokenizer_config": {"bos_token": null, "eos_token": "<|im_end|>", "pad_token": "<|endoftext|>", "unk_token": null}, "chat_template_jinja": "{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n'}}{% if message['content'] ... | {
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"pipeline_tag": "image-text-to-text",
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} | {"base_model": ["OpenGVLab/InternVL3-8B-Instruct"], "datasets": ["OpenGVLab/MMPR-v1.2"], "eval_results": null, "language": ["multilingual"], "library_name": "transformers", "license": "other", "license_name": "qwen", "license_link": "https://huggingface.co/Qwen/Qwen2.5-72B-Instruct/blob/main/LICENSE", "metrics": null, ... | # InternVL3-8B Transformers 🤗 Implementation
[\[📜 InternVL 1.0\]](https://huggingface.co/papers/2312.14238) [\[📜 InternVL 1.5\]](https://huggingface.co/papers/2404.16821) [\[📜 InternVL 2.5\]](https://huggingface.co/papers/2412.05271) [\[📜 InternVL2.5-MPO\]](https://huggingface.co/papers/2411.10442) [\[📜 Inte... | null | [
"other",
"qwen",
"https://huggingface.co/Qwen/Qwen2.5-72B-Instruct/blob/main/LICENSE"
] | [
"OpenGVLab/MMPR-v1.2"
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"multilingual"
] | 7,944,373,760 | null | null | [
"internvl",
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] | [
"text"
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649dab1df922fe4fa917f6c9 | 5b8a6156b5b6e013 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
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"BertPreTokenizer"
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"WordPiece"
] | 37 | wu981526092/Sentence-Level-Stereotype-Detector | wu981526092 | null | 2,076 | 153,948 | False | 2023-06-29T16:02:37Z | 2023-09-18T01:49:58Z | transformers | 6 | 0 | null | text-classification | null | [
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] | null | {"architectures": ["DistilBertForSequenceClassification"], "model_type": "distilbert", "tokenizer_config": {"cls_token": "[CLS]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
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} | {"base_model": null, "datasets": ["stereoset", "crows_pairs", "wu981526092/MGSD"], "eval_results": null, "language": ["en"], "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": ["f1", "recall", "precision", "accuracy"], "model_name": null, "pipeline_tag": null, "tags": null} | # Sentence-Level Stereotype Classifier
The Sentence-Level Stereotype Classifier is a transformer-based model developed to detect and classify different types of stereotypes present in the text at the sentence level. It is designed to recognize stereotypical and anti-stereotypical stereotypes towards gender, race, prof... | null | [
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] | [
"stereoset",
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] | [
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] | null | null | [
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"Prepend",
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] | null | [
"Replace",
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] | 309 | llSourcell/medllama2_7b | llSourcell | null | 41 | 147,365 | False | 2023-08-09T01:48:57Z | 2023-08-21T17:05:22Z | transformers | 132 | 0 | null | text-generation | null | [
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"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] | |
62bb5a130a925c547d40af09 | 931798862de10283 | ef4806e2ade9022f | 32,100 | Unigram | null | true | true | true | true | 103 | Precompiled | Sequence | Metaspace | [
"Precompiled"
] | [
"WhitespaceSplit",
"Metaspace"
] | [
"Metaspace"
] | 149 | czearing/article-title-generator | czearing | null | 338 | 149,634 | False | 2022-06-28T19:44:19Z | 2022-06-28T20:08:16Z | transformers | 23 | 0 | null | null | null | [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | 511d2f366414670c9d01631dd6484c2f810f5c3e | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"license:mit",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["T5ForConditionalGeneration"], "model_type": "t5", "tokenizer_config": {"eos_token": "</s>", "pad_token": "<pad>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModelForSeq2SeqLM",
"custom_class": null,
"pipeline_tag": "text2text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | ## Article Title Generator
The model is based on the T5 language model and trained using a large collection of Medium articles.
## Usage
Example code:
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("czearing/article-title-generator")
model = AutoModel.from_pretra... | null | [
"mit"
] | null | null | null | null | null | [
"t5",
"T5ForConditionalGeneration",
"AutoModelForSeq2SeqLM"
] | [
"text2text-generation"
] | null | null | null |
6246b9d0786badd3dbaaf1d2 | ada369218ea794db | 19583e88b0ebb24b | 32,001 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 819 | NYTK/named-entity-recognition-nerkor-hubert-hungarian | NYTK | null | 4,491 | 175,999 | False | 2022-04-01T08:37:36Z | 2023-01-12T10:21:56Z | transformers | 4 | 0 | null | token-classification | null | [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | 620a8333dc5ca2ef885810bae52d79d412f3e519 | [
"transformers",
"pytorch",
"bert",
"token-classification",
"hu",
"license:apache-2.0",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["BertForTokenClassification"], "model_type": "bert", "tokenizer_config": {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}} | {
"auto_model": "AutoModelForTokenClassification",
"custom_class": null,
"pipeline_tag": "token-classification",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["hu"], "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": ["f1"], "model_name": null, "pipeline_tag": null, "tags": ["token-classification"], "widget": [{"text": "A Kov\u00e1csn\u00e9 Nagy Erzs\u... | # Hungarian Named Entity Recognition Model with huBERT
For further models, scripts and details, see [our demo site](https://juniper.nytud.hu/demo/nlp).
- Pretrained model used: SZTAKI-HLT/hubert-base-cc
- Finetuned on [NYTK-NerKor](https://github.com/nytud/NYTK-NerKor)
- NE categories are: PER, LOC, MISC, ORG
... | null | [
"apache-2.0"
] | null | [
"hu"
] | null | null | [
"f1"
] | [
"AutoModelForTokenClassification",
"BertForTokenClassification",
"bert"
] | [
"token-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
6453b4dfdc897edfd15c0d59 | 58967ba68263f0aa | 2f8d5857ae5fa330 | 50,277 | BPE | 50,009 | true | true | true | true | 25 | NFC | ByteLevel | ByteLevel | [
"NFC"
] | [
"ByteLevel"
] | [
"ByteLevel"
] | 86 | RWKV/rwkv-4-169m-pile | RWKV | null | 4,190 | 161,238 | False | 2023-05-04T13:36:31Z | 2023-05-15T09:59:20Z | transformers | 8 | 0 | null | text-generation | null | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | 46bdc280eb97b6141d5d51a935e0c4870ecaefcc | [
"transformers",
"pytorch",
"rwkv",
"text-generation",
"dataset:EleutherAI/pile",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["RwkvForCausalLM"], "model_type": "rwkv", "tokenizer_config": {"bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "unk_token": "<|endoftext|>"}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["EleutherAI/pile"], "eval_results": null, "language": null, "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | 
# Model card for RWKV-4 | 169M parameters trained on Pile dataset
RWKV is a project led by [Bo Peng](https://github.com/BlinkDL). Learn more about the model architecture in the blogposts from Johan Wi... | null | null | [
"EleutherAI/pile"
] | null | null | null | null | [
"AutoModelForCausalLM",
"rwkv",
"RwkvForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
621ffdc136468d709f18149f | 911e922daa12795e | b6b7d241f1acf16d | 40,030 | Unigram | null | true | true | true | true | 1 | Sequence | Metaspace | Metaspace | [
