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should probably proofread and complete it, then remove this comment. -->
# ner-bert-german
This model can be used to do [named-entity recognition](https://en.wikipedia.org/wiki/Named-entity_recognition) i... | null | [
"apache-2.0"
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"wikiann"
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*Dynamic 4-bit: Unsloth's [Dynamic 4-bit Quants](https://unsloth.ai/blog/dynamic-4bit) selectively avoids quantizing certain parame... | null | [
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## 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:
* 8 model sizes, including 0.5B, 1.8B, 4B, 7B, 14B, 32B and 72B dense models, and an MoE m... | null | [
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] | 234 | lmms-lab/llava-onevision-qwen2-7b-si | lmms-lab | null | 2,925 | 324,541 | False | 2024-07-29T05:02:59Z | 2024-09-02T07:24:50Z | transformers | 12 | 0 | [{"name": "llava-onevision-qwen-7b-si", "results": [{"task": {"type": "multimodal"}, "dataset": {"name": "AI2D", "type": "ai2d"}, "metrics": [{"type": "accuracy", "value": 81.6, "name": "accuracy", "verified": false}]}, {"task": {"type": "multimodal"}, "dataset": {"name": "ChartQA", "type": "chartqa"}, "metrics": [{"ty... | text-generation | {"parameters": {"BF16": 8030348832}, "total": 8030348832} | [
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} | {"datasets": ["lmms-lab/LLaVA-OneVision-Data"], "language": ["en", "zh"], "library_name": "transformers", "license": "apache-2.0", "metrics": ["accuracy"], "tags": ["multimodal"], "model-index": [{"name": "llava-onevision-qwen-7b-si", "results": [{"task": {"type": "multimodal"}, "dataset": {"name": "AI2D", "type": "ai2... | # LLaVA-OneVision
Play with the model on the [LLaVA OneVision Chat](https://llava-onevision.lmms-lab.com/).
## Table of Contents
1. [Model Summary](##model-summary)
2. [Use](##use)
3. [Limitations](##limitations)
4. [Training](##training)
5. [Licens... | null | [
"apache-2.0"
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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 | null | null | null | [
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<picture>
<source srcset="https://github.com/XiaomiMiMo/MiMo-VL/raw/main/figures/Xiaomi_MiMo_darkmode.png?raw=true" media="(prefers-color-scheme: dark)">
<img src="https://github.com/XiaomiMiMo/MiMo-VL/raw/main/figures/Xiaomi_MiMo.png?raw=true" width="60%" alt="Xiaomi-MiMo" />
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] | null | {"architectures": ["DeciLMForCausalLM"], "auto_map": {"AutoConfig": "configuration_decilm.DeciLMConfig", "AutoModelForCausalLM": "modeling_decilm.DeciLMForCausalLM"}, "model_type": "nemotron-nas", "tokenizer_config": {"bos_token": "<|begin_of_text|>", "chat_template": "{{- bos_token }}{%- if messages[0]['role'] == 'sys... | {
"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": "transformers", "license": "other", "license_name": "nvidia-open-model-license", "license_link": "https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/", "metrics": null, "model_name": null,... | null | null | [
"other",
"nvidia-open-model-license",
"https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/"
] | null | [
"en"
] | null | null | null | [
"DeciLMForCausalLM",
"AutoModelForCausalLM",
"modeling_decilm.DeciLMForCausalLM",
"nemotron-nas"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
65f58be05f27918caf1237fe | c201230c641b14d0 | 5cc89648c8003a49 | 256,000 | BPE | 580,604 | true | false | true | true | 4 | Replace | null | Sequence | [
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse"
] | 161 | BAAI/bge-reranker-v2-gemma | BAAI | null | 22,831 | 383,568 | False | 2024-03-16T12:09:04Z | 2024-03-19T09:26:32Z | sentence-transformers | 82 | 0 | null | text-classification | {"parameters": {"F32": 2506172416}, "total": 2506172416} | [
".gitattributes",
"README.md",
"assets/BEIR-bge-en-v1.5.png",
"assets/BEIR-e5-mistral.png",
"assets/CMTEB-retrieval-bge-zh-v1.5.png",
"assets/llama-index.png",
"assets/miracl-bge-m3.png",
"config.json",
"generation_config.json",
"model-00001-of-00003.safetensors",
"model-00002-of-00003.safetenso... | 1787044f8b6fb740a9de4557c3a12377f84d9e17 | [
"sentence-transformers",
"safetensors",
"gemma",
"text-generation",
"transformers",
"text-classification",
"multilingual",
"arxiv:2312.15503",
"arxiv:2402.03216",
"license:apache-2.0",
"deploy:azure",
"region:us"
] | null | {"architectures": ["GemmaForCausalLM"], "model_type": "gemma", "tokenizer_config": {"bos_token": "<bos>", "eos_token": "<eos>", "pad_token": "<pad>", "unk_token": "<unk>", "use_default_system_prompt": false}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["multilingual"], "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-classification", "tags": ["transformers", "sentence-transformers"]} | # Reranker
**More details please refer to our Github: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/tree/master).**
- [Model List](#model-list)
- [Usage](#usage)
- [Fine-tuning](#fine-tune)
- [Evaluation](#evaluation)
- [Citation](#citation)
Different from embedding model, reranker uses question and docu... | null | [
"apache-2.0"
] | null | [
"multilingual"
] | 2,506,172,416 | null | null | [
"AutoModelForCausalLM",
"gemma",
"GemmaForCausalLM"
] | [
"text-generation",
"text-classification"
] | [
"text"
] | [
"text"
] | [
"text",
"logits"
] |
66415c94dae378e36b568d08 | 31dabac8dc7ce889 | 7d900c9c71170270 | 257,153 | BPE | 580,604 | true | true | true | true | 218 | Replace | Split | Sequence | [
"Replace"
] | [
"Split"
] | [
"Replace",
"ByteFallback",
"Fuse"
] | 138 | google/paligemma-3b-mix-448 | google | null | 2,901 | 330,413 | manual | 2024-05-13T00:19:32Z | 2024-07-19T12:10:08Z | transformers | 115 | 0 | null | image-text-to-text | {"parameters": {"F32": 2924351216}, "total": 2924351216} | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"generation_config.json",
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"model-00003-of-00003.safetensors",
"model.safetensors.index.json",
"preprocessor_config.json",
"special_tokens_map.json",
"token... | ead2d9a35598cb89119af004f5d023b311d1c4a1 | [
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"arxiv:2103.01913",
"arxiv:2205.12522",
"arxiv:2110.11624",
"arxiv:2108.03353",
... | null | {"architectures": ["PaliGemmaForConditionalGeneration"], "model_type": "paligemma", "tokenizer_config": {"bos_token": "<bos>", "eos_token": "<eos>", "pad_token": "<pad>", "unk_token": "<unk>", "use_default_system_prompt": false}} | {
"auto_model": "AutoModelForImageTextToText",
"custom_class": null,
"pipeline_tag": "image-text-to-text",
"processor": "AutoProcessor"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": "transformers", "license": "gemma", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "image-text-to-text", "tags": null, "extra_gated_heading": "Access PaliGemma on Hugging Face... | null | null | [
"gemma"
] | null | null | 2,924,351,216 | null | null | [
"PaliGemmaForConditionalGeneration",
"AutoModelForImageTextToText",
"paligemma"
] | [
"image-text-to-text"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"text"
] |
6711e1ce7e752052280fe40c | f215c6d483841783 | ef99c2b28a6f0e4a | 100,594 | BPE | 99,757 | true | true | true | true | 594 | Sequence | Sequence | ByteLevel | [] | [
"Split",
"Split",
"Split",
"Split",
"Split",
"Digits",
"ByteLevel"
] | [
"ByteLevel"
] | 585 | deepseek-ai/Janus-1.3B | deepseek-ai | null | 4,340 | 339,439 | False | 2024-10-18T04:19:26Z | 2025-01-27T13:31:24Z | transformers | 594 | 1 | null | any-to-any | {"parameters": {"BF16": 2089297547}, "total": 2089297547} | [
".gitattributes",
"README.md",
"arch.jpg",
"config.json",
"model.safetensors",
"preprocessor_config.json",
"processor_config.json",
"special_tokens_map.json",
"teaser.png",
"tokenizer.json",
"tokenizer_config.json"
] | 56c70f4d34ca802c96254aea598b7d8e58e73840 | [
"transformers",
"safetensors",
"multi_modality",
"muiltimodal",
"text-to-image",
"unified-model",
"any-to-any",
"arxiv:2410.13848",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | {"model_type": "multi_modality", "tokenizer_config": {"bos_token": "<\uff5cbegin\u2581of\u2581sentence\uff5c>", "eos_token": "<\uff5cend\u2581of\u2581sentence\uff5c>", "pad_token": "<\uff5c\u2581pad\u2581\uff5c>", "unk_token": null, "use_default_system_prompt": false}} | {
"auto_model": "AutoModel",
"custom_class": null,
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} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": "transformers", "license": "mit", "license_name": "deepseek", "license_link": "LICENSE", "metrics": null, "model_name": null, "pipeline_tag": "any-to-any", "tags": ["muiltimodal", "text-to-image", "unified-model"]} | ## 0. Update
**2024.10.20**: We have uploaded the correct `tokenizer_config.json`. The previous file was missing the `pad_token`, which caused poor visual generation results.
## 1. Introduction
Janus is a novel autoregressive framework that unifies multimodal understanding and generation.
It addresses the limitatio... | null | [
"mit",
"deepseek",
"LICENSE"
] | null | null | 2,089,297,547 | null | null | [
"multi_modality",
"AutoModel"
] | [
null,
"text-to-image",
"any-to-any"
] | [
"vision",
"multimodal"
] | [
"text",
"any"
] | [
"any",
"image"
] |
621ffdc136468d709f17a212 | 53ecf467267e2824 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 0 | cross-encoder/ms-marco-TinyBERT-L6 | cross-encoder | {
"models": [
{
"_id": "621ffdc136468d709f17e8fc",
"id": "nreimers/TinyBERT_L-6_H-768_v2"
}
],
"relation": "quantized"
} | 11,391 | 366,415 | False | 2022-03-02T23:29:05Z | 2025-08-29T14:35:22Z | sentence-transformers | 2 | 0 | null | text-ranking | {"parameters": {"I64": 512, "F32": 66955777}, "total": 66956289} | [
".gitattributes",
"CERerankingEvaluator_results.csv",
"README.md",
"config.json",
"flax_model.msgpack",
"model.safetensors",
"onnx/model.onnx",
"onnx/model_O1.onnx",
"onnx/model_O2.onnx",
"onnx/model_O3.onnx",
"onnx/model_O4.onnx",
"onnx/model_qint8_arm64.onnx",
"onnx/model_qint8_avx512.onnx... | defbb7d2405cfb2a0f9db418cd8a377c97469552 | [
"sentence-transformers",
"pytorch",
"jax",
"onnx",
"safetensors",
"openvino",
"bert",
"text-classification",
"transformers",
"text-ranking",
"en",
"dataset:sentence-transformers/msmarco",
"base_model:nreimers/TinyBERT_L-6_H-768_v2",
"base_model:quantized:nreimers/TinyBERT_L-6_H-768_v2",
... | 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"
} | {"base_model": ["nreimers/TinyBERT_L-6_H-768_v2"], "datasets": ["sentence-transformers/msmarco"], "eval_results": null, "language": ["en"], "library_name": "sentence-transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-ranking", "... | # Cross-Encoder for MS Marco
This model was trained on the [MS Marco Passage Ranking](https://github.com/microsoft/MSMARCO-Passage-Ranking) task.
The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a... | null | [
"apache-2.0"
] | [
"sentence-transformers/msmarco"
] | [
"en"
] | 66,956,289 | null | null | [
"BertForSequenceClassification",
"bert",
"AutoModelForSequenceClassification"
] | [
"text-ranking",
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
6699ab3e5bc23a0628e06e39 | 595f48517e0303f3 | 12ebeebff692ebb1 | 131,072 | BPE | 269,443 | false | true | true | true | 1,000 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 497 | FlorianJc/Mistral-Nemo-Instruct-2407-vllm-fp8 | FlorianJc | null | 22 | 312,403 | False | 2024-07-18T23:54:38Z | 2024-07-31T17:28:46Z | transformers | 8 | 0 | null | text-generation | {"parameters": {"F16": 1342592000, "F8_E4M3": 10905190400}, "total": 12247782400} | [
".gitattributes",
"README.md",
"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",
"params.json",
"special_tokens_map.json",
"tekken.json",
"tokenize... | 14da5c7273e601bf3f7b6cd55602346bdc9bbfaf | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"vllm",
"fp8",
"conversational",
"en",
"fr",
"de",
"es",
"it",
"pt",
"ru",
"zh",
"ja",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["MistralForCausalLM"], "model_type": "mistral", "quantization_config": {"quant_method": "fp8"}, "tokenizer_config": {"bos_token": "<s>", "chat_template": "{%- if messages[0]['role'] == 'system' %}\n {%- set system_message = messages[0]['content'] %}\n {%- set loop_messages = messages[1:] %}\n{%... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en", "fr", "de", "es", "it", "pt", "ru", "zh", "ja"], "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["vllm", "fp8"], "quantized_by": ... | ## Model infos:
FP8 (F8_E4M3) quantized version of Mistral-Nemo-Instruct-2407 with 512 epochs.
