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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_vicgalle__CarbonBeagle-11B-truthy)
| Metric |Value|
|---------------------------------... | null | [
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## Model description
CodeT5 is a family of encoder-decoder language models for code from the paper: [CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation](https://arxiv.org/pdf/2109.00859.pdf) by Yue Wang, Weishi Wang, Shafiq Joty, ... | null | [
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<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-... | null | [
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This model is a fine-tuned version of [microsoft/codebert-base](https://huggingface.co/microsoft/codebert-base).
It achieves the following results on the evaluation set:
- Loss: 0.8225
- Accuracy: 0.7279
- Weighted f1: 0.6508
- Micro f1: 0.7279
- Macro f1: 0.4611
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There is a post... | null | [
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} | {"base_model": ["Qwen/Qwen2.5-VL-7B-Instruct"], "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "image-text-to-text", "tags": ["multimodal", "unsloth"]} | # Qwen2.5-VL-7B-Instruct
<a href="https://chat.qwenlm.ai/" target="_blank" style="margin: 2px;">
<img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;"/>
</a>
## Introduction
In the past five months since Qwen2-VL’s r... | null | [
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> Update @ 2024.05.01: Pre-Release [Llama-3-KoEn-8B model](https://huggingface.co/beomi/Llama-3-KoEn-8B-preview) & [Llama-3-KoEn-8B-Instruct-preview](https://huggingface.co/beomi/Llama-3-KoEn-8B-Instruct-preview)
## Model Details
**Llama-3-KoEn-8B-Instruct-preview**
Llama-3-KoEn-... | null | [
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# About TexTeller
* 📮[2024-03-25] TexTeller 2.0 released! The training data for TexTeller 2.0 has been increased to 7.5M (about **15 times more** than TexTeller 1.0 and also improved in data quality). The trained TexTeller 2.0 demonstrated **superior performance** in the test set, especially i... | null | [
"apache-2.0"
] | [
"OleehyO/latex-formulas"
] | null | 298,101,248 | null | [
"bleu"
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"VisionEncoderDecoderModel"
] | [
"image-text-to-text",
"image-to-text"
] | [
"multimodal"
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"text",
"image"
] | [
"text"
] |
66871bbcc6545fe83e494bc5 | b0bafdb82bee9acf | 87c4d05625dfad98 | 128,001 | Unigram | null | true | true | true | true | 5 | Sequence | Sequence | Metaspace | [
"Strip",
"Precompiled",
"Replace"
] | [
"Metaspace"
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"Metaspace"
] | 649 | PORTULAN/serafim-900m-portuguese-pt-sentence-encoder-ir | PORTULAN | null | 88,291 | 343,649 | False | 2024-07-04T22:01:32Z | 2025-01-01T16:52:29Z | sentence-transformers | 5 | 0 | null | sentence-similarity | {"parameters": {"F32": 884593152}, "total": 884593152} | [
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This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1536 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here ... | null | [
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6753a1ebf49782992e8c0d6e | eac92e6e1f3524c5 | d3be3a2f0795da90 | 49,154 | BPE | 48,900 | false | true | false | true | 19 | null | Sequence | ByteLevel | null | [
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],
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} | 359,619 | 840,681 | False | 2024-12-07T01:16:27Z | 2026-02-11T15:04:40Z | transformers | 91 | 1 | null | text-classification | {"parameters": {"F32": 134516160}, "total": 134516160} | [
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<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated... | null | [
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64fbc64b8c21ebb3db8a0b72 | 53ecf467267e2824 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
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"endpoints_compatible",
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] | null | {"architectures": ["DistilBertForSequenceClassification"], "model_type": "distilbert", "tokenizer_config": {"cls_token": "[CLS]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
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---
# distilbert-base-uncased-go-emotions-student
## Model Description
This model is distilled from the zero-shot classification pipeline o... | null | [
"mit"
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"DistilBertForSequenceClassification",
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662752922024526699b6758d | 39b1e434a495263c | a1f1bd381dcb9329 | 128,256 | BPE | 280,147 | false | true | true | true | 256 | null | Sequence | ByteLevel | null | [
"Split",
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}
],
"relation": "finetune"
} | 7,935 | 296,214 | False | 2024-04-23T06:17:54Z | 2026-01-26T08:13:54Z | transformers | 4 | 0 | null | text-generation | {"parameters": {"BF16": 8030261248}, "total": 8030261248} | [
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} | {"base_model": ["meta-llama/Meta-Llama-3-8B"], "datasets": null, "eval_results": null, "language": ["en", "zh"], "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["medical"]} | ## WiNGPT2
[WiNGPT](https://github.com/winninghealth/WiNGPT2) 是一个基于GPT的医疗垂直领域大模型，旨在将专业的医学知识、医疗信息、数据融会贯通，为医疗行业提供智能化的医疗问答、诊断支持和医学知识等信息服务，提高诊疗效率和医疗服务质量。
## 更新日志
[2024/04/23] 更新 WiNGPT2-Llama-3-8B-Base 和 WiNGPT2-Llama-3-8B-Chat 模型（中文增强/多语言）与测评结果
[2024/04/01] 更新 WiNEval 测评结果
[2024/03/05] 开源7B/14B-Chat-4bit模型权重: [🤗](ht... | null | [
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Authors: [Ashvini Kumar Jindal](https://www.linkedin.com/in/ashvini-jindal-26653262/), [Pawan Kumar Rajpoot](https://www.linkedin.com/in/pawanrajpoot/), [Ankur Parikh](https://www.linkedin.com/in/an... | null | [
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"... | null | {"architectures": ["Qwen2ForCausalLM"], "model_type": "qwen2", "quantization_config": {"bits": 4, "quant_method": "awq"}, "tokenizer_config": {"bos_token": null, "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n ... | {
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## Introduction
Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters. Qwen2.5 brings the following improvements upon Qwen2:
- Significantly **more ... | null | [
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This is a 164M parameters model with the same architecture as [StarCoder](https://huggingface.co/bigcode/starcoder) (8k context length, MQA & FIM). It was trained on the Python data from [StarCoderData](https://huggingface.co/datasets/bigcode/starcoderdata)
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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.