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 296 | vinai/bartpho-syllable | vinai | null | 132,148 | 427,986 | False | 2022-03-02T23:29:05Z | 2024-08-20T03:57:12Z | transformers | 7 | 0 | null | feature-extraction | null | [
".gitattributes",
"README.md",
"__init__.py",
"config.json",
"dict.txt",
"pytorch_model.bin",
"sentencepiece.bpe.model",
"tf_model.h5",
"tokenization_bartpho.py",
"tokenization_bartpho_fast.py",
"tokenizer.json"
] | 36eee8b4d648dd99da56462edcda3c5c97f7f3de | [
"transformers",
"pytorch",
"tf",
"mbart",
"feature-extraction",
"arxiv:2109.09701",
"license:mit",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["MBartModel"], "model_type": "mbart"} | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | # <a name="introduction"></a> BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamese
Two BARTpho versions `BARTpho-syllable` and `BARTpho-word` are the first public large-scale monolingual sequence-to-sequence models pre-trained for Vietnamese. BARTpho uses the "large" architecture and pre-training scheme of... | null | [
"mit"
] | null | null | null | null | null | [
"AutoModel",
"mbart",
"MBartModel"
] | [
"feature-extraction"
] | [
"multimodal"
] | [
"text"
] | [
"embeddings"
] |
635ada37bfda845ec78e8fbb | 7469dded5408219b | d3a5589134b292ff | 250,100 | Unigram | null | true | true | true | true | 3 | Sequence | Metaspace | Metaspace | [
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 535 | bigscience/mt0-base | bigscience | null | 1,263 | 158,993 | False | 2022-10-27T19:21:27Z | 2023-09-26T09:17:05Z | transformers | 32 | 0 | [{"name": "mt0-base", "results": [{"task": {"type": "Coreference resolution"}, "dataset": {"type": "winogrande", "name": "Winogrande XL (xl)", "config": "xl", "split": "validation", "revision": "a80f460359d1e9a67c006011c94de42a8759430c"}, "metrics": [{"type": "Accuracy", "value": 53.28, "verified": false}]}, {"task": {... | text-generation | {"parameters": {"F32": 582401280}, "total": 582401280} | [
".gitattributes",
"README.md",
"config.json",
"model.safetensors",
"onnx/config.json",
"onnx/decoder_model.onnx",
"onnx/decoder_model_merged.onnx",
"onnx/decoder_with_past_model.onnx",
"onnx/encoder_model.onnx",
"onnx/generation_config.json",
"onnx/special_tokens_map.json",
"onnx/spiece.model"... | 0c500042c1f80b6b6ec208cf73d9ee43f1dac245 | [
"transformers",
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"onnx",
"safetensors",
"mt5",
"text2text-generation",
"af",
"am",
"ar",
"az",
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"ca",
"ceb",
"co",
"cs",
"cy",
"da",
"de",
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"eo",
"es",
"et",
"eu",
"fa",
"fi",
"fil",
"fr",
"fy",
"ga",
"gd",
"gl",... | null | {"architectures": ["MT5ForConditionalGeneration"], "model_type": "mt5", "tokenizer_config": {"eos_token": "</s>", "pad_token": "<pad>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModelForSeq2SeqLM",
"custom_class": null,
"pipeline_tag": "text2text-generation",
"processor": "AutoTokenizer"
} | {"datasets": ["bigscience/xP3", "mc4"], "language": ["af", "am", "ar", "az", "be", "bg", "bn", "ca", "ceb", "co", "cs", "cy", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fil", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "haw", "hi", "hmn", "ht", "hu", "hy", "ig", "is", "it", "iw", "ja", "jv", "ka", "kk",... | 
# Table of Contents
1. [Model Summary](#model-summary)
2. [Use](#use)
3. [Limitations](#limitations)
4. [Training](#training)
5. [Evaluation](#evaluation)
7. [Citation](#citation)
# Model Summary
> We present BLOOMZ & mT0, a ... | null | [
"apache-2.0"
] | [
"bigscience/xP3",
"mc4"
] | [
"af",
"am",
"ar",
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"be",
"bg",
"bn",
"ca",
"ceb",
"co",
"cs",
"cy",
"da",
"de",
"el",
"en",
"eo",
"es",
"et",
"eu",
"fa",
"fi",
"fil",
"fr",
"fy",
"ga",
"gd",
"gl",
"gu",
"ha",
"haw",
"hi",
"hmn",
"ht",
"hu",
"hy",
"ig",
"is",
"it",
... | 582,401,280 | null | null | [
"mt5",
"MT5ForConditionalGeneration",
"AutoModelForSeq2SeqLM"
] | [
"text2text-generation",
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
65afafe29d5b422218f839f9 | 478c3acd2402b078 | 82619ce8e1dcaab5 | 100,000 | BPE | 99,776 | false | true | true | true | 15 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 306 | alierenak/bert_turkish_sentiment | alierenak | {
"models": [
{
"_id": "63652199daa65e06f90ab342",
"id": "VRLLab/TurkishBERTweet"
}
],
"relation": "finetune"
} | 9 | 146,105 | False | 2024-01-23T12:24:02Z | 2024-01-23T12:39:39Z | transformers | 0 | 0 | [{"name": "turkish_sentiment3", "results": []}] | text-classification | {"parameters": {"F32": 162844419}, "total": 162844419} | [
".gitattributes",
"README.md",
"config.json",
"merges.txt",
"model.safetensors",
"runs/Jan23_12-24-02_0722eca8ca54/events.out.tfevents.1706012645.0722eca8ca54.169.1",
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"special_tokens_map.json",
"tokenizer.json",
... | 2d9cb4805d6aafc4120f53341235aa95a0144fbd | [
"transformers",
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"roberta",
"text-classification",
"generated_from_trainer",
"base_model:VRLLab/TurkishBERTweet",
"base_model:finetune:VRLLab/TurkishBERTweet",
"license:mit",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["RobertaForSequenceClassification"], "model_type": "roberta", "tokenizer_config": {"bos_token": "<s>", "cls_token": "<s>", "eos_token": "</s>", "mask_token": "<mask>", "pad_token": "<pad>", "sep_token": "</s>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"base_model": "VRLLab/TurkishBERTweet", "datasets": null, "eval_results": [], "language": null, "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": ["accuracy"], "model_name": "turkish_sentiment3", "pipeline_tag": null, "tags": ["generated_from_trainer"]} | <!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# turkish_sentiment3
This model is a fine-tuned version of [VRLLab/TurkishBERTweet](https://huggingface.co/VRLLab/TurkishBERTweet) ... | null | [
"mit"
] | null | null | 162,844,419 | null | [
"accuracy"
] | [
"roberta",
"AutoModelForSequenceClassification",
"RobertaForSequenceClassification"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
628c8538ef14f971b6980eb8 | d239c54d1f5946a3 | ef4806e2ade9022f | 32,100 | Unigram | null | true | true | true | true | 103 | Sequence | Metaspace | Metaspace | [
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 420 | oliverguhr/spelling-correction-german-base | oliverguhr | null | 250 | 454,483 | False | 2022-05-24T07:11:52Z | 2023-07-06T12:27:25Z | transformers | 15 | 0 | [{"name": "bart-base-spelling-de", "results": []}] | null | {"parameters": {"F32": 247534848}, "total": 247534848} | [
".gitattributes",
"README.md",
"all_results.json",
"config.json",
"eval_results.json",
"generation_config.json",
"model.safetensors",
"pytorch_model.bin",
"runs/Apr04_08-04-47_ipc13112/1680595976.1319165/events.out.tfevents.1680595976.ipc13112.351283.1",
"runs/Apr04_08-04-47_ipc13112/events.out.tf... | b3916256af432f30dbaeb184a1c6b5be78b6f62f | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["T5ForConditionalGeneration"], "model_type": "t5", "tokenizer_config": {"eos_token": "</s>", "pad_token": "<pad>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModelForSeq2SeqLM",
"custom_class": null,
"pipeline_tag": "text2text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": [], "language": null, "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": "bart-base-spelling-de", "pipeline_tag": null, "tags": ["generated_from_trainer"], "widget": [{"text": "correct: ein dran... | ## Model description
This is a proof of concept spelling correction model for german. The model should fix your typos and punctuation.
If you like to participate in the development or run your own experiments, have a look at [the GitHub repo](https://github.com/oliverguhr/spelling).