Tested on vLLM 0.5.3, but you need this patch to use it with vLLM 0.5.2 : https://github.com/vllm-project/vllm/pull/6548
```diff
--- vllm/model_executor/models/llama.py 2024-07-19 02:01:59.192831673 +0200
+++ vllm/model_exec... | null | [
"apache-2.0"
] | null | [
"en",
"fr",
"de",
"es",
"it",
"pt",
"ru",
"zh",
"ja"
] | 12,247,782,400 | null | null | [
"AutoModelForCausalLM",
"mistral",
"MistralForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
67b3996182cd92c362310d7b | 5913c555399afcb0 | e6ffdc5dd5f1fd86 | 256,000 | BPE | 580,604 | true | true | true | true | 249 | Replace | Split | Sequence | [
"Replace"
] | [
"Split"
] | [
"Replace",
"ByteFallback",
"Fuse"
] | 147 | google/siglip2-large-patch16-256 | google | null | 45,379 | 503,015 | False | 2025-02-17T20:17:37Z | 2025-02-21T04:03:44Z | transformers | 6 | 0 | null | zero-shot-image-classification | {"parameters": {"F32": 881526786}, "total": 881526786} | [
".gitattributes",
"README.md",
"config.json",
"model.safetensors",
"preprocessor_config.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer.model",
"tokenizer_config.json"
] | 787800c8990e6f058423089178e718139608408c | [
"transformers",
"safetensors",
"siglip",
"vision",
"zero-shot-image-classification",
"arxiv:2502.14786",
"arxiv:2303.15343",
"arxiv:2209.06794",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | {"model_type": "siglip", "tokenizer_config": {"bos_token": "<bos>", "chat_template": "{{ bos_token }}{% if messages[0]['role'] == 'system' %}{{ raise_exception('System role not supported') }}{% endif %}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Convers... | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": null,
"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": "zero-shot-image-classification", "tags": ["vision"], "widget": [{"src": "https://huggi... | null | null | [
"apache-2.0"
] | null | null | 881,526,786 | null | null | [
"AutoModel",
"siglip"
] | [
null,
"zero-shot-image-classification"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"logits"
] |
621ffdc136468d709f1799cd | 79641b267692b72f | f87d7bfce4fd9617 | 30,000 | Unigram | null | true | true | true | true | 5 | Sequence | Sequence | Metaspace | [
"Replace",
"Replace",
"NFKD",
"StripAccents",
"Lowercase",
"Precompiled"
] | [
"WhitespaceSplit",
"Metaspace"
] | [
"Metaspace"
] | 586 | bhadresh-savani/albert-base-v2-emotion | bhadresh-savani | null | 14,057 | 336,411 | False | 2022-03-02T23:29:05Z | 2021-09-15T18:03:36Z | transformers | 6 | 0 | null | text-classification | null | [
".gitattributes",
"README.md",
"config.json",
"convert_flax_to_pytorch.py",
"convert_pytorch_to_flax.py",
"convert_pytorch_to_tensorflow.py",
"flax_model.msgpack",
"pytorch_model.bin",
"special_tokens_map.json",
"tf_model.h5",
"tokenizer.json",
"tokenizer_config.json"
] | 4812613b3c07c549e13f09bb266dbf0e59f48de7 | [
"transformers",
"pytorch",
"tf",
"jax",
"albert",
"text-classification",
"emotion",
"en",
"dataset:emotion",
"arxiv:1909.11942",
"license:apache-2.0",
"endpoints_compatible",
"deploy:azure",
"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",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["emotion"], "eval_results": null, "language": ["en"], "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": ["Accuracy, F1 Score"], "model_name": null, "pipeline_tag": null, "tags": ["text-classification", "emotion", "pytorch"], "thumbnai... | # Albert-base-v2-emotion
## Model description:
[Albert](https://arxiv.org/pdf/1909.11942v6.pdf) is A Lite BERT architecture that has significantly fewer parameters than a traditional BERT architecture.
[Albert-base-v2](https://huggingface.co/albert-base-v2) finetuned on the emotion dataset using HuggingFace Trainer w... | null | [
"apache-2.0"
] | [
"emotion"
] | [
"en"
] | null | null | [
"Accuracy, F1 Score"
] | [
"AlbertForSequenceClassification",
"albert",
"AutoModelForSequenceClassification"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
6657e1c0f043923ffcabe822 | 12fda1d82910c40a | fadac82906efdc90 | 32,768 | BPE | 58,980 | true | false | true | true | 3 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 587 | solidrust/Codestral-22B-v0.1-hf-AWQ | solidrust | {
"models": [
{
"_id": "665767464018967357a21ddf",
"id": "bullerwins/Codestral-22B-v0.1-hf"
}
],
"relation": "quantized"
} | 446 | 428,636 | False | 2024-05-30T02:17:36Z | 2024-09-03T08:51:43Z | transformers | 2 | 0 | null | text-generation | {"parameters": {"I32": 21843935232, "F16": 403347456}, "total": 22247282688} | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
"model-00001-of-00003.safetensors",
"model-00002-of-00003.safetensors",
"model-00003-of-00003.safetensors",
"model.safetensors.index.json",
"quant_config.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer.model... | 17791fcc93a5cdeefef8e70a0c8d93bc916e7d98 | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"4-bit",
"AWQ",
"autotrain_compatible",
"endpoints_compatible",
"base_model:bullerwins/Codestral-22B-v0.1-hf",
"base_model:quantized:bullerwins/Codestral-22B-v0.1-hf",
"text-generation-inference",
"awq",
"region:us"
] | null | {"architectures": ["MistralForCausalLM"], "model_type": "mistral", "quantization_config": {"bits": 4, "quant_method": "awq"}, "tokenizer_config": {"bos_token": "<s>", "eos_token": "</s>", "pad_token": null, "unk_token": "<unk>", "use_default_system_prompt": false}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "bullerwins/Codestral-22B-v0.1-hf", "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": "text-generation", "tags": ["4-bit", "AWQ", "text-generation", "a... | # bullerwins/Codestral-22B-v0.1-hf AWQ
- Model creator: [bullerwins](https://huggingface.co/bullerwins)
- Original model: [Codestral-22B-v0.1-hf](https://huggingface.co/bullerwins/Codestral-22B-v0.1-hf)
## How to use
### Install the necessary packages
```bash
pip install --upgrade autoawq autoawq-kernels
```
###... | null | null | null | null | 22,247,282,688 | null | null | [
"AutoModelForCausalLM",
"mistral",
"MistralForCausalLM"
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] | [
"text"
] | [
"text"
] | [
"text"
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}
],
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] | null | {"architectures": ["Qwen3ForCausalLM"], "model_type": "qwen3", "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' %}\n... | {
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} | {"base_model": "Qwen/Qwen3-1.7B", "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": "https://huggingface.co/Qwen/Qwen3-1.7B/blob/main/LICENSE", "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["qw... | <div>
<p style="margin-bottom: 0; margin-top: 0;">
<strong>See <a href="https://huggingface.co/collections/unsloth/qwen3-680edabfb790c8c34a242f95">our collection</a> for all versions of Qwen3 including GGUF, 4-bit & 16-bit formats.</strong>
</p>
<p style="margin-bottom: 0;">
<em>Learn to run Qwen3 correct... | null | [
"apache-2.0",
"https://huggingface.co/Qwen/Qwen3-1.7B/blob/main/LICENSE"
] | null | [
"en"
] | null | null | null | [
"AutoModelForCausalLM",
"Qwen3ForCausalLM",
"qwen3"
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"base_model:finetune:mod... | null | {"architectures": ["ModularStarEncoder"], "auto_map": {"AutoConfig": "config.ModularStarEncoderConfig", "AutoModel": "modularStarEncoder.ModularStarEncoder"}, "model_type": "ModularStarEncoder", "tokenizer_config": {"bos_token": "<|endoftext|>", "cls_token": "<cls>", "eos_token": "<|endoftext|>", "mask_token": "<mask>"... | {
"auto_model": "AutoModel",
"custom_class": "modularStarEncoder.ModularStarEncoder",
"pipeline_tag": "feature-extraction",
"processor": null
} | {"base_model": ["modularStarEncoder/ModularStarEncoder"], "datasets": ["bigcode/the-stack-v2", "modularStarEncoder/SynthCode2Code2NL-neardedup"], "eval_results": null, "language": null, "library_name": "transformers", "license": "bigcode-openrail-m", "license_name": null, "license_link": null, "metrics": null, "model_n... | # ModularStarEncoder-1B Fine-Tuned model
<!-- Provide a quick summary of what the model is/does. -->
ModularStarEncoder-finetuned (MoSE) is an encoder built on top of [ModularStarEncoder-1B Pre-trained](https://huggingface.co/andreagurioli1995/ModularStarEncoder) on [SynthCoNL](https://huggingface.co/datasets/andreag... | null | [
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] | null | 1,061,894,145 | null | null | [
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"deploy:azure",
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] | 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",
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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} | # Roberta base finetuned on a dataset of empathic reactions to news stories (Buechel et al., 2018; Tafreshi et al., 2021, 2022)
## Table of Contents
- [Model Details](#model-details)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limit... | null | [
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] | null | [
"en"
] | null | null | null | [
"roberta",
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".gitattributes",
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"config.json",
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"merges.txt",
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] | caf238c63db946bdfbd00575713462838e823997 | [
"transformers",
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"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:2101.11038",
"license:mit",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["RobertaForMaskedLM"], "model_type": "roberta"} | {
"auto_model": "AutoModelForMaskedLM",
"custom_class": null,
"pipeline_tag": "fill-mask",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["bookcorpus", "wikipedia"], "eval_results": null, "language": "en", "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["exbert"]} | # Muppet: Massive Multi-task Representations with Pre-Finetuning
# RoBERTa base model
This is a Massive Multi-task Pre-finetuned version of Roberta base. It was introduced in
[this paper](https://arxiv.org/abs/2101.11038). The model improves over roberta-base in a wide range of GLUE, QA tasks (details can be found in ... | null | [
"mit"
] | [
"bookcorpus",
"wikipedia"
] | [
"en"
] | null | null | null | [
"roberta",
"AutoModelForMaskedLM",
"RobertaForMaskedLM"
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"fill-mask"
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"Precompiled"
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] | 782256a408c536816ac315f91f69699a9dfff079 | [
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"text-classification",
"hu",
"license:mit",
"text-embeddings-inference",
"endpoints_compatible",
"deploy:azure",
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] | null | {"architectures": ["XLMRobertaForSequenceClassification"], "model_type": "xlm-roberta", "tokenizer_config": {"bos_token": "<s>", "eos_token": "</s>", "sep_token": "</s>", "cls_token": "<s>", "unk_token": "<unk>", "pad_token": "<pad>", "mask_token": {"content": "<mask>", "single_word": false, "lstrip": true, "rstrip": f... | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
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} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["hu"], "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": ["accuracy"], "model_name": null, "pipeline_tag": null, "tags": ["text-classification"], "widget": [{"text": "J\u00f3 reggelt! majd k\u00fcld\u0... | # Hungarian Sentence-level Sentiment Analysis Model with XLM-RoBERTa
For further models, scripts and details, see [our repository](https://github.com/nytud/sentiment-analysis) or [our demo site](https://juniper.nytud.hu/demo/nlp).
- Pretrained model used: XLM-RoBERTa base
- Finetuned on Hungarian Twitter Sentimen... | null | [
"mit"
] | null | [
"hu"
] | null | null | [
"accuracy"
] | [
"AutoModelForSequenceClassification",
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"xlm-roberta"
] | [
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] | [
"text"
] | [
"text"
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"models": [
{
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}
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"relation": "finetune"
} | 21,344 | 393,930 | False | 2024-09-25T18:39:13Z | 2025-06-02T21:11:33Z | transformers | 18 | 0 | null | text-generation | {"parameters": {"BF16": 3212749824}, "total": 3212749824} | [
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"regi... | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": "<|begin_of_text|>", "eos_token": "<|end_of_text|>", "pad_token": "<|finetune_right_pad_id|>", "unk_token": null}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "meta-llama/Llama-3.2-3B", "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "llama3.2", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["llama-3", "llama", "meta", "facebook", "unsloth", ... | # Finetune Llama 3.2, Gemma 2, Mistral 2-5x faster with 70% less memory via Unsloth!