Model Input:
> ein dransformer i... | null | [
"apache-2.0"
] | null | null | 247,534,848 | null | null | [
"t5",
"T5ForConditionalGeneration",
"AutoModelForSeq2SeqLM"
] | [
"text2text-generation"
] | null | null | null |
68ac9193a377a9b3e010cd49 | 6c53decdf26fa934 | 21ca95dec54278ad | 151,678 | BPE | 151,387 | true | true | true | true | 35 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 820 | OpenGVLab/InternVL3_5-1B | OpenGVLab | {
"models": [
{
"_id": "68ac9193f8ae7902de501e35",
"id": "OpenGVLab/InternVL3_5-1B-MPO"
}
],
"relation": "finetune"
} | 60,210 | 390,688 | False | 2025-08-25T16:38:43Z | 2025-08-29T17:57:08Z | transformers | 26 | 0 | null | image-text-to-text | null | [
".gitattributes",
"README.md",
"added_tokens.json",
"chat_template.jinja",
"config.json",
"configuration_intern_vit.py",
"configuration_internvl_chat.py",
"conversation.py",
"generation_config.json",
"merges.txt",
"model.safetensors",
"modeling_intern_vit.py",
"modeling_internvl_chat.py",
... | 2f71cf52542334823e48a46ffba0e2bc9add3446 | [
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"custom_code",
"image-text-to-text",
"conversational",
"multilingual",
"dataset:OpenGVLab/MMPR-v1.2",
"dataset:OpenGVLab/MMPR-Tiny",
"arxiv:2312.14238",
"arxiv:2404.16821",
"arxiv:2412.05271",
"arxiv:2411.1... | null | {"architectures": ["InternVLChatModel"], "auto_map": {"AutoConfig": "configuration_internvl_chat.InternVLChatConfig", "AutoModel": "modeling_internvl_chat.InternVLChatModel", "AutoModelForCausalLM": "modeling_internvl_chat.InternVLChatModel"}, "model_type": "internvl_chat", "tokenizer_config": {"bos_token": null, "eos_... | {
"auto_model": "AutoModel",
"custom_class": "modeling_internvl_chat.InternVLChatModel",
"pipeline_tag": "feature-extraction",
"processor": null
} | {"base_model": ["OpenGVLab/InternVL3_5-1B-MPO"], "datasets": ["OpenGVLab/MMPR-v1.2", "OpenGVLab/MMPR-Tiny"], "eval_results": null, "language": ["multilingual"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "imag... | # InternVL3_5-1B
[\[📂 GitHub\]](https://github.com/OpenGVLab/InternVL) [\[📜 InternVL 1.0\]](https://huggingface.co/papers/2312.14238) [\[📜 InternVL 1.5\]](https://huggingface.co/papers/2404.16821) [\[📜 InternVL 2.5\]](https://huggingface.co/papers/2412.05271) [\[📜 InternVL2.5-MPO\]](https://huggingface.co/pap... | null | [
"apache-2.0"
] | [
"OpenGVLab/MMPR-v1.2",
"OpenGVLab/MMPR-Tiny"
] | [
"multilingual"
] | null | null | null | [
"AutoModel",
"internvl_chat",
"InternVLChatModel",
"modeling_internvl_chat.InternVLChatModel"
] | [
"image-text-to-text",
"feature-extraction"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"text",
"embeddings"
] |
66451bf53ba2d1ff158a7f36 | 3f287b04362fff24 | a1f1bd381dcb9329 | 128,256 | BPE | 280,147 | false | true | true | true | 256 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 158 | WhiteRabbitNeo/Llama-3-WhiteRabbitNeo-8B-v2.0 | WhiteRabbitNeo | null | 423 | 146,795 | False | 2024-05-15T20:32:53Z | 2024-05-15T20:59:37Z | transformers | 59 | 0 | null | text-generation | {"parameters": {"F16": 8030261248}, "total": 8030261248} | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
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"... | 43e6591fbd08688c78712e134679b6d1da9911ef | [
"transformers",
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"text-generation",
"conversational",
"license:llama3",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
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] | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": "<|begin_of_text|>", "chat_template": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|s... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": "llama3", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | # Our latest 33B model is live (We'll always be serving the newest model in our web app, and on Kindo.ai)!
Access at: https://www.whiterabbitneo.com/
# Our Discord Server
Join us at: https://discord.gg/8Ynkrcbk92 (Updated on Dec 29th. Now permanent link to join)
# Llama-3 Licence + WhiteRabbitNeo Extended Vers... | null | [
"llama3"
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"da... | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": "<|begin_of_text|>", "chat_template": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content... | {
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Qra is a series of LLMs adapted to the Polish language, resulting from a collaboration between the National Information Processing Institute (OPI) and Gdańsk University of Technology (PG). The models were trained o... | null | [
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### Usage
Here's an example of performing inference using the model with [FastEmbed](https://github.com/qdrant/fastembed).
```py
from fastembed import TextEmbedding
documents = [
... | null | [
"apache-2.0"
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"BertModel",
"AutoModel",
"bert"
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"logits",
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"ByteLevel"
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] | 123 | facebook/blenderbot-1B-distill | facebook | null | 55 | 146,638 | False | 2022-03-02T23:29:05Z | 2023-03-30T16:12:16Z | transformers | 37 | 0 | null | null | null | [
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] | 9f646f8349f10ed5763d11db190210520c2594cc | [
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"dataset:blended_skill_talk",
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} | {"base_model": null, "datasets": ["blended_skill_talk"], "eval_results": null, "language": ["en"], "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": ["perplexity"], "model_name": null, "pipeline_tag": null, "tags": ["convAI", "conversational", "facebook"], "thumbnail"... | ## Model description
+ Paper: [Recipes for building an open-domain chatbot](https://arxiv.org/abs/1907.06616)
+ [Original PARLAI Code](https://parl.ai/projects/recipes/)
### Abstract
Building open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural mo... | null | [
"apache-2.0"
] | [
"blended_skill_talk"
] | [
"en"
] | null | null | [
"perplexity"
] | [
"AutoModelForSeq2SeqLM",
"BlenderbotForConditionalGeneration",
"blenderbot"
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"text2text-generation"
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{
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}
],
"relation": "finetune"
} | 510 | 148,676 | False | 2025-04-07T10:39:13Z | 2025-05-11T22:54:03Z | transformers | 680 | 0 | null | text-generation | null | [
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} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en"], "library_name": null, "license": "other", "license_name": "tongyi-qianwen", "license_link": "https://huggingface.co/Qwen/Qwen1.5-72B-Chat-GPTQ-Int4/blob/main/LICENSE", "metrics": null, "model_name": null, "pipeline_tag": "text-generation",... | # Qwen1.5-72B-Chat-GPTQ-Int4
## Introduction
Qwen1.5 is the beta version of Qwen2, a transformer-based decoder-only language model pretrained on a large amount of data. In comparison with the previous released Qwen, the improvements include:
* 6 model sizes, including 0.5B, 1.8B, 4B, 7B, 14B, and 72B;
* Significan... | null | [
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} | {"base_model": null, "datasets": ["allenai/MADLAD-400"], "eval_results": null, "language": ["multilingual", "en", "ru", "es", "fr", "de", "it", "pt", "pl", "nl", "vi", "tr", "sv", "id", "ro", "cs", "zh", "hu", "ja", "th", "fi", "fa", "uk", "da", "el", "no", "bg", "sk", "ko", "ar", "lt", "ca", "sl", "he", "et", "lv", "h... | # Model Card for MADLAD-400-10B-MT
# Table of Contents
0. [TL;DR](#TL;DR)
1. [Model Details](#model-details)
2. [Usage](#usage)
3. [Uses](#uses)
4. [Bias, Risks, and Limitations](#bias-risks-and-limitations)
5. [Training Details](#training-details)
6. [Evaluation](#evaluation)
7. [Environmental Impact](#environmenta... | null | [
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"en"
] | 3,754,622,976 | 3,085,938,688 | null | [
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"qwen2vl",
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"multimodal"
] | [
"text",
"image"
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"Replace"
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"Replace",
"ByteFallback",
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"apache-2.0"
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"text"
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67b87c3d15b8db13cc755c34 | 7c2a837aac87574b | 1e78464774d502ca | 128,256 | BPE | 280,147 | false | true | true | true | 256 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 762 | EuroBERT/EuroBERT-210m | EuroBERT | null | 8,104 | 187,138 | False | 2025-02-21T13:14:37Z | 2025-10-18T16:22:42Z | transformers | 83 | 0 | null | fill-mask | {"parameters": {"F32": 310266624}, "total": 310266624} | [
".gitattributes",
"README.md",
"config.json",
"configuration_eurobert.py",
"img/banner.png",
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"img/long_context.png",
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"model.safetensors",
"modeling_eurobert.py",
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"tokenizer_config... | 39b51e15dd1f1a06f58b5cbf6a8a188cec60bd0e | [
"transformers",
"pytorch",
"safetensors",
"eurobert",
"fill-mask",
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"custom_code",
"en",
"fr",
"de",
"es",
"zh",
"it",
"ru",
"pl",
"pt",
"ja",
"vi",
"nl",
"ar",
"tr",
"hi",
"arxiv:2503.05500",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["EuroBertForMaskedLM"], "auto_map": {"AutoConfig": "configuration_eurobert.EuroBertConfig", "AutoModel": "modeling_eurobert.EuroBertModel", "AutoModelForPreTraining": "modeling_eurobert.EuroBertPreTrainedModel", "AutoModelForMaskedLM": "modeling_eurobert.EuroBertForMaskedLM", "AutoModelForSequenceCla... | {
"auto_model": "AutoModelForMaskedLM",
"custom_class": null,
"pipeline_tag": "fill-mask",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en", "fr", "de", "es", "zh", "it", "ru", "pl", "pt", "ja", "vi", "nl", "ar", "tr", "hi"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "... | # EuroBERT-210m
<div>
<img src="img/banner.png" width="100%" alt="EuroBERT" />
</div>
## Table of Contents
1. [Overview](#overview)
2. [Usage](#Usage)
3. [Evaluation](#Evaluation)
4. [License](#license)
5. [Citation](#citation)
## Overview
EuroBERT is a family of multilingual encoder models designed for a variety... | null | [
"apache-2.0"
] | null | [
"en",
"fr",
"de",
"es",
"zh",
"it",
"ru",
"pl",
"pt",
"ja",
"vi",
"nl",
"ar",
"tr",
"hi"
] | 310,266,624 | null | null | [
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"AutoModelForMaskedLM",
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"text"
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"Lowercase",
"Precompiled"
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] | [
"Metaspace"
] | 822 | Alireza1044/albert-base-v2-mnli | Alireza1044 | null | 58 | 144,869 | False | 2022-03-02T23:29:04Z | 2021-07-27T21:10:33Z | transformers | 0 | 0 | null | text-classification | null | [
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"spiece.model",
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] | ce8224e1445a916d9d9b9f721bed8dad382f35f0 | [
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"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["AlbertForSequenceClassification"], "model_type": "albert", "tokenizer_config": {"bos_token": "[CLS]", "eos_token": "[SEP]", "unk_token": "<unk>", "sep_token": "[SEP]", "pad_token": "<pad>", "cls_token": "[CLS]", "mask_token": {"content": "[MASK]", "single_word": false, "lstrip": true, "rstrip": fals... | {
"auto_model": "AutoModelForSequenceClassification",
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"pipeline_tag": "text-classification",
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} | {"base_model": null, "datasets": ["glue"], "eval_results": null, "language": ["en"], "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": ["accuracy"], "model_name": null, "pipeline_tag": null, "tags": ["generated_from_trainer"], "model_index": [{"name": "mnli", "results... | <!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mnli
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the GLUE MNLI dataset.