We have a free Google Colab Tesla T4 notebook for Llama 3.2 (3B) here: https://colab.research.google.com/drive/1Ys44kVvmeZtnICzWz0xgpRnrIOjZAuxp?usp=sharing
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/D... | null | [
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] | null | [
"en"
] | 3,212,749,824 | null | null | [
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"text"
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"text"
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} | null | null | null | null | null | null | null | null | null | [
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"en",
"arxiv:2502.02737",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
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] | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "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": "transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | # SmolLM2
## Table of Contents
1. [Model Summary](#model-summary)
2. [Evaluation](#evaluation)
3. [Limitations](#limitations)
4. [Training](#training)
5. [License](#license)
6. [Citation](#citation... | null | [
"apache-2.0"
] | null | [
"en"
] | 1,711,376,384 | null | null | [
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"text"
] | [
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}
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} | 8,327 | 348,139 | False | 2024-04-21T15:11:45Z | 2024-07-04T10:08:56Z | transformers | 688 | 1 | null | text-generation | null | [
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"te... | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": "<|begin_of_text|>", "chat_template": "{{ '<|begin_of_text|>' }}{% set system_message = 'You are a helpful assistant.' %}{% if messages[0]['role'] == 'system' %}{% set system_message = messages[0]['content'] %}{% set loop_m... | {
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} | {"base_model": "meta-llama/Meta-Llama-3-8B-Instruct", "datasets": null, "eval_results": null, "language": ["en", "zh"], "library_name": "transformers", "license": "llama3", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-generation", "tags": ["llama-factory", "orpo... | 🚀 [May 9, 2024] We're excited to introduce [Llama3-**70B**-Chinese-Chat](https://huggingface.co/shenzhi-wang/Llama3-70B-Chinese-Chat)! Full-parameter fine-tuned on a mixed Chinese-English dataset of ~100K preference pairs, its Chinese performance **surpasses ChatGPT** and **matches GPT-4**, as shown by C-Eval and CMML... | null | [
"llama3"
] | null | [
"en",
"zh"
] | null | null | null | [
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"llama",
"LlamaForCausalLM"
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"NFC"
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"ByteLevel"
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"ByteLevel"
] | 86 | anas-awadalla/mpt-1b-redpajama-200b | anas-awadalla | null | 2,800 | 322,517 | False | 2023-05-25T00:58:32Z | 2023-08-05T06:19:59Z | transformers | 3 | 0 | null | text-generation | null | [
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] | null | {"architectures": ["MosaicGPT"], "auto_map": {"AutoConfig": "configuration_mosaic_gpt.MosaicGPTConfig", "AutoModelForCausalLM": "mosaic_gpt.MosaicGPT"}, "model_type": "mosaic_gpt", "tokenizer_config": {"bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "unk_token": "<|endoftext|>"}} | {
"auto_model": "AutoModelForCausalLM",
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} | {"base_model": null, "datasets": ["togethercomputer/RedPajama-Data-1T"], "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} | # MPT-1b-RedPajama-200b
MPT-1b-RedPajama-200b is a 1.3 billion parameter decoder-only transformer trained on the [RedPajama dataset](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T).
The model was trained for 200B tokens by sampling from the subsets of the RedPajama dataset in the same proportions a... | null | [
"apache-2.0"
] | [
"togethercomputer/RedPajama-Data-1T"
] | null | null | null | null | [
"mosaic_gpt",
"MosaicGPT",
"AutoModelForCausalLM",
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] | [
"text-generation"
] | [
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] | [
"text"
] | [
"text"
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},
... | 10,904 | 441,903 | False | 2024-08-30T12:45:27Z | 2026-01-28T17:17:43Z | transformers | 52 | 0 | null | text-generation | null | [
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"base_model:merge:spe... | null | {"architectures": ["MistralForCausalLM"], "model_type": "mistral", "tokenizer_config": {"bos_token": "<s>", "chat_template": "{{bos_token}}{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistan... | {
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} | {"base_model": ["speakleash/Bielik-11B-v2", "speakleash/Bielik-11B-v2.0-Instruct", "speakleash/Bielik-11B-v2.1-Instruct", "speakleash/Bielik-11B-v2.2-Instruct"], "datasets": null, "eval_results": null, "language": ["pl"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": nul... | <p align="center">
<img src="https://huggingface.co/speakleash/Bielik-11B-v2/raw/main/speakleash_cyfronet.png">
</p>
# Bielik-11B-v2.3-Instruct
Bielik-11B-v2.3-Instruct is a generative text model featuring 11 billion parameters.
It is a linear merge of the [Bielik-11B-v2.0-Instruct](https://huggingface.co/speaklea... | null | [
"apache-2.0"
] | null | [
"pl"
] | null | null | null | [
"AutoModelForCausalLM",
"mistral",
"MistralForCausalLM"
] | [
"text-generation"
] | [
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"text"
] | [
"text"
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"Prepend",
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] | null | [
"Replace",
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] | 591 | WizardLMTeam/WizardCoder-Python-34B-V1.0 | WizardLMTeam | null | 335 | 308,604 | False | 2023-08-26T03:59:07Z | 2024-01-19T03:06:41Z | transformers | 772 | 0 | [{"name": "WizardCoder-Python-34B-V1.0", "results": [{"task": {"type": "text-generation"}, "dataset": {"type": "openai_humaneval", "name": "HumanEval"}, "metrics": [{"name": "pass@1", "type": "pass@1", "value": 0.732, "verified": false}]}]}] | text-generation | null | [
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] | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": {"__type": "AddedToken", "content": "<s>", "lstrip": false, "normalized": true, "rstrip": false, "single_word": false}, "eos_token": {"__type": "AddedToken", "content": "</s>", "lstrip": false, "normalized": true, "rstrip":... | {
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"pipeline_tag": "text-generation",
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} | {"library_name": "transformers", "license": "llama2", "metrics": ["code_eval"], "tags": ["code"], "model-index": [{"name": "WizardCoder-Python-34B-V1.0", "results": [{"task": {"type": "text-generation"}, "dataset": {"name": "HumanEval", "type": "openai_humaneval"}, "metrics": [{"type": "pass@1", "value": 0.732, "name":... | ## WizardCoder: Empowering Code Large Language Models with Evol-Instruct
<p style="font-size:28px;" align="center">
🏠 <a href="https://wizardlm.github.io/" target="_blank">Home Page</a> </p>
<p align="center">
<p align="center">
🤗 <a href="https://huggingface.co/WizardLM" target="_blank">HF Repo</a> •🐱 <a href="ht... | null | [
"llama2"
] | null | null | null | null | [
"code_eval"
] | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
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"NFC"
] | [
"Split",
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"models": [
{
"_id": "66cee7970454d6467b65060e",
"id": "Qwen/Qwen2-VL-2B-Instruct"
}
],
"relation": "finetune"
} | 241,705 | 849,306 | False | 2025-03-27T11:08:51Z | 2025-07-25T01:11:10Z | transformers | 117 | 1 | null | text-classification | {"parameters": {"BF16": 2444721665}, "total": 2444721665} | [
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] | 5b91da00be08ae2949e4e842b94d721c5c31eda3 | [
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"base_model:finetune:Qwen/Qwen2-VL-2B-Instruct",
"license:cc-by-nc-4.0",
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] | null | {"architectures": ["JinaVLForRanking"], "auto_map": {"AutoModel": "modeling.JinaVLForRanking"}, "model_type": "qwen2_vl", "processor_config": {"chat_template": "{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'syste... | {
"auto_model": "AutoModel",
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"pipeline_tag": "feature-extraction",
"processor": "AutoProcessor"
} | {"base_model": ["Qwen/Qwen2-VL-2B-Instruct"], "datasets": null, "eval_results": null, "language": ["multilingual"], "library_name": "transformers", "license": "cc-by-nc-4.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-classification", "tags": ["vidore", "reran... | null | null | [
"cc-by-nc-4.0"
] | null | [
"multilingual"
] | 2,444,721,665 | null | null | [
"AutoModel",
"qwen2_vl",
"JinaVLForRanking",
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] | [
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"multimodal"
] | [
"text"
] | [
"logits",
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] |
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"Strip",
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 592 | llm-blender/PairRM | llm-blender | null | 735 | 309,261 | False | 2023-11-06T07:08:24Z | 2024-01-22T15:47:01Z | transformers | 205 | 0 | null | text-generation | null | [
".gitattributes",
"README.md",
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"ranker_config.json",
"special_tokens_map.json",
"spm.model",
"tokenizer.json",
"tokenizer_config.json",
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] | 5b880cc73776ac75a835b3e0bd5169bcb5be013b | [
"transformers",
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"evaluation",
"llm",
"instruction",
"reranking",
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"en",
"dataset:openai/summarize_from_feedback",
"dataset:openai/webgpt_comparisons",
"dataset:Dahoas/synthetic-instruct-gptj-pairwise",
... | null | {"model_type": "deberta", "tokenizer_config": {"bos_token": "[CLS]", "cls_token": "[CLS]", "eos_token": "[SEP]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": null,
"processor": null
} | {"base_model": null, "datasets": ["openai/summarize_from_feedback", "openai/webgpt_comparisons", "Dahoas/synthetic-instruct-gptj-pairwise", "Anthropic/hh-rlhf", "lmsys/chatbot_arena_conversations", "openbmb/UltraFeedback"], "eval_results": null, "language": ["en"], "library_name": null, "license": "mit", "license_name"... | # Pairwise Reward Model for LLMs (PairRM) from LLM-Blender
- Github: [https://github.com/yuchenlin/LLM-Blender](https://github.com/yuchenlin/LLM-Blender)
- Paper: [https://arxiv.org/abs/2306.02561](https://arxiv.org/abs/2306.02561)
- Space Demo: [https://huggingface.co/spaces/llm-blender/LLM-Blender](https://hugging... | null | [
"mit"
] | [
"openai/summarize_from_feedback",
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"Anthropic/hh-rlhf",
"lmsys/chatbot_arena_conversations",
"openbmb/UltraFeedback"
] | [
"en"
] | null | null | [
"accuracy"
] | [
"AutoModel",
"deberta"
] | [
null,
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
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] | 475 | TechxGenus/DeepSeek-Coder-V2-Lite-Base-AWQ | TechxGenus | null | 69 | 306,485 | False | 2024-06-18T18:53:42Z | 2024-06-22T14:29:37Z | transformers | 3 | 0 | null | text-generation | {"parameters": {"I32": 15283519488, "F16": 422964736}, "total": 15706484224} | [
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<!-- markdownlint-disable html -->
<!-- markdownlint-disable no-duplicate-header -->
<div align="center">
<img src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/logo.svg?raw=true" width="60%" alt="DeepSeek-V2" />
</div>
<hr>
<div align="center" style="line-... | null | [
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"text"
] | [
"text"
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"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 33 | LTP/base1 | LTP | null | 197 | 307,003 | False | 2022-08-14T04:35:09Z | 2025-08-15T08:40:34Z | transformers | 6 | 0 | null | null | {"parameters": {"I64": 512, "F32": 164978416}, "total": 164978928} | [
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} | null | ------------------------------ | -----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------... | null | null | null | null | 164,978,928 | null | null | [
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null
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634946623d8dc83a55ccbb87 | a57660e30f43755b | 7ce118731980e5c6 | 29,794 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 279 | dominguesm/legal-bert-ner-base-cased-ptbr | dominguesm | null | 193 | 306,887 | False | 2022-10-14T11:22:10Z | 2023-03-16T22:29:37Z | transformers | 18 | 0 | [{"name": "checkpoints", "results": [{"task": {"name": "Token Classification", "type": "token-classification"}, "metrics": [{"name": "F1", "type": "f1", "value": 0.9525622169191057, "verified": false}, {"name": "Precision", "type": "precision", "value": 0.9438680702115613, "verified": false}, {"name": "Recall", "type":... | token-classification | {"parameters": {"I64": 512, "F32": 108342541}, "total": 108343053} | [
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"runs/e3_lr2e-05/1665668964.0356135/events.out.tfevents.1665668964.9dd23284ee7b.94.1",
"runs/e3_lr2e-05/1665669018.9886503/events.out.tfevents.1665669018.9dd23284ee7b.94.3",
"runs/e3_lr2e-05/1665669243.3965552/even... | 44210927c925448df025985e0ed48081bb5ac57c | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"bert",
"token-classification",
"generated_from_trainer",
"pt",
"license:cc-by-4.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["BertForTokenClassification"], "model_type": "bert", "tokenizer_config": {"cls_token": "[CLS]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
"auto_model": "AutoModelForTokenClassification",
"custom_class": null,
"pipeline_tag": "token-classification",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["pt"], "library_name": null, "license": "cc-by-4.0", "license_name": null, "license_link": null, "metrics": ["precision", "recall", "f1", "accuracy"], "model_name": null, "pipeline_tag": null, "tags": ["generated_from_trainer"], "thumbnail": "htt... | ## (BERT base) NER model in the legal domain in Portuguese
**README under construction**
**ner-legal-bert-base-cased-ptbr** is a NER model (token classification) in the legal domain in Portuguese that was finetuned from the model [dominguesm/legal-bert-base-cased-ptbr](https://huggingface.co/dominguesm/legal-bert-bas... | null | [
"cc-by-4.0"
] | null | [
"pt"
] | 108,343,053 | null | [
"precision",
"recall",
"f1",
"accuracy"
] | [
"AutoModelForTokenClassification",
"BertForTokenClassification",
"bert"
] | [
"token-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
67d12ea244be283390542537 | 46323c0f61df476d | 1fe366e13272caa0 | 262,145 | BPE | 514,906 | true | true | true | true | 6,415 | Replace | Split | Sequence | [
"Replace"
] | [
"Split"
] | [
"Replace",
"ByteFallback",
"Fuse"
] | 49 | unsloth/gemma-3-27b-it | unsloth | {
"models": [
{
"_id": "67c35b9bb236f0d365bf29d3",
"id": "google/gemma-3-27b-it"
}
],
"relation": "finetune"
} | 6,568 | 368,009 | False | 2025-03-12T06:50:10Z | 2025-05-12T08:04:33Z | transformers | 16 | 0 | null | image-text-to-text | {"parameters": {"BF16": 27432406640}, "total": 27432406640} | [
".gitattributes",
"README.md",
"added_tokens.json",
"chat_template.jinja",
"chat_template.json",
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"generation_config.json",
"model-00001-of-00012.safetensors",
"model-00002-of-00012.safetensors",
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"model-00005... | 7a5a3053dbd5d1d58e48159e87b9df2fc545a49a | [
"transformers",
"safetensors",
"gemma3",
"image-text-to-text",
"unsloth",
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"conversational",
"en",
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"arxiv:1904.09728",
"arxiv:1705.03551",
"arxiv:1911.01547",
"arxiv:1907.10641",
"arxiv:1903.00161",
"arxi... | null | {"architectures": ["Gemma3ForConditionalGeneration"], "model_type": "gemma3", "processor_config": {"chat_template": "{{ bos_token }}\n{%- if messages[0]['role'] == 'system' -%}\n {%- if messages[0]['content'] is string -%}\n {%- set first_user_prefix = messages[0]['content'] + '\n\n' -%}\n {%- else -%}\n ... | {
"auto_model": "AutoModelForImageTextToText",
"custom_class": null,
"pipeline_tag": "image-text-to-text",
"processor": "AutoProcessor"
} | {"base_model": "google/gemma-3-27b-it", "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "gemma", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["unsloth", "transformers", "gemma3", "gemma", "google"]} | <div>
<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><a href="https://docs.... | null | [
"gemma"
] | null | [
"en"
] | 27,432,406,640 | null | null | [
"AutoModelForImageTextToText",
"Gemma3ForConditionalGeneration",
"gemma3"
] | [
"image-text-to-text"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"text"
] |
65a9487b57f263e3d0cda17d | b679a407515528d6 | 66824fb64a2f47bb | 100,289 | BPE | 100,000 | false | true | false | true | 33 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 501 | stabilityai/stablelm-2-1_6b | stabilityai | null | 5,236 | 317,616 | False | 2024-01-18T15:49:15Z | 2024-07-10T11:59:18Z | transformers | 193 | 0 | null | text-generation | {"parameters": {"BF16": 1644515328}, "total": 1644515328} | [
".gitattributes",
"LICENSE.md",
"README.md",
"config.json",
"configuration_stablelm.py",
"generation_config.json",
"merges.txt",
"model.safetensors",
"modeling_stablelm.py",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | f499ead74c53749bd93cebc6ce8bc0d7bdf1eaef | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"causal-lm",
"en",
"de",
"es",
"fr",
"it",
"nl",
"pt",
"dataset:tiiuae/falcon-refinedweb",
"dataset:togethercomputer/RedPajama-Data-1T",
"dataset:uonlp/CulturaX",
"dataset:CarperAI/pilev2-dev",
"dataset:bigcode/starcoderda... | null | {"architectures": ["StableLmForCausalLM"], "model_type": "stablelm", "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": ["tiiuae/falcon-refinedweb", "togethercomputer/RedPajama-Data-1T", "uonlp/CulturaX", "CarperAI/pilev2-dev", "bigcode/starcoderdata", "DataProvenanceInitiative/Commercially-Verified-Licenses"], "eval_results": null, "language": ["en", "de", "es", "fr", "it", "nl", "pt"], "library_name": ... | # `Stable LM 2 1.6B`
Please note: For commercial use, please refer to https://stability.ai/license
## Model Description
`Stable LM 2 1.6B` is a 1.6 billion parameter decoder-only language model pre-trained on 2 trillion tokens of diverse multilingual and code datasets for two epochs.
## Usage
Get started generatin... | null | [
"other"
] | [
"tiiuae/falcon-refinedweb",
"togethercomputer/RedPajama-Data-1T",
"uonlp/CulturaX",
"CarperAI/pilev2-dev",
"bigcode/starcoderdata",
"DataProvenanceInitiative/Commercially-Verified-Licenses"
] | [
"en",
"de",
"es",
"fr",
"it",
"nl",
"pt"
] | 1,644,515,328 | null | null | [
"AutoModelForCausalLM",
"stablelm",
"StableLmForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
621ffdc136468d709f17dad1 | 2a296d0c9d0e3920 | 7a200ecd99c055f3 | 42,000 | WordPiece | null | true | true | true | true | 110 | Sequence | BertPreTokenizer | WordPiece | [
"Replace",
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 593 | m3hrdadfi/distilbert-zwnj-wnli-mean-tokens | m3hrdadfi | null | 8 | 304,947 | False | 2022-03-02T23:29:05Z | 2021-06-28T18:05:51Z | sentence-transformers | 1 | 0 | null | sentence-similarity | null | [
".gitattributes",
"1_Pooling/config.json",
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"config_sentence_transformers.json",
"modules.json",
"pytorch_model.bin",
"sentence_bert_config.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | 6d0d94f899be52bc72f68f3f3b5800650cb0395b | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"text-embeddings-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["DistilBertModel"], "model_type": "distilbert", "tokenizer_config": {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}} | {
"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": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "sentence-similarity", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "tr... | # Sentence Embeddings with `distilbert-zwnj-wnli-mean-tokens`
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers... | null | null | null | null | null | null | null | [
"AutoModel",
"distilbert",
"DistilBertModel"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
621ffdc136468d709f17a2f6 | 42107db771322c6d | e9c2ff5e848a7383 | 35,000 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 382 | daekeun-ml/koelectra-small-v3-nsmc | daekeun-ml | null | 3,239 | 543,854 | False | 2022-03-02T23:29:05Z | 2025-03-09T21:39:03Z | transformers | 5 | 0 | null | text-classification | {"parameters": {"I64": 512, "F32": 14122498}, "total": 14123010} | [
".gitattributes",
"README.md",
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"model.safetensors",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"training_args.bin",
"vocab.txt"
] | d1c0233fc013ae8bd879d7b61f2bec69aa3af4c9 | [
"transformers",
"pytorch",
"safetensors",
"electra",
"text-classification",
"classification",
"ko",
"dataset:nsmc",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["ElectraForSequenceClassification"], "model_type": "electra", "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": ["nsmc"], "eval_results": null, "language": ["ko"], "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": ["accuracy", "f1", "precision", "recall- accuracy"], "model_name": null, "pipeline_tag": null, "tags": ["classification"], "widget": [{"text... | # Sentiment Binary Classification (fine-tuning with KoELECTRA-Small-v3 model and Naver Sentiment Movie Corpus dataset)
## Usage (Amazon SageMaker inference applicable)
It uses the interface of the SageMaker Inference Toolkit as is, so it can be easily deployed to SageMaker Endpoint.
### inference_nsmc.py
```python
i... | null | [
"mit"
] | [
"nsmc"
] | [
"ko"
] | 14,123,010 | null | [
"accuracy",
"f1",
"precision",
"recall- accuracy"
] | [
"ElectraForSequenceClassification",
"AutoModelForSequenceClassification",
"electra"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
6531ff866d969dc22ef5fa0a | 191e06b04083279c | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 26 | Falconsai/intent_classification | Falconsai | null | 690 | 308,030 | False | 2023-10-20T04:18:14Z | 2023-12-09T14:40:16Z | transformers | 58 | 0 | null | text-classification | {"parameters": {"F32": 66965007}, "total": 66965007} | [
".gitattributes",
"README.md",
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"coreml/text-classification/float32_model.mlpackage/Data/com.apple.CoreML/model.mlmodel",
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"coreml/text-classification/float32_model.mlpackage/Manifest.json",
"mode... | 630d0d4668170a2a64d8d80b04d9844415bd4367 | [
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"en",
"arxiv:1910.01108",
"license:apache-2.0",
"text-embeddings-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | 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": 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": "text-classification", "tags": null, "widget": [{"text": "I ordered from you 2 weeks ago and it... | # Model Card: Fine-Tuned DistilBERT for User Intent Classification
## Model Description
The **Fine-Tuned DistilBERT** is a variant of the BERT transformer model,
distilled for efficient performance while maintaining high accuracy.
It has been adapted and fine-tuned for the specific task of classifying user intent in ... | null | [
"apache-2.0"
] | null | [
"en"
] | 66,965,007 | null | null | [
"DistilBertForSequenceClassification",
"distilbert",
"AutoModelForSequenceClassification"
] | [
"text-classification"
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"text"
] | [
"text"
] | [
"logits"
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621ffdc136468d709f17dc64 | c88b36417e2a82ee | dd84a2b1ff97657d | 250,002 | Unigram | null | true | true | true | true | 5 | Precompiled | Sequence | Metaspace | [
"Precompiled"
] | [
"WhitespaceSplit",
"Metaspace"
] | [
"Metaspace"
] | 64 | marefa-nlp/marefa-ner | marefa-nlp | null | 4,514 | 322,922 | False | 2022-03-02T23:29:05Z | 2021-12-04T05:21:57Z | transformers | 24 | 0 | null | token-classification | null | [
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"special_tokens_map.json",
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"training_args.bin"
] | 97150023f089d776bf025950d1e4506625c71c34 | [
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"pytorch",
"xlm-roberta",
"token-classification",
"ar",
"dataset:Marefa-NER",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["XLMRobertaForTokenClassification"], "model_type": "xlm-roberta", "tokenizer_config": {"bos_token": "<s>", "eos_token": "</s>", "sep_token": "</s>", "cls_token": "<s>", "unk_token": "<unk>", "pad_token": "<pad>", "mask_token": {"content": "<mask>", "single_word": false, "lstrip": true, "rstrip": fals... | {
"auto_model": "AutoModelForTokenClassification",
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"pipeline_tag": "token-classification",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["Marefa-NER"], "eval_results": null, "language": "ar", "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null, "widget": [{"text": "\u0641\u064a \u0627\u0633\u062a\u0627\u062f \u0627\u06... | # Tebyan تبيـان
## Marefa Arabic Named Entity Recognition Model
## نموذج المعرفة لتصنيف أجزاء النص
<p align="center">
<img src="https://huggingface.co/marefa-nlp/marefa-ner/resolve/main/assets/marefa-tebyan-banner.png" alt="Marfa Arabic NER Model" width="600"/>
</p?
---------
**Version**: 1.3
**Last Upda... | null | null | [
"Marefa-NER"
] | [
"ar"
] | null | null | null | [
"XLMRobertaForTokenClassification",
"AutoModelForTokenClassification",
"xlm-roberta"
] | [
"token-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
65648d0d0c754d598ce002b0 | dccbb7b59eac010e | daab2a732bab72e2 | 128,001 | Unigram | null | true | true | true | true | 5 | Sequence | Sequence | Metaspace | [
"Strip",
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 159 | MoritzLaurer/deberta-v3-large-zeroshot-v1.1-all-33 | MoritzLaurer | null | 4,659 | 319,212 | False | 2023-11-27T12:35:25Z | 2024-04-01T15:10:33Z | transformers | 58 | 0 | null | zero-shot-classification | {"parameters": {"F16": 435063810}, "total": 435063810} | [
".gitattributes",
"LICENSE",
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"spm.model",
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] | c5dca3bda16d30337e493e3e3e5caa19a3e7c8c2 | [
"transformers",
"pytorch",
"onnx",
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"deberta-v2",
"text-classification",
"zero-shot-classification",
"en",
"arxiv:2312.17543",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["DebertaV2ForSequenceClassification"], "model_type": "deberta-v2", "tokenizer_config": {"bos_token": "[CLS]", "cls_token": "[CLS]", "eos_token": "[SEP]", "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": null, "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-classification"]} | # Model description: deberta-v3-large-zeroshot-v1.1-all-33
The model is designed for zero-shot classification with the Hugging Face pipeline.
The model can do one universal classification task: determine whether a hypothesis is "true" or "not true" given a text
(`entailment` vs. `not_entailment`).