It a... | null | [
"apache-2.0"
] | [
"glue"
] | [
"en"
] | null | null | [
"accuracy"
] | [
"AlbertForSequenceClassification",
"albert",
"AutoModelForSequenceClassification"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
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] |
621ffdc136468d709f17b3f8 | 099fc7b2924a795c | 5f9f736c7dc6fbfa | 28,996 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 453 | gchhablani/bert-base-cased-finetuned-sst2 | gchhablani | null | 45 | 144,965 | False | 2022-03-02T23:29:05Z | 2021-09-20T09:09:06Z | transformers | 0 | 0 | [{"name": "bert-base-cased-finetuned-sst2", "results": [{"task": {"name": "Text Classification", "type": "text-classification"}, "dataset": {"name": "GLUE SST2", "type": "glue", "args": "sst2"}, "metrics": [{"name": "Accuracy", "type": "accuracy", "value": 0.9231651376146789, "verified": false}]}]}] | text-classification | null | [
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] | null | {"architectures": ["BertForSequenceClassification"], "model_type": "bert", "tokenizer_config": {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}} | {
"auto_model": "AutoModelForSequenceClassification",
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"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"datasets": ["glue"], "language": ["en"], "license": "apache-2.0", "metrics": ["accuracy"], "tags": ["generated_from_trainer", "fnet-bert-base-comparison"], "model-index": [{"name": "bert-base-cased-finetuned-sst2", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name"... | <!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-cased-finetuned-sst2
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on... | null | [
"apache-2.0"
] | [
"glue"
] | [
"en"
] | null | null | [
"accuracy"
] | [
"BertForSequenceClassification",
"bert",
"AutoModelForSequenceClassification"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
6555c5932da2e4f12e84d81a | d6e6379268d2e729 | 0000e6c6a03fba1a | 32,003 | BPE | 61,249 | true | false | true | true | 6 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 513 | TinyLlama/TinyLlama-1.1B-Chat-v0.4 | TinyLlama | null | 1,248 | 163,921 | False | 2023-11-16T07:32:35Z | 2023-11-18T12:01:55Z | transformers | 44 | 0 | null | text-generation | {"parameters": {"F32": 1100060672}, "total": 1100060672} | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"generation_config.json",
"model.safetensors",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer.model",
"tokenizer_config.json"
] | 5378798738b1ec3ebf6f7c10c938540ff6dbb436 | [
"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"en",
"dataset:cerebras/SlimPajama-627B",
"dataset:bigcode/starcoderdata",
"dataset:OpenAssistant/oasst_top1_2023-08-25",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"region... | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": {"__type": "AddedToken", "content": "<s>", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false}, "eos_token": {"__type": "AddedToken", "content": "</s>", "lstrip": false, "normalized": false, "rstrip... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["cerebras/SlimPajama-627B", "bigcode/starcoderdata", "OpenAssistant/oasst_top1_2023-08-25"], "eval_results": null, "language": ["en"], "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "... | <div align="center">
# TinyLlama-1.1B
</div>
https://github.com/jzhang38/TinyLlama
The TinyLlama project aims to **pretrain** a **1.1B Llama model on 3 trillion tokens**. With some proper optimization, we can achieve this within a span of "just" 90 days using 16 A100-40G GPUs 🚀🚀. The training has started on 2023-0... | null | [
"apache-2.0"
] | [
"cerebras/SlimPajama-627B",
"bigcode/starcoderdata",
"OpenAssistant/oasst_top1_2023-08-25"
] | [
"en"
] | 1,100,060,672 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
621ffdc136468d709f180bb2 | bb370b43b509b91d | 1499027e90969722 | 50,124 | BPE | 69,066 | true | true | false | true | 229 | Sequence | Metaspace | Metaspace | [
"NFKC",
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] | [
"Metaspace"
] | [
"Metaspace"
] | 823 | taeminlee/kogpt2 | taeminlee | null | 37 | 144,104 | False | 2022-03-02T23:29:05Z | 2023-06-29T05:17:27Z | transformers | 2 | 0 | null | text-generation | {"parameters": {"F32": 124242432, "U8": 12582912}, "total": 136825344} | [
".gitattributes",
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"flax_model.msgpack",
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"pytorch_model.bin",
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"spiece.model",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | 7a2ab5d7eb07af8728e3c4806b4a87314656fc88 | [
"transformers",
"pytorch",
"jax",
"safetensors",
"gpt2",
"text-generation",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["GPT2LMHeadModel"], "model_type": "gpt2", "tokenizer_config": {"unk_token": "<unk>", "pad_token": "<pad>", "bos_token": "<s>", "eos_token": "</s>"}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | null | # KoGPT2-Transformers
KoGPT2 on Huggingface Transformers
### KoGPT2-Transformers
- [SKT-AI 에서 공개한 KoGPT2 (ver 1.0)](https://github.com/SKT-AI/KoGPT2)를 [Transformers](https://github.com/huggingface/transformers)에서 사용하도록 하였습니다.
- **SKT-AI 에서 KoGPT2 2.0을 공개하였습니다. https://huggingface.co/skt/kogpt2-base-v2/**
### Demo... | null | null | null | null | 136,825,344 | null | null | [
"GPT2LMHeadModel",
"AutoModelForCausalLM",
"gpt2"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
66f110d9e4b4a1c1cfbe99d4 | 643d190440f20cd9 | 84110b1ed2b9294f | 151,666 | BPE | 151,387 | true | true | true | true | 23 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 493 | unsloth/Qwen2.5-Coder-7B-Instruct-bnb-4bit | unsloth | {
"models": [
{
"_id": "66e9866925a2de6873aa5a9b",
"id": "Qwen/Qwen2.5-Coder-7B-Instruct"
}
],
"relation": "quantized"
} | 59,363 | 388,563 | False | 2024-09-23T06:55:21Z | 2024-11-12T02:53:32Z | transformers | 12 | 0 | null | text-generation | {"parameters": {"F32": 451584, "BF16": 1090328064, "U8": 6729271280}, "total": 7820050928} | [
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"base_model:Qwen/Qwen2.5-Coder-7B-Instruct",
"base_model:quantized:Qwen/Qwen2.5-Coder-7B-I... | null | {"architectures": ["Qwen2ForCausalLM"], "model_type": "qwen2", "quantization_config": {"load_in_4bit": true, "load_in_8bit": false, "quant_method": "bitsandbytes"}, "tokenizer_config": {"bos_token": null, "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "Qwen/Qwen2.5-Coder-7B-Instruct", "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["unsloth", "transformers", "code", "qwen",... | # Finetune Llama 3.1, Gemma 2, Mistral 2-5x faster with 70% less memory via Unsloth!