This task format... | null | [
"mit"
] | null | [
"en"
] | 435,063,810 | null | null | [
"AutoModelForSequenceClassification",
"deberta-v2",
"DebertaV2ForSequenceClassification"
] | [
"zero-shot-classification",
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
6273afc63d70b36612a8ef9b | 931798862de10283 | ef4806e2ade9022f | 32,100 | Unigram | null | true | true | true | true | 103 | Precompiled | Sequence | Metaspace | [
"Precompiled"
] | [
"WhitespaceSplit",
"Metaspace"
] | [
"Metaspace"
] | 149 | fabiochiu/t5-small-medium-title-generation | fabiochiu | null | 14 | 303,692 | False | 2022-05-05T11:06:46Z | 2023-08-03T07:55:10Z | transformers | 10 | 0 | [{"name": "t5-small-medium-title-generation", "results": []}] | null | {"parameters": {"F32": 60506624}, "total": 60506624} | [
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"model.safetensors",
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"tf_model.h5",
"tokenizer.json",
"tokenizer_config.json"
] | 05963f4e2799db77118b9a2d0024edee4c9c94ba | [
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] | null | {"architectures": ["T5ForConditionalGeneration"], "model_type": "t5", "tokenizer_config": {"eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>"}} | {
"auto_model": "AutoModelForSeq2SeqLM",
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} | {"base_model": null, "datasets": null, "eval_results": [], "language": null, "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": "t5-small-medium-title-generation", "pipeline_tag": null, "tags": ["generated_from_keras_callback"], "widget": [{"text": "summari... | <!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Model description
This model is [t5-small](https://huggingface.co/t5-small) fine-tuned on the [190k Medium Articles](https://www.kaggle... | null | null | null | null | 60,506,624 | null | null | [
"t5",
"T5ForConditionalGeneration",
"AutoModelForSeq2SeqLM"
] | [
"text2text-generation"
] | null | null | null |
6627253888596f571aedfb4a | 803e3c8528c55bf1 | a1f1bd381dcb9329 | 128,256 | BPE | 280,147 | false | true | true | true | 256 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
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"ByteLevel"
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{
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"id": "meta-llama/Meta-Llama-3-8B-Instruct"
}
],
"relation": "finetune"
} | 14,440 | 373,227 | False | 2024-04-23T03:04:24Z | 2024-06-02T02:14:24Z | transformers | 114 | 0 | [{"name": "lightblue/suzume-llama-3-8B-multilingual", "results": []}] | text-generation | {"parameters": {"BF16": 8030261248}, "total": 8030261248} | [
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"en... | 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... | {
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<img width=400 src="https://cdn-uploads.huggingface.co/production/uploads/64b63f8ad57e02621dc93c8b/kg3QjQOde0X743csGJT-f.png" alt="Suzume - a Japanese tree sparrow"/>
</p>
# Suzume
[[Paper](https://arxiv.org/abs/2405.12612)] [[Dataset](https://huggingface.co/datasets/lightblue/tagengo-gpt4)]
Thi... | null | [
"other",
"llama-3",
"https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct/raw/main/LICENSE"
] | null | null | 8,030,261,248 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
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] | [
"text"
] | [
"text"
] |
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"ByteLevel"
] | [
"ByteLevel"
] | 6 | sentence-transformers/msmarco-roberta-base-v2 | sentence-transformers | null | 347 | 310,711 | False | 2022-03-02T23:29:05Z | 2025-03-06T13:37:08Z | sentence-transformers | 1 | 0 | null | sentence-similarity | {"parameters": {"I64": 514, "F32": 124645632}, "total": 124646146} | [
".gitattributes",
"1_Pooling/config.json",
"README.md",
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"onnx/model_O3.onnx",
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... | febc26b63c1274abe0ccc806ad9d404dc7fa76cc | [
"sentence-transformers",
"pytorch",
"tf",
"jax",
"onnx",
"safetensors",
"openvino",
"roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["RobertaModel"], "model_type": "roberta", "tokenizer_config": {"unk_token": {"content": "<unk>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "bos_token": {"content": "<s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": t... | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": "sentence-transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "sentence-similarity", "tags": ["sentence-transformers", "feature-extraction",... | # sentence-transformers/msmarco-roberta-base-v2
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when yo... | null | [
"apache-2.0"
] | null | null | 124,646,146 | null | null | [
"roberta",
"AutoModel",
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] | [
"sentence-similarity",
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] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
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"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 0 | RedHatAI/bge-large-en-v1.5-quant | RedHatAI | null | 84 | 308,757 | False | 2023-10-03T15:50:50Z | 2023-11-13T17:05:07Z | transformers | 22 | 0 | [{"name": "bge-large-en-v1.5-quant", "results": [{"task": {"type": "Classification"}, "dataset": {"type": "mteb/amazon_counterfactual", "name": "MTEB AmazonCounterfactualClassification (en)", "config": "en", "split": "test", "revision": "e8379541af4e31359cca9fbcf4b00f2671dba205"}, "metrics": [{"type": "accuracy", "valu... | feature-extraction | null | [
".gitattributes",
"README.md",
"bge-large-latency.png",
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] | f805bd9801169b3ed443f628d8a3514dc6298914 | [
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"quantized",
"embeddings",
"int8",
"mteb",
"deepsparse",
"en",
"license:mit",
"model-index",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["BertModel"], "model_type": "bert"} | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"language": ["en"], "license": "mit", "tags": ["sparse", "sparsity", "quantized", "onnx", "embeddings", "int8", "mteb", "deepsparse"], "model-index": [{"name": "bge-large-en-v1.5-quant", "results": [{"task": {"type": "Classification"}, "dataset": {"name": "MTEB AmazonCounterfactualClassification (en)", "type": "mteb/a... | null | null | [
"mit"
] | null | [
"en"
] | null | null | null | [
"BertModel",
"AutoModel",
"bert"
] | [
"feature-extraction"
] | [
"multimodal"
] | [
"text"
] | [
"embeddings"
] |
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"ByteLevel"
] | [
"ByteLevel"
] | 594 | kotoba-tech/kotoba-whisper-v1.0 | kotoba-tech | null | 1,196 | 308,063 | False | 2024-04-14T08:53:48Z | 2024-10-23T00:47:03Z | transformers | 58 | 0 | [{"name": "kotoba-tech/kotoba-whisper-v1.0", "results": [{"task": {"type": "automatic-speech-recognition"}, "dataset": {"name": "CommonVoice 8 (Japanese test set)", "type": "japanese-asr/ja_asr.common_voice_8_0"}, "metrics": [{"name": "WER", "type": "WER", "value": 59.2, "verified": false}, {"name": "CER", "type": "CER... | automatic-speech-recognition | {"parameters": {"F32": 756405760}, "total": 756405760} | [
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"merges.txt",
"model.safetensors",
"normalizer.json",
"preprocessor_config.json",
"run_distillation.py",
"special_tokens_m... | f116c5b1ac613f3e3e8d49ee7e31aa8fbf819a41 | [
"transformers",
"safetensors",
"whisper",
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"audio",
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"ja",
"dataset:japanese-asr/whisper_transcriptions.reazonspeech.large",
"dataset:japanese-asr/whisper_transcriptions.reazonspeech.large.wer_10.0",
"dataset:japanese-asr/whisper_transcriptions.r... | null | {"architectures": ["WhisperForConditionalGeneration"], "model_type": "whisper", "tokenizer_config": {"bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "pad_token": "<|endoftext|>", "unk_token": "<|endoftext|>"}} | {
"auto_model": "AutoModelForSpeechSeq2Seq",
"custom_class": null,
"pipeline_tag": "automatic-speech-recognition",
"processor": "AutoProcessor"
} | {"datasets": ["japanese-asr/whisper_transcriptions.reazonspeech.large", "japanese-asr/whisper_transcriptions.reazonspeech.large.wer_10.0", "japanese-asr/whisper_transcriptions.reazonspeech.large.wer_10.0.vectorized"], "language": "ja", "library_name": "transformers", "license": "apache-2.0", "metrics": ["wer", "cer"], ... | # Kotoba-Whisper (v1.0)
[**faster-whisper weight**](https://huggingface.co/kotoba-tech/kotoba-whisper-v1.0-faster), [**whisper.cpp weight**](https://huggingface.co/kotoba-tech/kotoba-whisper-v1.0-ggml), [**pipeline with stable-ts/punctuation**](https://huggingface.co/kotoba-tech/kotoba-whisper-v1.1)
***News:*** Newer... | null | [
"apache-2.0"
] | [
"japanese-asr/whisper_transcriptions.reazonspeech.large",
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"japanese-asr/whisper_transcriptions.reazonspeech.large.wer_10.0.vectorized"
] | [
"ja"
] | 756,405,760 | null | [
"wer",
"cer"
] | [
"WhisperForConditionalGeneration",
"whisper",
"AutoModelForSpeechSeq2Seq"
] | [
"automatic-speech-recognition"
] | [
"multimodal"
] | [
"audio"
] | [
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] |
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"ByteLevel"
] | 401 | CohereLabs/c4ai-command-r-v01 | CohereLabs | null | 14,900 | 361,745 | auto | 2024-03-11T15:50:54Z | 2025-04-16T03:10:13Z | transformers | 1,104 | 0 | null | text-generation | null | [
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] | null | {"architectures": ["CohereForCausalLM"], "model_type": "cohere", "tokenizer_config": {"bos_token": "<BOS_TOKEN>", "chat_template": [{"name": "default", "template": "{{ bos_token }}{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% elif false... | {
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"pipeline_tag": "text-generation",
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} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en", "fr", "de", "es", "it", "pt", "ja", "ko", "zh", "ar"], "library_name": "transformers", "license": "cc-by-nc-4.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null, "inferenc... | null | null | [
"cc-by-nc-4.0"
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"en",
"fr",
"de",
"es",
"it",
"pt",
"ja",
"ko",
"zh",
"ar"
] | null | null | null | [
"CohereForCausalLM",
"AutoModelForCausalLM",
"cohere"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
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67ff92b1076c67c2283306a3 | b647996d6543f730 | 7ab62ce4e32550df | 151,674 | BPE | 151,387 | true | true | true | true | 31 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 172 | OpenGVLab/InternVL3-78B-Instruct | OpenGVLab | {
"models": [
{
"_id": "6800b6df9ff7a46b499e858f",
"id": "OpenGVLab/InternVL3-78B-Pretrained"
}
],
"relation": "finetune"
} | 338 | 305,211 | False | 2025-04-16T11:21:21Z | 2025-09-11T12:51:37Z | transformers | 8 | 0 | null | image-text-to-text | {"parameters": {"BF16": 78408318336}, "total": 78408318336} | [
".gitattributes",
"README.md",
"added_tokens.json",
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"model-00001-of-00033.safeten... | 84364c38db707eb006ab4db14df245de8d345174 | [
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"internvl_chat",
"feature-extraction",
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"custom_code",
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"conversational",
"multilingual",
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"arxiv:2404.16821",
"arxiv:2412.05271",
"arxiv:2411.10442",
"arxiv:2504.10479",
"arxiv:2412.09616",
"base_model:O... | 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/InternVL3-78B-Pretrained"], "datasets": null, "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_name": nu... | # InternVL3-78B-Instruct
[\[📂 GitHub\]](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://huggingfac... | null | [
"other",
"qwen",
"https://huggingface.co/Qwen/Qwen2.5-72B-Instruct/blob/main/LICENSE"
] | null | [
"multilingual"
] | 78,408,318,336 | null | null | [
"AutoModel",
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"image"
] | [
"text",
"embeddings"
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660d592839e32b059e43b3fa | 8ea9d3573d2b0194 | aa40254639535039 | 255,029 | BPE | 253,333 | true | true | true | true | 37 | NFC | Sequence | ByteLevel | [
"NFC"
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"Digits",
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"ByteLevel"
] | 401 | CohereLabs/c4ai-command-r-plus | CohereLabs | null | 2,808 | 315,220 | auto | 2024-04-03T13:27:04Z | 2025-04-16T03:03:37Z | transformers | 1,775 | 0 | null | text-generation | null | [
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} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en", "fr", "de", "es", "it", "pt", "ja", "ko", "zh", "ar"], "library_name": "transformers", "license": "cc-by-nc-4.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null, "inferenc... | null | null | [
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66421160d530bc54492bb212 | e4ef70d70f53cba9 | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 595 | Onebu/intent-global | Onebu | {
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}
],
"relation": "finetune"
} | 1 | 17 | False | 2024-05-13T13:10:56Z | 2024-05-13T14:27:04Z | transformers | 0 | 0 | [{"name": "intent-global", "results": []}] | text-classification | {"parameters": {"F32": 124648708}, "total": 124648708} | [
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"base_model:finetune:FacebookAI/roberta-base",
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"text-embeddings-inference",
"endpoints_compatible",
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] | 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>"}} | {
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"pipeline_tag": "text-classification",
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} | {"base_model": "roberta-base", "datasets": null, "eval_results": [], "language": null, "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": ["accuracy", "precision", "recall", "f1"], "model_name": "intent-global", "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. -->
# intent-global
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It... | null | [
"mit"
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# Occiglot-7B-EU5-Instruct
> A [polyglot](https://en.wikipedia.org/wiki/Multilingualism#In_individuals) language model for the [Occident](https://en.wikipedia.org/wiki/Occident).
>
**Occiglot-7B-EU5-Instruct** is a the ins... | null | [
"apache-2.0"
] | null | [
"en",
"es",
"de",
"fr",
"it"
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] | [
"text"
] | [
"text"
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] | 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... | {
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} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": "cc-by-sa-4.0", "license_name": null, "license_link": null, "metrics": ["accuracy"], "model_name": null, "pipeline_tag": "text-generation", "tags": ["code"]} | A capable language model for text to SQL generation for Postgres, Redshift and Snowflake that is on-par with the most capable generalist frontier models.
## Model Description
Developed by: Defog, In... | null | [
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632607315cf955bfbbe16842 | 81e57f49429072b8 | d3a5589134b292ff | 250,100 | Unigram | null | true | true | true | true | 3 | Sequence | Metaspace | Metaspace | [
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] | 598 | kriton/greek-text-summarization | kriton | null | 30 | 302,427 | False | 2022-09-17T17:43:13Z | 2024-02-10T10:35:59Z | transformers | 7 | 0 | null | summarization | null | [
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] | null | {"architectures": ["MT5ForConditionalGeneration"], "model_type": "mt5", "tokenizer_config": {"eos_token": "</s>", "pad_token": "<pad>", "unk_token": "<unk>"}} | {
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} | {"base_model": null, "datasets": null, "eval_results": null, "language": "el", "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["summarization"]} | # Abstractive Greek Text Summarization
Application is deployed in [Hugging Face Spaces](https://huggingface.co/spaces/kriton/greek-text-summarization).<br>
We trained mT5-small for the downstream task of text summarization in Greek.
```python
from transformers import AutoTokenizer
from transformers import AutoModelFor... | null | [
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] | null | [
"el"
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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. -->
Tiny random llama2 for CI test | null | null | null | null | 4,186,944 | null | null | [
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"ByteLevel"
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} | {"base_model": null, "datasets": ["climatebert/climate_specificity"], "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": ["climate"]} | # Model Card for distilroberta-base-climate-specificity
## Model Description
This is the fine-tuned ClimateBERT language model with a classification head for classifying climate-related paragraphs into specific and non-specific paragraphs.
Using the [climatebert/distilroberta-base-climate-f](https://huggingface.co/c... | null | [
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We have a Google Colab Tesla T4 notebook for Llama-3 8b here: https://colab.research.google.com/drive/135ced7oHytdxu3N2DNe1Z0kqjyYIkDXp?usp=sharing
Built with Meta Llama 3
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main... | null | [
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"models": [
{
"_id": "66e81c0ecfd2f50991047e92",
"id": "Qwen/Qwen2.5-0.5B-Instruct"
}
],
"relation": "quantized"
} | 49,650 | 418,792 | False | 2025-02-06T02:05:30Z | 2025-02-06T03:39:03Z | transformers | 4 | 0 | null | text-generation | {"parameters": {"F32": 59880, "BF16": 190338944, "U8": 313236116}, "total": 503634940} | [
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"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
... | 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",
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"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "Qwen/Qwen2.5-0.5B-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"]} | <div>
<p style="margin-bottom: 0;">
<strong>See <a href="https://huggingface.co/collections/unsloth/qwen-25-66fe4c08fb9ada518e8a0d3f">our collection</a> for versions of Qwen2.5 including 4-bit formats.</strong>
</p>
<p style="margin-bottom: 0;">
<em>Unsloth's <a href="https://unsloth.ai/blog/dynamic-4bit"... | null | [
"apache-2.0"
] | null | [
"en"
] | 503,634,940 | null | null | [
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] | [
"text"
] | [
"text"
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"ByteLevel"
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] | 599 | hf-tiny-model-private/tiny-random-CodeGenForCausalLM | hf-tiny-model-private | null | 2,095 | 303,188 | False | 2023-03-29T18:38:49Z | 2023-03-29T18:38:54Z | transformers | 0 | 0 | null | text-generation | null | [
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"ByteFallback",
"Fuse"
] | 147 | google/siglip2-base-patch16-512 | google | null | 102,536 | 762,256 | False | 2025-02-17T20:02:34Z | 2025-02-21T04:03:42Z | transformers | 37 | 1 | null | zero-shot-image-classification | {"parameters": {"F32": 375823874}, "total": 375823874} | [
".gitattributes",
"README.md",
"config.json",
"model.safetensors",
"preprocessor_config.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer.model",
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] | a89f5c5093f902bf39d3cd4d81d2c09867f0724b | [
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"arxiv:2502.14786",
"arxiv:2303.15343",
"arxiv:2209.06794",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | {"model_type": "siglip", "tokenizer_config": {"bos_token": "<bos>", "chat_template": "{{ bos_token }}{% if messages[0]['role'] == 'system' %}{{ raise_exception('System role not supported') }}{% endif %}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Convers... | {
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"apache-2.0"
] | null | null | 375,823,874 | null | null | [
"AutoModel",
"siglip"
] | [
null,
"zero-shot-image-classification"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"logits"
] |
66b1ee77370319ebd0478f22 | ba58f5d5c866bd11 | 2033f0e76cd5232e | 128,000 | BPE | 260,662 | true | false | true | true | 261 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 600 | utter-project/EuroLLM-1.7B-Instruct | utter-project | {
"models": [
{
"_id": "66b1eddcbdaee30b3d5b6123",
"id": "utter-project/EuroLLM-1.7B"
}
],
"relation": "finetune"
} | 18,673 | 611,774 | False | 2024-08-06T09:35:51Z | 2024-12-16T12:46:04Z | transformers | 97 | 0 | null | text-generation | {"parameters": {"BF16": 1656850432}, "total": 1656850432} | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
"model.safetensors",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer.model",
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] | a25c7fa65fc2a644e6270b8940dbe295b51da681 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"en",
"de",
"es",
"fr",
"it",
"pt",
"pl",
"nl",
"tr",
"sv",
"cs",
"el",
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"sk",
"da",
"lt",
"lv",
"et",
"bg",
"no",
"ca",
"hr",
"ga",
"mt",
"gl",... | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": "<s>", "chat_template": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}<|im_start|>system\n{% if messages[0]['role'] == 'system' %}{{ messages[0]['content'] }}{% endif %}<|im... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": ["utter-project/EuroLLM-1.7B"], "datasets": null, "eval_results": null, "language": ["en", "de", "es", "fr", "it", "pt", "pl", "nl", "tr", "sv", "cs", "el", "hu", "ro", "fi", "uk", "sl", "sk", "da", "lt", "lv", "et", "bg", "no", "ca", "hr", "ga", "mt", "gl", "zh", "ru", "ko", "ja", "ar", "hi"], "library_... | ## *Model updated on September 24*
# Model Card for EuroLLM-1.7B-Instruct
This is the model card for the first instruction tuned model of the EuroLLM series: EuroLLM-1.7B-Instruct. You can also check the pre-trained version: [EuroLLM-1.7B](https://huggingface.co/utter-project/EuroLLM-1.7B).
- **Developed by:**... | null | [
"apache-2.0"
] | null | [
"en",
"de",
"es",
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"it",
"pt",
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"nl",
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"hr",
"ga",
"mt",
"gl",
"zh",
"ru",
"ko",
"ja",
"ar",
"hi"
] | 1,656,850,432 | null | null | [
"AutoModelForCausalLM",
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"LlamaForCausalLM"
] | [
"text-generation"
] | [
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"text"
] | [
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64c5b89db496b4e17690cfb4 | e23b6bdbe047bb7e | 8bd7d71767538b82 | 32,000 | BPE | 61,249 | true | false | true | true | 3 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 46 | upstage/SOLAR-0-70b-16bit | upstage | null | 28,234 | 379,574 | False | 2023-07-30T01:10:53Z | 2023-09-13T09:14:02Z | transformers | 258 | 0 | null | text-generation | null | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
"pytorch_model-00001-of-00015.bin",
"pytorch_model-00002-of-00015.bin",
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"pytorch_model-00006-of-00015.bin",
"pytorch... | 43ff16100b9aec3c4d0c56116796149c1c455efc | [
"transformers",
"pytorch",
"llama",
"text-generation",
"upstage",
"llama-2",
"instruct",
"instruction",
"en",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 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": null, "eval_results": null, "language": ["en"], "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-generation", "tags": ["upstage", "llama-2", "instruct", "instruction"]} | # Updates
Solar, a new bot created by Upstage, is now available on **Poe**. As a top-ranked model on the HuggingFace Open LLM leaderboard, and a fine tune of Llama 2, Solar is a great example of the progress enabled by open source.
Try now at https://poe.com/Solar-0-70b
# SOLAR-0-70b-16bit model card
The model name h... | null | null | null | [
"en"
] | null | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
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"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 601 | imvladikon/sentence-transformers-alephbert | imvladikon | null | 4,764 | 330,589 | False | 2023-04-04T07:57:25Z | 2023-04-06T15:21:09Z | sentence-transformers | 9 | 0 | null | sentence-similarity | null | [
".gitattributes",
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"pytorch_model.bin",
"sentence_bert_config.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | 547db5a2028766922251e5e2be6a973674ee0c07 | [
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"he",
"arxiv:2104.04052",
"arxiv:1908.10084",
"text-embeddings-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["BertModel"], "model_type": "bert", "tokenizer_config": {"cls_token": "[CLS]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
"auto_model": "AutoModel",
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"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["he"], "library_name": "sentence-transformers", "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "sentence-similarity", "tags": ["sentence-transformers", "feature-extraction", "sent... | # imvladikon/sentence-transformers-alephbert[WIP]
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
Current version is distillation of the [LaBSE](https://huggingface.co/s... | null | null | null | [
"he"
] | null | null | null | [
"BertModel",
"AutoModel",
"bert"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
621ffdc136468d709f17b7a2 | 2d0341bd3a73bae6 | ef4806e2ade9022f | 32,100 | Unigram | null | true | true | true | true | 103 | Sequence | Metaspace | Metaspace | [
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 32 | google/t5-small-lm-adapt | google | null | 209 | 299,780 | False | 2022-03-02T23:29:05Z | 2023-01-24T16:52:21Z | transformers | 10 | 0 | null | null | null | [
".gitattributes",
"README.md",
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"pytorch_model.bin",
"special_tokens_map.json",
"spiece.model",
"tf_model.h5",
"tokenizer.json",
"tokenizer_config.json"
] | 4ff52753a4ddd906de166c573db99280719b22f9 | [
"transformers",
"pytorch",
"tf",
"t5",
"text2text-generation",
"t5-lm-adapt",
"en",
"dataset:c4",
"arxiv:2002.05202",
"arxiv:1910.10683",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"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"
} | {"base_model": null, "datasets": ["c4"], "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": ["t5-lm-adapt"]} | [Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) Version 1.1 - LM-Adapted
## Version 1.1 - LM-Adapted
[T5 Version 1.1 - LM Adapted](https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md#lm-adapted-t511lm100k) includes the follo... | null | [
"apache-2.0"
] | [
"c4"
] | [
"en"
] | null | null | null | [
"t5",
"T5ForConditionalGeneration",
"AutoModelForSeq2SeqLM"
] | [
"text2text-generation"
] | null | null | null |
68e92bb2df72923e5af1103d | 7428ef9ae5ef868f | 67da9c236fa81925 | 151,669 | BPE | 151,387 | true | true | true | true | 26 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 47 | OpenPipe/Qwen3-14B-Instruct | OpenPipe | {
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{
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"id": "Qwen/Qwen3-14B-Base"
}
],
"relation": "finetune"
} | 621,318 | 1,833,870 | False | 2025-10-10T15:52:18Z | 2025-10-10T16:02:55Z | transformers | 12 | 0 | null | text-generation | {"parameters": {"BF16": 14768307200}, "total": 14768307200} | [
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"model-00005-of-00008.safetens... | 99e4a359e990d8d00b77ac707c34bbfbc9d8c98a | [
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"base_model:finetune:Qwen/Qwen3-14B-Base",
"license:apache-2.0",
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"endpoints_compatible",
"region:us"
] | null | {"architectures": ["Qwen3ForCausalLM"], "model_type": "qwen3", "tokenizer_config": {"bos_token": null, "eos_token": "<|im_end|>", "pad_token": "<|endoftext|>", "unk_token": null}, "chat_template_jinja": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0].role == 'system' %}\n {{- message... | {
"auto_model": "AutoModelForCausalLM",
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} | {"base_model": ["Qwen/Qwen3-14B-Base"], "datasets": null, "eval_results": null, "language": null, "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": "https://huggingface.co/Qwen/Qwen3-14B/blob/main/LICENSE", "metrics": null, "model_name": null, "pipeline_tag": "text-generatio... | null | null | [
"apache-2.0",
"https://huggingface.co/Qwen/Qwen3-14B/blob/main/LICENSE"
] | null | null | 14,768,307,200 | null | null | [
"AutoModelForCausalLM",
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"qwen3"
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"text"
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"text"
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"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 602 | K-intelligence/Midm-2.0-Base-Instruct | K-intelligence | null | 5,991 | 342,946 | False | 2025-07-03T13:17:35Z | 2025-10-29T05:33:06Z | transformers | 134 | 0 | null | text-generation | null | [
".gitattributes",
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"transformers",
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"KT",
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"Mi:dm",
"conversational",
"en",
"ko",
"license:mit",
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] | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": "<|begin_of_text|>", "chat_template": "{{- bos_token }}\n\n{%- if not date_string is defined %}\n {%- if strftime_now is defined %}\n {%- set date_string = strftime_now('%d %b %Y') %}\n {%- else %}\n {%-... | {
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} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en", "ko"], "library_name": "transformers", "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-generation", "tags": ["KT", "K-intelligence", "Mi:dm"], "inference": true} | <p align="center">
<br>
<span style="font-size: 60px; font-weight: bold;">Mi:dm 2.0 Base</span>
</br>
</p>
<p align="center">
🤗 <a href="https://huggingface.co/collections/K-intelligence/mi-dm-20-6866406c301e5f45a6926af8">Mi:dm 2.0 Models</a> |
📜 <a href="https://github.com/K-intelligence-Midm/Midm-2.0/blob/main... | null | [
"mit"
] | null | [
"en",
"ko"
] | null | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
621ffdc136468d709f17def4 | 53ecf467267e2824 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 0 | microsoft/layoutlm-large-uncased | microsoft | null | 8,671 | 330,510 | False | 2022-03-02T23:29:05Z | 2021-08-11T05:28:26Z | transformers | 10 | 0 | null | null | null | [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tf_model.h5",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | 1e7d50dced3cdfea3a3d63c610e2aab36933dbef | [
"transformers",
"pytorch",
"tf",
"layoutlm",
"arxiv:1912.13318",
"endpoints_compatible",
"region:us"
] | null | {"model_type": "layoutlm", "tokenizer_config": {}} | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": null,
"processor": null
} | null | # LayoutLM
Multimodal (text + layout/format + image) pre-training for document AI
[Microsoft Document AI](https://www.microsoft.com/en-us/research/project/document-ai/) | [GitHub](https://aka.ms/layoutlm)
## Model description
LayoutLM is a simple but effective pre-training method of text and layout for document imag... | null | null | null | null | null | null | null | [
"layoutlm",
"AutoModel"
] | [
null
] | null | null | null |
64c1a5f3561398def76872d6 | 6da045b618e599f9 | 280bed8d033f4b3f | 1,024 | BPE | 827 | false | true | true | true | 4 | null | Sequence | ByteLevel | null | [
"Split",
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] | [
"ByteLevel"
] | 603 | akreal/tiny-random-BloomForCausalLM | akreal | null | 1,225 | 297,832 | False | 2023-07-26T23:02:11Z | 2023-03-29T18:37:17Z | transformers | 0 | 0 | null | text-generation | null | [
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"pytorch",
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] | null | {"architectures": ["BloomForCausalLM"], "model_type": "bloom", "tokenizer_config": {"bos_token": "<s>", "eos_token": "</s>", "pad_token": "<pad>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModelForCausalLM",
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"pipeline_tag": "text-generation",
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} | null | null | null | null | null | null | null | null | null | [
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] | [
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"text"
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] | 42 | nickypro/tinyllama-15M | nickypro | null | 2,823 | 332,142 | False | 2023-09-16T12:53:54Z | 2024-04-03T06:53:38Z | transformers | 15 | 0 | null | text-generation | {"parameters": {"F16": 15191712}, "total": 15191712} | [
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"license:mit",
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"region:us"
] | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": {"__type": "AddedToken", "content": "<s>", "lstrip": false, "normalized": true, "rstrip": false, "single_word": false}, "eos_token": {"__type": "AddedToken", "content": "</s>", "lstrip": false, "normalized": true, "rstrip":... | {
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These are converted from
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See the [llama2.c](https://github.com/karpathy/llama2.c) project for more details. | null | [
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<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
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<img src="https://raw.githubusercontent.com/aub-mind/arabert/master/AraGPT2.png" width="100" align="left"/>
You can find more information in our paper [AraGPT2](https://arxiv.org/abs/2012.15520)
The code in this repository was used to train all GPT2 variants. The code support training and fine-tuning G... | null | null | null | [
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Meet LeoLM, the first open and commercially available German Foundation Language Model built on Llama-2.