We have a Qwen 2.5 (all model sizes) [free Google Colab Tesla T4 notebook](https://colab.research.google.com/drive/1Kose-ucXO1IBaZq5BvbwWieuubP7hxvQ?usp=sharing).
Also a [Qwen 2.5 conversational style notebook](https://colab.research.... | null | [
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] | null | [
"en"
] | 7,820,050,928 | null | null | [
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"Qwen2ForCausalLM",
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] | [
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] | [
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"NFC"
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"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 47 | cyankiwi/Qwen3-Coder-30B-A3B-Instruct-AWQ-4bit | cyankiwi | {
"models": [
{
"_id": "688b1597e5e83e19d1b3238a",
"id": "Qwen/Qwen3-Coder-30B-A3B-Instruct"
}
],
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} | 148,105 | 1,118,049 | False | 2025-08-01T02:24:23Z | 2026-03-23T07:20:34Z | transformers | 48 | 2 | null | text-generation | {"parameters": {"I64": 37248, "I32": 29896998912, "BF16": 1569404928}, "total": 5306567040} | [
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} | {"base_model": ["Qwen/Qwen3-Coder-30B-A3B-Instruct"], "datasets": null, "eval_results": null, "language": null, "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": "https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct/blob/main/LICENSE", "metrics": null, "model_name": null... | null | null | [
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"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 824 | Kevinger/Hub-Report-1705947362 | Kevinger | {
"models": [
{
"_id": "621ffdc136468d709f180297",
"id": "sentence-transformers/all-mpnet-base-v2"
}
],
"relation": "finetune"
} | 10 | 143,519 | False | 2024-01-22T18:16:06Z | 2024-01-22T18:50:24Z | transformers | 1 | 0 | [{"name": "Hub-Report-1705947362", "results": []}] | text-classification | {"parameters": {"F32": 109492616}, "total": 109492616} | [
".gitattributes",
"README.md",
"config.json",
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"text-classification",
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"license:apache-2.0",
"text-embeddings-inference",
"endpoints_compatible",
... | null | {"architectures": ["MPNetForSequenceClassification"], "model_type": "mpnet", "tokenizer_config": {"bos_token": "<s>", "cls_token": "<s>", "eos_token": "</s>", "mask_token": "<mask>", "pad_token": "<pad>", "sep_token": "</s>", "unk_token": "[UNK]"}} | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"base_model": "sentence-transformers/all-mpnet-base-v2", "datasets": null, "eval_results": [], "language": null, "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": ["f1", "accuracy"], "model_name": "Hub-Report-1705947362", "pipeline_tag": null, "tags": ["generated_fro... | <!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Hub-Report-1705947362
This model is a fine-tuned version of [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sent... | null | [
"apache-2.0"
] | null | null | 109,492,616 | null | [
"f1",
"accuracy"
] | [
"MPNetForSequenceClassification",
"AutoModelForSequenceClassification",
"mpnet"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
6325a6072a0af3bd451ea46c | 7a4cc4a6090329ff | ab8f40e5a89244c4 | 250,680 | BPE | 250,434 | false | true | true | true | 4 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 31 | bigscience/bloomz | bigscience | null | 1,581 | 152,238 | False | 2022-09-17T10:48:39Z | 2024-07-19T03:29:13Z | transformers | 513 | 0 | [{"name": "bloomz", "results": [{"task": {"type": "Coreference resolution"}, "dataset": {"type": "winogrande", "name": "Winogrande XL (xl)", "config": "xl", "split": "validation", "revision": "a80f460359d1e9a67c006011c94de42a8759430c"}, "metrics": [{"type": "Accuracy", "value": 59.27, "verified": false}]}, {"task": {"t... | text-generation | {"parameters": {"BF16": 176247271424}, "total": 176247271424} | [
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"config.json",
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"text-generation",
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"mr",
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... | null | {"architectures": ["BloomForCausalLM"], "model_type": "bloom", "tokenizer_config": {"unk_token": "<unk>", "eos_token": "</s>", "bos_token": "<s>", "pad_token": "<pad>"}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"datasets": ["bigscience/xP3"], "language": ["ak", "ar", "as", "bm", "bn", "ca", "code", "en", "es", "eu", "fon", "fr", "gu", "hi", "id", "ig", "ki", "kn", "lg", "ln", "ml", "mr", "ne", "nso", "ny", "or", "pa", "pt", "rn", "rw", "sn", "st", "sw", "ta", "te", "tn", "ts", "tum", "tw", "ur", "vi", "wo", "xh", "yo", "zh",... | 
# Table of Contents
1. [Model Summary](#model-summary)
2. [Use](#use)
3. [Limitations](#limitations)
4. [Training](#training)
5. [Evaluation](#evaluation)
7. [Citation](#citation)
# Model Summary
> We present BLOOMZ & mT0, a ... | null | [
"bigscience-bloom-rail-1.0"
] | [
"bigscience/xP3"
] | [
"ak",
"ar",
"as",
"bm",
"bn",
"ca",
"code",
"en",
"es",
"eu",
"fon",
"fr",
"gu",
"hi",
"id",
"ig",
"ki",
"kn",
"lg",
"ln",
"ml",
"mr",
"ne",
"nso",
"ny",
"or",
"pa",
"pt",
"rn",
"rw",
"sn",
"st",
"sw",
"ta",
"te",
"tn",
"ts",
"tum",
"tw",
... | 176,247,271,424 | null | null | [
"AutoModelForCausalLM",
"BloomForCausalLM",
"bloom"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
66eb498455940cd564bc84ad | 848b60b6cd304b65 | ffc911e785abe60e | 151,665 | BPE | 151,387 | true | true | true | true | 22 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 15 | unsloth/Qwen2.5-3B-Instruct-bnb-4bit | unsloth | {
"models": [
{
"_id": "66e98d74d4219fc3f1db215f",
"id": "Qwen/Qwen2.5-3B-Instruct"
}
],
"relation": "quantized"
} | 50,699 | 246,611 | False | 2024-09-18T21:43:32Z | 2025-02-06T03:34:00Z | transformers | 14 | 0 | null | text-generation | {"parameters": {"F32": 237888, "BF16": 311406592, "U8": 2861322944}, "total": 3172967424} | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"generation_config.json",
"merges.txt",
"model.safetensors",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | 2672b588848527e456d320c11926c794539f47d5 | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"unsloth",
"qwen",
"conversational",
"en",
"arxiv:2407.10671",
"base_model:Qwen/Qwen2.5-3B-Instruct",
"base_model:quantized:Qwen/Qwen2.5-3B-Instruct",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"4-... | null | {"architectures": ["Qwen2ForCausalLM"], "model_type": "qwen2", "quantization_config": {"load_in_4bit": true, "load_in_8bit": false, "quant_method": "bitsandbytes"}, "tokenizer_config": {"bos_token": null, "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "Qwen/Qwen2.5-3B-Instruct", "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["unsloth", "transformers", "qwen"]} | # Finetune Llama 3.1, Gemma 2, Mistral 2-5x faster with 70% less memory via Unsloth!
We have a Qwen 2.5 (all model sizes) [free Google Colab Tesla T4 notebook](https://colab.research.google.com/drive/1Kose-ucXO1IBaZq5BvbwWieuubP7hxvQ?usp=sharing).