Our models extend Llama-2's capabilities into German through continued pretraining on a large corpus of German-language and mostly loca... | null | null | [
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*Dynamic 4-bit: Unsloth's [Dynamic 4-bit Quants](https://unsloth.ai/blog/dynamic-4bit) selectively avoids quantizing certain parame... | null | [
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"processor": "AutoTokenizer"
} | {"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. -->
## 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 | 1,235,814,400 | null | null | [
"modeling_ilama.IlamaModel",
"llama",
"AutoModel",
"IlamaModel"
] | [
"feature-extraction"
] | [
"multimodal"
] | [
"text"
] | [
"embeddings"
] |
6461e4fe805bf4012704e3a4 | 1518b5b91df51a3a | 06c049f6287d7099 | 52,000 | BPE | 51,760 | true | true | true | true | 2 | NFKC | ByteLevel | ByteLevel | [
"NFKC"
] | [
"ByteLevel"
] | [
"ByteLevel"
] | 604 | cyberagent/open-calm-7b | cyberagent | null | 944 | 302,476 | False | 2023-05-15T07:53:34Z | 2023-05-18T01:12:08Z | transformers | 205 | 0 | null | text-generation | null | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
"pytorch_model-00001-of-00002.bin",
"pytorch_model-00002-of-00002.bin",
"pytorch_model.bin.index.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | 276a5fb67510554e11ef191a2da44c919acccdf5 | [
"transformers",
"pytorch",
"gpt_neox",
"text-generation",
"japanese",
"causal-lm",
"ja",
"dataset:wikipedia",
"dataset:cc100",
"dataset:mc4",
"license:cc-by-sa-4.0",
"text-generation-inference",
"deploy:azure",
"region:us"
] | null | {"architectures": ["GPTNeoXForCausalLM"], "model_type": "gpt_neox", "tokenizer_config": {"bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "pad_token": "<|padding|>", "unk_token": "<|endoftext|>"}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["wikipedia", "cc100", "mc4"], "eval_results": null, "language": ["ja"], "library_name": null, "license": "cc-by-sa-4.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["japanese", "causal-lm"], "inference": false} | # OpenCALM-7B
## Model Description
OpenCALM is a suite of decoder-only language models pre-trained on Japanese datasets, developed by CyberAgent, Inc.
## Usage
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("cyberagent/open-calm-7b",... | null | [
"cc-by-sa-4.0"
] | [
"wikipedia",
"cc100",
"mc4"
] | [
"ja"
] | null | null | null | [
"gpt_neox",
"AutoModelForCausalLM",
"GPTNeoXForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
621ffdc036468d709f17618d | 191e06b04083279c | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 26 | Jorgeutd/bert-large-uncased-finetuned-ner | Jorgeutd | {
"models": [
{
"_id": "621ffdc036468d709f17433e",
"id": "google-bert/bert-large-uncased"
}
],
"relation": "finetune"
} | 26 | 293,052 | False | 2022-03-02T23:29:04Z | 2023-11-06T14:28:33Z | transformers | 6 | 0 | [{"name": "bert-large-uncased-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "conll2003", "args": "conll2003"}, "metrics": [{"type": "precision", "value": 0.9504719600222099, "name": "Precision", "verified": false}, {"type"... | token-classification | {"parameters": {"I64": 512, "F32": 334101513}, "total": 334102025} | [
".gitattributes",
".gitignore",
"README.md",
"config.json",
"model.safetensors",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"training_args.bin",
"vocab.txt"
] | 09c3f364791bb09caad1f92050c9ff0fd6e2a0c1 | [
"transformers",
"pytorch",
"safetensors",
"bert",
"token-classification",
"generated_from_trainer",
"en",
"dataset:conll2003",
"base_model:google-bert/bert-large-uncased",
"base_model:finetune:google-bert/bert-large-uncased",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"de... | 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": "bert-large-uncased", "datasets": ["conll2003"], "language": "en", "license": "apache-2.0", "metrics": ["precision", "recall", "f1", "accuracy"], "tags": ["generated_from_trainer"], "widget": [{"text": "My name is Scott and I live in Columbus."}, {"text": "My name is Scott and I am calling from Buffalo, ... | <!-- 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-large-uncased-finetuned-ner
This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-unc... | null | [
"apache-2.0"
] | [
"conll2003"
] | [
"en"
] | 334,102,025 | null | [
"precision",
"recall",
"f1",
"accuracy"
] | [
"AutoModelForTokenClassification",
"BertForTokenClassification",
"bert"
] | [
"token-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
657961e2e390cfd409cf3bb6 | 2d0341bd3a73bae6 | ef4806e2ade9022f | 32,100 | Unigram | null | true | true | true | true | 103 | Sequence | Metaspace | Metaspace | [
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 32 | zhiqiulin/clip-flant5-xl | zhiqiulin | {
"models": [
{
"_id": "6352be38507b679c3c5f2943",
"id": "google/flan-t5-xl"
}
],
"relation": "finetune"
} | 3,402 | 408,973 | False | 2023-12-13T07:48:50Z | 2024-10-06T20:29:57Z | transformers | 3 | 0 | null | text-generation | null | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"spiece.model",
"tokenizer.json",
"tokenizer_config.json",
"trainer_state.json",
"training_args.bin"
] | 3b4a6b1b618f4e286f5353b5b5147a3ae7d9ec55 | [
"transformers",
"pytorch",
"t5",
"text-generation",
"en",
"arxiv:2404.01291",
"base_model:google/flan-t5-xl",
"base_model:finetune:google/flan-t5-xl",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["CLIPT5ForConditionalGeneration"], "model_type": "t5", "tokenizer_config": {"eos_token": "</s>", "pad_token": "<pad>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModelForSeq2SeqLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": ["google/flan-t5-xl"], "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} | # CLIP-FlanT5-XL (VQAScore)
<!-- Provide a quick summary of what the model is/does. -->
This model is a fine-tuned version of google/flan-t5-xl designed for image-text retrieval tasks, as presented in the VQAScore paper.
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed b... | null | [
"apache-2.0"
] | null | [
"en"
] | null | null | null | [
"t5",
"CLIPT5ForConditionalGeneration",
"AutoModelForSeq2SeqLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
645392dbdc897edfd1586ce3 | 270541bf63f471b2 | 5f9f736c7dc6fbfa | 28,996 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 605 | ProdicusII/ZeroShotBioNER | ProdicusII | null | 889 | 293,642 | False | 2023-05-04T11:11:23Z | 2023-05-12T12:23:02Z | transformers | 7 | 0 | null | token-classification | null | [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | 731585bd9b2cab1b2be0fba11e38a9810d6a3fc1 | [
"transformers",
"pytorch",
"bert",
"token-classification",
"biology",
"medical",
"zero-shot",
"few-shot",
"en",
"dataset:bigbio/chemdner",
"dataset:ncbi_disease",
"dataset:jnlpba",
"dataset:bigbio/n2c2_2018_track2",
"dataset:bigbio/bc5cdr",
"arxiv:2305.04928",
"license:mit",
"endpoin... | null | {"architectures": ["BertForTokenClassification"], "model_type": "bert", "tokenizer_config": {"cls_token": "[CLS]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
"auto_model": "AutoModelForTokenClassification",
"custom_class": null,
"pipeline_tag": "token-classification",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["bigbio/chemdner", "ncbi_disease", "jnlpba", "bigbio/n2c2_2018_track2", "bigbio/bc5cdr"], "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "mit", "license_name": null, "license_link": null, "metrics": ["precision", "recall", "f1"], "model_name": null... | # Zero and few shot NER for biomedical texts
## Model description
This model was created during the research collaboration between Bayer Pharma and Serbian Institute for Artificial Intelligence Research and Development.
The model is trained on about 25+ biomedical NER classes and can perform also zero-shot inference... | null | [
"mit"
] | [
"bigbio/chemdner",
"ncbi_disease",
"jnlpba",
"bigbio/n2c2_2018_track2",
"bigbio/bc5cdr"
] | [
"en"
] | null | null | [
"precision",
"recall",
"f1"
] | [
"AutoModelForTokenClassification",
"BertForTokenClassification",
"bert"
] | [
"token-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
634e7485aae4bde2c8dd5543 | d551fd31cc33a2e0 | 1e2eefe464922984 | 458 | WordLevel | null | false | true | false | false | 430 | null | Split | null | null | [
"Split"
] | null | 606 | AI4PD/ZymCTRL | AI4PD | null | 8,530 | 309,633 | False | 2022-10-18T09:40:21Z | 2025-03-11T06:52:15Z | transformers | 26 | 0 | null | text-generation | null | [
".gitattributes",
"5.run_clm-post.py",
"README.md",
"config.json",
"github1.png",
"pytorch_model.bin",
"tokenizer.json",
"vocab.json"
] | 3c532ef172b9cd2e95238baadf5167ebb89fbc32 | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"biology",
"arxiv:1909.05858",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["GPT2LMHeadModel"], "model_type": "gpt2"} | {
"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": "text-generation", "tags": ["biology"], "widget": [{"text": "1.1.1.21<sep><start>"}], "inference"... | # **ZymCTRL**
ZymCTRL (Enzyme Control) ([ see preprint ](https://www.biorxiv.org/content/10.1101/2024.05.03.592223v1))
is a conditional language model for the generation of artificial functional enzymes.
It was trained on the UniProt database of sequences containing (Enzyme Commission) EC annotations, comprising ove... | null | [
"apache-2.0"
] | null | null | null | null | null | [
"GPT2LMHeadModel",
"AutoModelForCausalLM",
"gpt2"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
640244d417f1f2c38466d752 | 3d9bc37acbc06fc7 | 7619f8e118ffd696 | 57,544 | Unigram | null | true | true | true | true | 27 | Sequence | Metaspace | Metaspace | [
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 607 | to-be/donut-base-finetuned-invoices | to-be | null | 577 | 295,450 | False | 2023-03-03T19:04:52Z | 2023-03-03T19:18:45Z | transformers | 28 | 0 | null | image-to-text | null | [
".gitattributes",
"README.md",
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"pytorch_model.bin",
"sentencepiece.bpe.model",
"special_tokens_map.json",
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] | cb3d39882a06a7342e440070a2e50f27b06205ee | [
"transformers",
"pytorch",
"vision-encoder-decoder",
"image-text-to-text",
"donut",
"image-to-text",
"vision",
"invoices",
"arxiv:2111.15664",
"license:cc-by-nc-sa-4.0",
"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": null, "library_name": null, "license": "cc-by-nc-sa-4.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["donut", "image-to-text", "vision", "invoices"]} | # Donut finetuned on invoices
Based on Donut base model (introduced in the paper [OCR-free Document Understanding Transformer](https://arxiv.org/abs/2111.15664) by Geewok et al. and first released in [this repository](https://github.com/clovaai/donut).