Also a [Qwen 2.5 conversational style notebook](https://colab.research.... | null | [
"apache-2.0"
] | null | [
"en"
] | 3,172,967,424 | null | null | [
"AutoModelForCausalLM",
"Qwen2ForCausalLM",
"qwen2"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
664491ab5f41dc5dbfd12e23 | f6bd53b02735a215 | 3f3879d485dc5e16 | 63,992 | BPE | 110,029 | true | false | false | true | 231 | Sequence | null | Sequence | [
"Replace"
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"Replace",
"ByteFallback",
"Fuse",
"Strip"
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"NOTICE",
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"model-00006-of-00015.saf... | 05a747585797c119aca9c49965003e99e9490b60 | [
"transformers",
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"llama",
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"arxiv:2403.04652",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"unk_token": "<unk>", "bos_token": "<|startoftext|>", "eos_token": "<|endoftext|>", "pad_token": "<unk>"}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | <div align="center">
<picture>
<img src="https://raw.githubusercontent.com/01-ai/Yi/main/assets/img/Yi_logo_icon_light.svg" width="150px">
</picture>
</div>
<p align="center">
<a href="https://github.com/01-ai">🐙 GitHub</a> •
<a href="https://discord.gg/hYUwWddeAu">👾 Discord</a> •
<a href="https://twitter... | null | [
"apache-2.0"
] | null | null | 34,388,917,248 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
646a5e71f51b56a285c13850 | 8d41a1c0df4c43e0 | 8507af116e525098 | 57,531 | Unigram | null | true | true | true | true | 14 | Sequence | Metaspace | Metaspace | [
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 560 | ahmed-masry/unichart-base-960 | ahmed-masry | null | 6,704 | 155,589 | False | 2023-05-21T18:09:53Z | 2023-12-22T01:31:00Z | transformers | 3 | 0 | null | image-text-to-text | null | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"generation_config.json",
"preprocessor_config.json",
"pytorch_model.bin",
"sentencepiece.bpe.model",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | e145bd54ec4efbb6f944df5aef605450887f9280 | [
"transformers",
"pytorch",
"vision-encoder-decoder",
"image-text-to-text",
"en",
"arxiv:2305.14761",
"license:gpl-3.0",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["VisionEncoderDecoderModel"], "model_type": "vision-encoder-decoder", "tokenizer_config": {"bos_token": "<s>", "cls_token": "<s>", "eos_token": "</s>", "mask_token": {"__type": "AddedToken", "content": "<mask>", "lstrip": true, "normalized": true, "rstrip": false, "single_word": false}, "pad_token": ... | {
"auto_model": "AutoModelForImageTextToText",
"custom_class": null,
"pipeline_tag": "image-text-to-text",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en"], "library_name": null, "license": "gpl-3.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | # TL;DR
The abstract of the paper states that:
> Charts are very popular for analyzing data, visualizing key insights and answering complex reasoning questions about data. To facilitate chart-based data analysis using natural language, several downstream tasks have been introduced recently such as chart question answ... | null | [
"gpl-3.0"
] | null | [
"en"
] | null | null | null | [
"AutoModelForImageTextToText",
"vision-encoder-decoder",
"VisionEncoderDecoderModel"
] | [
"image-text-to-text"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"text"
] |
65ba43be21afe0ec207125b7 | c227782c72f6e559 | 6d16a13156ebd699 | 20,480 | BPE | 20,266 | false | true | true | true | 4 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 825 | vinai/PhoGPT-4B-Chat | vinai | null | 887 | 156,262 | False | 2024-01-31T12:57:34Z | 2024-11-12T07:44:43Z | transformers | 43 | 0 | null | text-generation | null | [
".gitattributes",
"README.md",
"adapt_tokenizer.py",
"attention.py",
"blocks.py",
"config.json",
"configuration_mpt.py",
"custom_embedding.py",
"fc.py",
"ffn.py",
"flash_attn_triton.py",
"generation_config.json",
"hf_prefixlm_converter.py",
"meta_init_context.py",
"modeling_mpt.py",
"n... | 3b11fad96d853b78f2fecc76f13aedb381b02d5a | [
"transformers",
"pytorch",
"mpt",
"text-generation",
"conversational",
"custom_code",
"vi",
"arxiv:2311.02945",
"license:bsd-3-clause",
"text-generation-inference",
"endpoints_compatible",
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] | null | {"architectures": ["MPTForCausalLM"], "auto_map": {"AutoConfig": "configuration_mpt.MPTConfig", "AutoModelForCausalLM": "modeling_mpt.MPTForCausalLM"}, "model_type": "mpt", "tokenizer_config": {"bos_token": "<s>", "eos_token": "</s>", "pad_token": "<pad>", "unk_token": "<unk>", "chat_template": "{% if not add_generatio... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": "modeling_mpt.MPTForCausalLM",
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["vi"], "library_name": null, "license": "bsd-3-clause", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | # PhoGPT: Generative Pre-training for Vietnamese
We open-source a state-of-the-art 4B-parameter generative model series for Vietnamese, which includes the base pre-trained monolingual model PhoGPT-4B and its chat variant, PhoGPT-4B-Chat. The base model, PhoGPT-4B, with exactly 3.7B parameters, is pre-trained from scr... | null | [
"bsd-3-clause"
] | null | [
"vi"
] | null | null | null | [
"modeling_mpt.MPTForCausalLM",
"AutoModelForCausalLM",
"mpt",
"MPTForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
65ad8ef5c8903e28aec1e9a3 | 88cd37e502d27a0a | 0e7bc0977f200d1c | 32,000 | BPE | 58,980 | true | false | true | true | 3 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 88 | abhishekchohan/mistral-7B-forest-dpo | abhishekchohan | null | 212 | 143,563 | False | 2024-01-21T21:39:01Z | 2024-02-14T01:08:16Z | transformers | 2 | 0 | null | text-generation | null | [
".gitattributes",
".ipynb_checkpoints/config-checkpoint.json",
"README.md",
"config.json",
"generation_config.json",
"pytorch_model-00001-of-00003.bin",
"pytorch_model-00002-of-00003.bin",
"pytorch_model-00003-of-00003.bin",
"pytorch_model.bin.index.json",
"special_tokens_map.json",
"tokenizer.j... | 94125ef685bdbc393d77f70c02368a7959ab2eec | [
"transformers",
"pytorch",
"mistral",
"text-generation",
"en",
"dataset:Intel/orca_dpo_pairs",
"dataset:nvidia/HelpSteer",
"dataset:jondurbin/truthy-dpo-v0.1",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["MistralForCausalLM"], "model_type": "mistral", "tokenizer_config": {"bos_token": "<s>", "eos_token": "</s>", "pad_token": "</s>", "unk_token": "<unk>", "use_default_system_prompt": false}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["Intel/orca_dpo_pairs", "nvidia/HelpSteer", "jondurbin/truthy-dpo-v0.1"], "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-generation... | ### Mistral-7B-Forest-DPO
Introducing Mistral-7B-Forest-DPO, a LLM fine-tuned with base model mistralai/Mistral-7B-v0.1, using direct preference optimization.
This model showcases exceptional prowess across a spectrum of natural language processing (NLP) tasks.
A mixture of the following datasets was used for fine-tu... | null | [
"apache-2.0"
] | [
"Intel/orca_dpo_pairs",
"nvidia/HelpSteer",
"jondurbin/truthy-dpo-v0.1"
] | [
"en"
] | null | null | null | [
"AutoModelForCausalLM",
"mistral",
"MistralForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
66f2a1c6ddc2949c859eed74 | 827f6451ef99df30 | dd84a2b1ff97657d | 250,002 | Unigram | null | true | true | true | true | 5 | Precompiled | Sequence | Metaspace | [
"Precompiled"
] | [
"WhitespaceSplit",
"Metaspace"
] | [
"Metaspace"
] | 355 | nlpai-lab/KoE5 | nlpai-lab | {
"models": [
{
"_id": "649e866b9badc2e0a464292e",
"id": "intfloat/multilingual-e5-large"
}
],
"relation": "finetune"
} | 19,315 | 198,926 | False | 2024-09-24T11:25:58Z | 2024-12-23T05:51:39Z | transformers | 49 | 0 | null | feature-extraction | {"parameters": {"F32": 559890432}, "total": 559890432} | [
".gitattributes",
"1_Pooling/config.json",
"2_Normalize/__init__",
"KoE5-results-avg.png",
"KoE5-results-by-datasets.png",
"KoE5-results.png",
"README.md",
"config.json",
"config_sentence_transformers.json",
"model.safetensors",
"modules.json",
"special_tokens_map.json",
"tokenizer.json",
... | bc6d284c60fe5a973e74c1751b92594c9f581213 | [
"transformers",
"safetensors",
"xlm-roberta",
"feature-extraction",
"ko",
"en",
"dataset:nlpai-lab/ko-triplet-v1.0",
"base_model:intfloat/multilingual-e5-large",
"base_model:finetune:intfloat/multilingual-e5-large",
"license:mit",
"text-embeddings-inference",
"endpoints_compatible",
"deploy:... | null | {"architectures": ["XLMRobertaModel"], "model_type": "xlm-roberta", "tokenizer_config": {"bos_token": "<s>", "cls_token": "<s>", "eos_token": "</s>", "mask_token": "<mask>", "pad_token": "<pad>", "sep_token": "</s>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"base_model": ["intfloat/multilingual-e5-large"], "datasets": ["nlpai-lab/ko-triplet-v1.0"], "eval_results": null, "language": ["ko", "en"], "library_name": "transformers", "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "feature-extraction", "tags": n... | # 🔎 KoE5
Introducing KoE5, a model with advanced retrieval abilities.
It has shown remarkable performance in Korean text retrieval.
For details, visit the [KURE repository](https://github.com/nlpai-lab/KURE)
---
## Model Versions
| Model Name | Dimension | Sequence Length | Introduction |
|:----:|:---:|:---:|:---... | null | [
"mit"
] | [
"nlpai-lab/ko-triplet-v1.0"
] | [
"ko",
"en"
] | 559,890,432 | null | null | [
"XLMRobertaModel",
"AutoModel",
"xlm-roberta"
] | [
"feature-extraction"
] | [
"multimodal"
] | [
"text"
] | [
"embeddings"
] |
6636d96942fbbd291bf8fc5d | 3f287b04362fff24 | a1f1bd381dcb9329 | 128,256 | BPE | 280,147 | false | true | true | true | 256 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 158 | migtissera/Tess-2.0-Llama-3-8B | migtissera | null | 16,186 | 221,154 | False | 2024-05-05T00:57:13Z | 2024-06-02T01:04:27Z | transformers | 17 | 0 | null | text-generation | {"parameters": {"F16": 8030261248}, "total": 8030261248} | [
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"config.json",
"generation_config.json",
"model-00001-of-00004.safetensors",
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"... | 9a577d7666eb90c13752ada950a0b5f91d3749f1 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"license:llama3",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": "<|begin_of_text|>", "chat_template": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|s... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": "llama3", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | 
# Tess-2.0-Llama-3-8B
Tess, short for Tesoro (Treasure in Italian), is a general purpose Large Language Model series. Tess-2.0-Llama-3-8B was trained on the meta-llama/Meta-Llama-3-8B base.
Compute for Tess-2.0-Llama-3-8B was ... | null | [
"llama3"
] | null | null | 8,030,261,248 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
681301cd264f6a6cf6366ce2 | 9238df92a51a19a0 | bb994dcedee363b1 | 32,768 | BPE | 32,001 | false | true | false | true | 524 | null | Sequence | ByteLevel | null | [
"Punctuation",
"Split",
"ByteLevel",
"Split"
] | [
"ByteLevel"
] | 826 | tiiuae/Falcon-H1-0.5B-Base | tiiuae | null | 34,245 | 266,170 | False | 2025-05-01T05:08:29Z | 2025-07-31T03:51:29Z | transformers | 16 | 0 | null | text-generation | {"parameters": {"BF16": 521411104}, "total": 521411104} | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
"model.safetensors",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | 59fb76e8c5d3fc7441b062be638e1ba0afd5c687 | [
"transformers",
"safetensors",
"falcon_h1",
"text-generation",
"falcon-h1",
"en",
"arxiv:2507.22448",
"license:other",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["FalconH1ForCausalLM"], "model_type": "falcon_h1", "tokenizer_config": {"eos_token": "<|end_of_text|>"}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "other", "license_name": "falcon-llm-license", "license_link": "https://falconllm.tii.ae/falcon-terms-and-conditions.html", "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["fal... | null | null | [
"other",
"falcon-llm-license",
"https://falconllm.tii.ae/falcon-terms-and-conditions.html"
] | null | [
"en"
] | 521,411,104 | null | null | [
"AutoModelForCausalLM",
"FalconH1ForCausalLM",
"falcon_h1"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
667d2971ccf046998c586b47 | 34093184d10f76a9 | 35b001f3a491f9f9 | 151,646 | BPE | 151,387 | true | true | true | true | 3 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 53 | MaziyarPanahi/calme-2.2-qwen2-7b | MaziyarPanahi | {
"models": [
{
"_id": "665f181fb9e194a20c5e8d44",
"id": "Qwen/Qwen2-7B"
}
],
"relation": "finetune"
} | 45 | 141,947 | False | 2024-06-27T08:57:21Z | 2024-09-19T11:22:48Z | transformers | 1 | 0 | [{"name": "calme-2.2-qwen2-7b", "results": [{"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "IFEval (0-Shot)", "type": "HuggingFaceH4/ifeval", "args": {"num_few_shot": 0}}, "metrics": [{"type": "inst_level_strict_acc and prompt_level_strict_acc", "value": 35.97, "name": "strict accu... | text-generation | {"parameters": {"BF16": 7615616512}, "total": 7615616512} | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"generation_config.json",
"merges.txt",
"model-00001-of-00004.safetensors",
"model-00002-of-00004.safetensors",
"model-00003-of-00004.safetensors",
"model-00004-of-00004.safetensors",
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"qwen2-fine... | c38ab484010a3a1044c2bc08dfe697b1c4bbc7f8 | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"chat",
"qwen",
"finetune",
"chatml",
"OpenHermes-2.5",
"HelpSteer2",
"Orca",
"SlimOrca",
"conversational",
"en",
"dataset:nvidia/HelpSteer2",
"dataset:teknium/OpenHermes-2.5",
"dataset:microsoft/orca-math-word-problems-200k"... | null | {"architectures": ["Qwen2ForCausalLM"], "model_type": "qwen2", "tokenizer_config": {"bos_token": null, "chat_template": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "Qwen/Qwen2-7B", "datasets": ["nvidia/HelpSteer2", "teknium/OpenHermes-2.5", "microsoft/orca-math-word-problems-200k", "Open-Orca/SlimOrca"], "language": ["en"], "library_name": "transformers", "license": "apache-2.0", "pipeline_tag": "text-generation", "tags": ["chat", "qwen", "qwen2", "finetune", "chat... | <img src="./qwen2-fine-tunes-maziyar-panahi.webp" alt="Qwen2 fine-tune" width="500" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
# MaziyarPanahi/calme-2.2-qwen2-7b
This is a fine-tuned version of the `Qwen/Qwen2-7B` model. It aims to improve the base model across all benchmarks.
# ⚡ Quantized GGU... | null | [
"apache-2.0"
] | [
"nvidia/HelpSteer2",
"teknium/OpenHermes-2.5",
"microsoft/orca-math-word-problems-200k",
"Open-Orca/SlimOrca"
] | [
"en"
] | 7,615,616,512 | null | null | [
"AutoModelForCausalLM",
"Qwen2ForCausalLM",
"qwen2"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
64fb343eb60e2c9ddd0b5d9d | 64a5709615eb4b90 | df12e32592e310b8 | 29,797 | WordPiece | null | true | true | true | true | 8 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 827 | pysentimiento/bert-pt-emotion | pysentimiento | null | 1,450 | 147,428 | False | 2023-09-08T14:48:30Z | 2023-09-08T14:49:36Z | transformers | 2 | 0 | null | text-classification | null | [
".gitattributes",
"added_tokens.json",
"config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | 205d0647c6dedcec5f010d11c076e21c7b0fa2de | [
"transformers",
"pytorch",
"bert",
"text-classification",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["BertForSequenceClassification"], "model_type": "bert", "tokenizer_config": {"cls_token": "[CLS]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | null | null | null | null | null | null | null | null | null | [
"BertForSequenceClassification",
"bert",
"AutoModelForSequenceClassification"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
66d68d8fcf9564361233ecb1 | 1e0a2493163c0c63 | 6622292dca275a2d | 128,256 | BPE | 280,147 | false | true | true | true | 256 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 18 | unsloth/Meta-Llama-3.1-70B-Instruct | unsloth | {
"models": [
{
"_id": "66969ad27a033bf62173f3e2",
"id": "meta-llama/Llama-3.1-70B-Instruct"
}
],
"relation": "quantized"
} | 9,637 | 203,191 | False | 2024-09-03T04:16:15Z | 2024-12-07T05:31:42Z | transformers | 4 | 0 | null | text-generation | {"parameters": {"BF16": 70553706496}, "total": 70553706496} | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
"model-00001-of-00030.safetensors",
"model-00002-of-00030.safetensors",
"model-00003-of-00030.safetensors",
"model-00004-of-00030.safetensors",
"model-00005-of-00030.safetensors",
"model-00006-of-00030.safetensors",
"model-0... | 1fdd0a465a29664d155aee8a9f77c55a65cc8d5f | [
"transformers",
"safetensors",
"llama",
"text-generation",
"llama-3",
"meta",
"facebook",
"unsloth",
"conversational",
"en",
"base_model:meta-llama/Llama-3.1-70B-Instruct",
"base_model:quantized:meta-llama/Llama-3.1-70B-Instruct",
"license:llama3.1",
"text-generation-inference",
"endpoin... | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": "<|begin_of_text|>", "chat_template": "{{- bos_token }}\n{%- if custom_tools is defined %}\n {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message is defined %}\n {%- set tools_in_user_messag... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "meta-llama/Meta-Llama-3.1-70B-Instruct", "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "llama3.1", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["llama-3", "llama", "meta", "faceboo... | # Finetune Llama 3.1, Gemma 2, Mistral 2-5x faster with 70% less memory via Unsloth!