The model was trained with a few thousand of annotated invoices ... | null | [
"cc-by-nc-sa-4.0"
] | null | null | null | null | null | [
"AutoModelForImageTextToText",
"vision-encoder-decoder",
"VisionEncoderDecoderModel"
] | [
"image-text-to-text",
"image-to-text"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"text"
] |
661cd90df640202499887d75 | bcedfbbaedf61cc5 | 94507c3d22ac2826 | 60,516 | BPE | 39,382 | true | true | true | false | 5 | Sequence | Whitespace | null | [
"NFC",
"Lowercase"
] | [
"Whitespace"
] | null | 334 | jinaai/jina-reranker-v1-tiny-en | jinaai | null | 4,205 | 311,875 | False | 2024-04-15T07:36:45Z | 2025-09-10T09:51:37Z | transformers | 25 | 0 | null | text-ranking | {"parameters": {"BF16": 33046273}, "total": 33046273} | [
".gitattributes",
"README.md",
"config.json",
"configuration_bert.py",
"merges.txt",
"model.safetensors",
"modeling_bert.py",
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"... | null | {"architectures": ["JinaBertModel"], "auto_map": {"AutoConfig": "configuration_bert.JinaBertConfig", "AutoModel": "modeling_bert.JinaBertModel", "AutoModelForMaskedLM": "modeling_bert.JinaBertForMaskedLM", "AutoModelForQuestionAnswering": "modeling_bert.JinaBertForQuestionAnswering", "AutoModelForSequenceClassification... | {
"auto_model": "AutoModel",
"custom_class": "modeling_bert.JinaBertModel",
"pipeline_tag": "feature-extraction",
"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": "text-ranking", "tags": ["reranker", "cross-encoder", "transformers.js", "sentence-tr... | <br><br>
<p align="center">
<img src="https://huggingface.co/datasets/jinaai/documentation-images/resolve/main/logo.webp" alt="Jina AI: Your Search Foundation, Supercharged!" width="150px">
</p>
<p align="center">
<b>Trained by <a href="https://jina.ai/"><b>Jina AI</b></a>.</b>
</p>
# jina-reranker-v1-tiny-en
This ... | null | [
"apache-2.0"
] | null | [
"en"
] | 33,046,273 | null | null | [
"JinaBertModel",
"AutoModel",
"bert",
"modeling_bert.JinaBertModel"
] | [
"feature-extraction",
"text-ranking"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
65c09987c084467acac0553f | fbd395e40dc9abcc | f540d0cefa06a5d9 | 100,002 | BPE | 99,757 | true | true | true | true | 2 | Sequence | Sequence | ByteLevel | [] | [
"Split",
"Split",
"Split",
"Split",
"Split",
"Digits",
"ByteLevel"
] | [
"ByteLevel"
] | 236 | deepseek-ai/deepseek-math-7b-rl | deepseek-ai | null | 2,552 | 301,869 | False | 2024-02-05T08:17:11Z | 2024-03-19T03:54:22Z | transformers | 91 | 0 | null | text-generation | {"parameters": {"BF16": 6910365696}, "total": 6910365696} | [
".gitattributes",
"LICENSE",
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... | f3cd419a172ff5a100e9a563ebfe7900d78e9740 | [
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"arxiv:2402.03300",
"license:other",
"text-generation-inference",
"endpoints_compatible",
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] | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": {"__type": "AddedToken", "content": "<\uff5cbegin\u2581of\u2581sentence\uff5c>", "lstrip": false, "normalized": true, "rstrip": false, "single_word": false}, "eos_token": {"__type": "AddedToken", "content": "<\uff5cend\u258... | {
"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": "other", "license_name": "deepseek", "license_link": "https://github.com/deepseek-ai/DeepSeek-Math/blob/main/LICENSE-MODEL", "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | <p align="center">
<img width="500px" alt="DeepSeek Chat" src="https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/images/logo.png?raw=true">
</p>
<p align="center"><a href="https://www.deepseek.com/">[🏠Homepage]</a> | <a href="https://chat.deepseek.com/">[🤖 Chat with DeepSeek LLM]</a> | <a href="https://discor... | null | [
"other",
"deepseek",
"https://github.com/deepseek-ai/DeepSeek-Math/blob/main/LICENSE-MODEL"
] | null | null | 6,910,365,696 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
64a0d785490dcccc480dc232 | 85c4a0556826f65f | 244621b26e3d0cd3 | 256,300 | Unigram | null | true | true | true | true | 304 | Sequence | Metaspace | Metaspace | [
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 608 | google/umt5-small | google | null | 3,508 | 297,523 | False | 2023-07-02T01:48:53Z | 2023-07-06T02:31:38Z | transformers | 24 | 0 | null | null | null | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"spiece.model",
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"tokenizer_config.json"
] | 8c63c2b77efbf8e41206a2c8d994846cc9392360 | [
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"multilingual",
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"be",
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"bn",
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"cy",
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Meta-Llama-3.1-8B-Instruct-quantized.w8a8
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StreetCLIP is a robust foundation model for open-domain image geolocalization and other
geographic and climate-related tasks.
Trained on an original dataset of 1.1 million street-level urban and rural geo-tagged images, it achieves
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With this model, you can classify emotions in English text data. The model was trained on 6 diverse datasets and predicts Ekman's 6 basic emotions, plus a neutral class:
1) anger 🤬
2) disgust 🤢
3) fear 😨
4) joy 😀
5) neutral 😐
6) sadness 😭
7) surprise 😲
The model is a fine-tuned checkpoint of ... | null | null | null | [
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## Introduction
Qwen2.5-Math-RM-72B is specifically designed to guide the Qwen2.5-Math model throughout the training process by offering more granular feedback on the quality of reasoning and intermediate steps, ultimately facilitating more robust model improvements.
Key Highlights:
- Multilin... | null | [
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should probably proofread and complete it, then remove this comment. -->
<p align="center" width="100%">
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## Introduction
In addition to the mathematical Outcome Reward Model (ORM) Qwen2.5-Math-RM-72B, we release the Process Reward Model (PRM), namely Qwen2.5-Math-PRM-7B and Qwen2.5-Math-PRM-72B. PRMs emerge as a promising approach for process supervision in mathematical reasoning of Large Language ... | null | [
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"text-generation",
"conversational",
"arxiv:1910.09700",
"text-generation-inference",
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] | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": "<s>", "chat_template": "{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% elif USE_DEFAULT_PROMPT == true and not '<<SYS>>' in messages[0]['co... | {
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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. -->
## 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 | 1,062,992 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
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"text-generation"
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"text"
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"text"
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6569089395ab5c3a595ed769 | 4380129efbbc86ec | dfd094bbcec644b8 | 32,005 | BPE | 61,249 | true | false | true | true | 8 | Sequence | null | Sequence | [
"Prepend",
"Replace"
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"Replace",
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"models": [
{
"_id": "654bb14be1671abcbc25521b",
"id": "epfl-llm/meditron-7b"
}
],
"relation": "quantized"
} | 8,151 | 299,505 | False | 2023-11-30T22:11:31Z | 2023-11-30T22:26:18Z | transformers | 4 | 0 | null | text-generation | {"parameters": {"I32": 6476005376, "F16": 262410240}, "total": 6738415616} | [
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] | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "quantization_config": {"bits": 4, "quant_method": "awq"}, "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_prom... | {
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"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "epfl-llm/meditron-7b", "datasets": ["epfl-llm/guidelines"], "eval_results": null, "language": ["en"], "library_name": null, "license": "llama2", "license_name": null, "license_link": null, "metrics": ["accuracy", "perplexity"], "model_name": "Meditron 7B", "pipeline_tag": null, "tags": null, "inference"... | <!-- markdownlint-disable MD041 -->
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content:... | null | [
"llama2"
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"epfl-llm/guidelines"
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"en"
] | 6,738,415,616 | null | [
"accuracy",
"perplexity"
] | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
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67572da7bb157414865cbaf8 | 1e0a2493163c0c63 | 6622292dca275a2d | 128,256 | BPE | 280,147 | false | true | true | true | 256 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 18 | Infermatic/Llama-3.3-70B-Instruct-FP8-Dynamic | Infermatic | {
"models": [
{
"_id": "66944fd095c7fa6e68c314ae",
"id": "meta-llama/Llama-3.1-70B"
}
],
"relation": "quantized"
} | 846 | 294,287 | False | 2024-12-09T17:49:27Z | 2024-12-09T18:36:58Z | transformers | 2 | 0 | null | text-generation | null | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
"model-00001-of-00015.safetensors",
"model-00002-of-00015.safetensors",
"model-00003-of-00015.safetensors",
"model-00004-of-00015.safetensors",
"model-00005-of-00015.safetensors",
"model-00006-of-00015.safetensors",
"model-0... | a13bfed30b62041083fce267cb4089a05e1c2d1c | [
"transformers",
"safetensors",
"llama",
"text-generation",
"facebook",
"meta",
"pytorch",
"llama-3",
"conversational",
"en",
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"pt",
"hi",
"es",
"th",
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"arxiv:2204.05149",
"base_model:meta-llama/Llama-3.1-70B",
"base_model:quantized:meta-llama/Llama-3.1-70B",
"... | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "quantization_config": {"quant_method": "compressed-tensors"}, "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_... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": ["meta-llama/Llama-3.1-70B"], "datasets": null, "eval_results": null, "language": ["en", "fr", "it", "pt", "hi", "es", "th", "de"], "library_name": "transformers", "license": "llama3.3", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["faceb... | # This quant was made for and by [Infermatic.ai](https://infermatic.ai/)
[meta-llama/Llama-3.3-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct)
Copy of the original card
## Model Information
The Meta Llama 3.3 multilingual large language model (LLM) is a pretrained and instruction tuned gener... | null | [
"llama3.3"
] | null | [
"en",
"fr",
"it",
"pt",
"hi",
"es",
"th",
"de"
] | null | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
621ffdc036468d709f1743fd | aed290a0124eb522 | dd84a2b1ff97657d | 250,002 | Unigram | null | true | true | true | true | 5 | Precompiled | Sequence | Metaspace | [
"Precompiled"
] | [
"WhitespaceSplit",
"Metaspace"
] | [
"Metaspace"
] | 10 | AIDA-UPM/mstsb-paraphrase-multilingual-mpnet-base-v2 | AIDA-UPM | null | 1,491 | 292,953 | False | 2022-03-02T23:29:04Z | 2021-07-13T14:12:45Z | transformers | 12 | 0 | null | sentence-similarity | null | [
".gitattributes",
"1_Pooling/config.json",
"README.md",
"config.json",
"config_sentence_transformers.json",
"modules.json",
"pytorch_model.bin",
"sentence_bert_config.json",
"sentencepiece.bpe.model",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | 7c1614a46aa544de7d3bfecf05de0734cc72d0ed | [
"transformers",
"pytorch",
"xlm-roberta",
"feature-extraction",
"sentence-similarity",
"multilingual",
"text-embeddings-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["XLMRobertaModel"], "model_type": "xlm-roberta", "tokenizer_config": {"bos_token": "<s>", "eos_token": "</s>", "sep_token": "</s>", "cls_token": "<s>", "unk_token": "<unk>", "pad_token": "<pad>", "mask_token": {"content": "<mask>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": ... | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": "multilingual", "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "sentence-similarity", "tags": ["feature-extraction", "sentence-similarity", "transformers", "m... | # mstsb-paraphrase-multilingual-mpnet-base-v2
This is a fine-tuned version of `paraphrase-multilingual-mpnet-base-v2` from [sentence-transformers](https://www.SBERT.net) model with [Semantic Textual Similarity Benchmark](http://ixa2.si.ehu.eus/stswiki/index.php/Main_Page) extended to 15 languages: It maps sentences & ... | null | null | null | [
"multilingual"
] | null | null | null | [
"XLMRobertaModel",
"AutoModel",
"xlm-roberta"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
662ec08234f94a2b824e715b | 39b1e434a495263c | a1f1bd381dcb9329 | 128,256 | BPE | 280,147 | false | true | true | true | 256 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 349 | nvidia/Llama3-ChatQA-1.5-8B | nvidia | null | 11,083 | 337,271 | False | 2024-04-28T21:32:50Z | 2024-05-24T17:28:49Z | transformers | 555 | 0 | null | text-generation | null | [
".gitattributes",
"README.md",
"config.json",
"docs/docs.json",
"generation_config.json",
"model-00001-of-00002.safetensors",
"model-00002-of-00002.safetensors",
"model.safetensors.index.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | 3b98162e3f97550d62aeeb19ea50208f968c678a | [
"transformers",
"safetensors",
"llama",
"text-generation",
"nvidia",
"chatqa-1.5",
"chatqa",
"llama-3",
"pytorch",
"conversational",
"en",
"arxiv:2401.10225",
"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": "{{ bos_token }}{%- if messages[0]['role'] == 'system' -%}{% set loop_messages = messages[1:] %}{%- else -%}{% set loop_messages = messages %}{% endif %}System: This is a chat between a... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en"], "library_name": null, "license": "llama3", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-generation", "tags": ["nvidia", "chatqa-1.5", "chatqa", "llama-3", "pytorch"]} | ## Model Details
We introduce Llama3-ChatQA-1.5, which excels at conversational question answering (QA) and retrieval-augmented generation (RAG). Llama3-ChatQA-1.5 is developed using an improved training recipe from [ChatQA paper](https://arxiv.org/pdf/2401.10225), and it is built on top of [Llama-3 base model](https:/... | null | [
"llama3"
] | null | [
"en"
] | null | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
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