We have a free Google Colab Tesla T4 notebook for Llama 3.1 (8B) here: https://colab.research.google.com/drive/1Ys44kVvmeZtnICzWz0xgpRnrIOjZAuxp?usp=sharing
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/D... | null | [
"llama3.1"
] | null | [
"en"
] | 70,553,706,496 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
65fd9fa74254e0834430a340 | 5d253ce26c8dd572 | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 572 | MoritzLaurer/roberta-base-zeroshot-v2.0-c | MoritzLaurer | null | 54,564 | 215,110 | False | 2024-03-22T15:11:35Z | 2024-04-04T07:04:03Z | transformers | 4 | 0 | null | zero-shot-classification | {"parameters": {"F16": 124647170}, "total": 124647170} | [
".gitattributes",
"README.md",
"config.json",
"merges.txt",
"model.safetensors",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"training_args.bin",
"vocab.json"
] | d825e740e0c59881cf0b0b1481ccf726b6d65341 | [
"transformers",
"safetensors",
"roberta",
"text-classification",
"zero-shot-classification",
"en",
"arxiv:2312.17543",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["RobertaForSequenceClassification"], "model_type": "roberta", "tokenizer_config": {"bos_token": "<s>", "cls_token": "<s>", "eos_token": "</s>", "mask_token": "<mask>", "pad_token": "<pad>", "sep_token": "</s>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"base_model": "facebookai/roberta-base", "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "zero-shot-classification", "tags": ["text-classification", "zero-shot-... | # Model description: roberta-base-zeroshot-v2.0-c
## zeroshot-v2.0 series of models
Models in this series are designed for efficient zeroshot classification with the Hugging Face pipeline.
These models can do classification without training data and run on both GPUs and CPUs.
An overview of the latest zeroshot clas... | null | [
"mit"
] | null | [
"en"
] | 124,647,170 | null | null | [
"roberta",
"AutoModelForSequenceClassification",
"RobertaForSequenceClassification"
] | [
"zero-shot-classification",
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
65d7f36d685624d5f2dc927d | 58967ba68263f0aa | 2f8d5857ae5fa330 | 50,277 | BPE | 50,009 | true | true | true | true | 25 | NFC | ByteLevel | ByteLevel | [
"NFC"
] | [
"ByteLevel"
] | [
"ByteLevel"
] | 86 | acon96/Home-3B-v3-GGUF | acon96 | null | 1,324 | 149,401 | False | 2024-02-23T01:22:53Z | 2025-10-25T21:28:14Z | transformers | 43 | 0 | null | text-generation | {"parameters": {"BF16": 2795443200}, "total": 2795443200} | [
".gitattributes",
"Home-3B-v3.f16.gguf",
"Home-3B-v3.q2_k.gguf",
"Home-3B-v3.q3_k_m.gguf",
"Home-3B-v3.q4_k_m.gguf",
"Home-3B-v3.q5_k_m.gguf",
"Home-3B-v3.q8_0.gguf",
"LICENSE",
"README.md",
"config.json",
"configuration_stablelm_epoch.py",
"generation_config.json",
"model-00001-of-00003.saf... | f15e3260782cfa10114c0e98734c80f751a15e13 | [
"transformers",
"safetensors",
"gguf",
"stablelm_epoch",
"text-generation",
"automation",
"home",
"assistant",
"conversational",
"custom_code",
"en",
"de",
"es",
"fr",
"dataset:acon96/Home-Assistant-Requests",
"license:other",
"endpoints_compatible",
"region:us"
] | {"total": 2795443200, "architecture": "stablelm", "context_length": 4096, "chat_template": "{% for message in messages %}\n{%- if message['role'] == 'user' or message['role'] == 'tool' -%}\n<|user|> {{ message['content'] }}{{ eos_token }}\n{%- elif message['role'] == 'system' -%}\n<|system|> {{ message['content'] }}\nS... | {"architectures": ["StableLMEpochForCausalLM"], "auto_map": {"AutoConfig": "configuration_stablelm_epoch.StableLMEpochConfig", "AutoModelForCausalLM": "modeling_stablelm_epoch.StableLMEpochForCausalLM"}, "model_type": "stablelm_epoch", "tokenizer_config": {"bos_token": "<|endoftext|>", "chat_template": "{% for message ... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": "modeling_stablelm_epoch.StableLMEpochForCausalLM",
"pipeline_tag": "text-generation",
"processor": null
} | {"base_model": null, "datasets": ["acon96/Home-Assistant-Requests"], "eval_results": null, "language": ["en", "de", "es", "fr"], "library_name": null, "license": "other", "license_name": null, "license_link": "https://huggingface.co/acon96/Home-3B-v3-GGUF/raw/main/LICENSE", "metrics": null, "model_name": null, "pipelin... | # Home 3B v3
The "Home" model is a fine tuning of the StableLM-3B-Zephyr model from Stability AI. The model is able to control devices in the user's house as well as perform basic question and answering. The fine tuning dataset is a [custom curated dataset](https://github.com/acon96/home-llm) designed to teach the mo... | null | [
"other",
"https://huggingface.co/acon96/Home-3B-v3-GGUF/raw/main/LICENSE"
] | [
"acon96/Home-Assistant-Requests"
] | [
"en",
"de",
"es",
"fr"
] | 2,795,443,200 | 2,795,443,200 | null | [
"modeling_stablelm_epoch.StableLMEpochForCausalLM",
"AutoModelForCausalLM",
"stablelm",
"StableLMEpochForCausalLM",
"stablelm_epoch"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
62694f5d84ad29bad601566e | 5afd7d59d2aa5e01 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 125 | LiYuan/amazon-query-product-ranking | LiYuan | null | 14 | 141,418 | False | 2022-04-27T14:12:45Z | 2022-04-28T13:09:08Z | transformers | 13 | 0 | [{"name": "distilbert-base-uncased-finetuned-mnli-amazon-query-shopping", "results": []}] | text-classification | null | [
".gitattributes",
"README.md",
"checkpoint-13/config.json",
"checkpoint-13/optimizer.pt",
"checkpoint-13/pytorch_model.bin",
"checkpoint-13/rng_state.pth",
"checkpoint-13/scheduler.pt",
"checkpoint-13/special_tokens_map.json",
"checkpoint-13/tokenizer.json",
"checkpoint-13/tokenizer_config.json",
... | 3496770750474054cbc36156f260683a3b56603b | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["DistilBertForSequenceClassification"], "model_type": "distilbert", "tokenizer_config": {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}} | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": [], "language": null, "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": ["accuracy"], "model_name": "distilbert-base-uncased-finetuned-mnli-amazon-query-shopping", "pipeline_tag": null, "tags": ["generated_from_tr... | <!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-mnli-amazon-query-shopping
This model is a fine-tuned version of [distilbert-base-uncased](http... | null | [
"apache-2.0"
] | null | null | null | null | [
"accuracy"
] | [
"DistilBertForSequenceClassification",
"distilbert",
"AutoModelForSequenceClassification"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
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