<!--- Describe your model here ... | null | [
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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 | 124,665,626 | null | null | [
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"pytorch",
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"license:apache-2.0",
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] | null | {"architectures": ["GPTNeoXRewardModel"], "model_type": "gpt_neox_reward_model", "tokenizer_config": {"bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "unk_token": "<|endoftext|>"}} | {
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- base model: [andreaskoepf/pythia-6.9b-gpt4all-pretrain](https://huggingface.co/andreaskoepf/pythia-6.9b-gpt4all-pretrain)
- wandb: https://wandb.ai/open-assistant/reward-model/runs/5xld9wmd
- checkpoint: 3500 steps
Compute was generously provided by [Stability AI](https://stability.... | null | [
"apache-2.0"
] | null | null | null | null | null | [
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null
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"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
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] | null | {"architectures": ["GPT2LMHeadModel"], "model_type": "gpt2"} | {
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GPT-2 is a transformers model pretrained on a very large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic proce... | null | [
"other"
] | null | null | 137,022,720 | null | null | [
"GPT2LMHeadModel",
"AutoModelForCausalLM",
"gpt2"
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"models": [
{
"_id": "6698d8a0653e4babe21e1e7d",
"id": "meta-llama/Llama-3.1-8B-Instruct"
}
],
"relation": "quantized"
} | 22,927 | 659,603 | False | 2025-02-15T10:54:04Z | 2025-02-15T23:02:14Z | transformers | 4 | 0 | null | text-generation | {"parameters": {"F32": 475344, "BF16": 1227100160, "U8": 7015836210}, "total": 8243411714} | [
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] | 3f09f44543858745a6af68161eded01dbcc536ad | [
"transformers",
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"llama-3",
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"facebook",
"unsloth",
"conversational",
"en",
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"base_model:quantized:meta-llama/Llama-3.1-8B-Instruct",
"license:llama3.1",
"text-generation-in... | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "quantization_config": {"load_in_4bit": true, "load_in_8bit": false, "quant_method": "bitsandbytes"}, "tokenizer_config": {"bos_token": "<|begin_of_text|>", "chat_template": "{{- bos_token }}\n{%- if custom_tools is defined %}\n {%- set tools = custom_to... | {
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<p style="margin-bottom: 0; margin-top: 0;">
<strong>See <a href="https://huggingface.co/collections/unsloth/llama-31-collection-6753dca76f47d9ce1696495f">our collection</a> for versions of Llama 3.1 including 4-bit + 16b-bit formats.</strong>
</p>
<div style="display: flex; gap: 5px; align-items: cente... | null | [
"llama3.1"
] | null | [
"en"
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661dbb1ed8427174a87fbc67 | 6a97a9e193c41c6e | d965e4a9140ecf72 | 21,128 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
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"BertPreTokenizer"
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"WordPiece"
] | 653 | Mike0307/text2vec-base-chinese-rag | Mike0307 | null | 972 | 255,399 | False | 2024-04-15T23:41:18Z | 2024-06-09T07:32:25Z | transformers | 10 | 0 | null | sentence-similarity | {"parameters": {"F32": 102267648}, "total": 102267648} | [
".gitattributes",
"README.md",
"config.json",
"model.safetensors",
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"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | 588f2f7fa60ee4151031ec07b2587a5f843c908b | [
"transformers",
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"bert",
"feature-extraction",
"sentence-similarity",
"license:apache-2.0",
"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",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"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": ["spearmanr"], "model_name": null, "pipeline_tag": "sentence-similarity", "tags": null} | ## Overview
This model is primarily designed for language understanding between Chinese texts.<br>
It utilizes the **CoSENT** training framework for the purpose of the **Retrieval-Augmented Generation(RAG)** task.
## Download the model
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokeni... | null | [
"apache-2.0"
] | null | null | 102,267,648 | null | [
"spearmanr"
] | [
"BertModel",
"AutoModel",
"bert"
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"sentence-similarity",
"feature-extraction"
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"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
6605f5733b0163cbeefb2ea1 | bfe694f64f4a18f5 | daab2a732bab72e2 | 128,001 | Unigram | null | true | true | true | true | 5 | Sequence | Sequence | Metaspace | [
"Strip",
"Precompiled",
"Replace"
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"Metaspace"
] | [
"Metaspace"
] | 22 | MoritzLaurer/deberta-v3-base-zeroshot-v2.0 | MoritzLaurer | {
"models": [
{
"_id": "621ffdc136468d709f17dee4",
"id": "microsoft/deberta-v3-base"
}
],
"relation": "quantized"
} | 43,366 | 373,518 | False | 2024-03-28T22:55:47Z | 2024-04-11T13:42:07Z | transformers | 12 | 1 | null | zero-shot-classification | {"parameters": {"F16": 184423682}, "total": 184423682} | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"model.safetensors",
"onnx/added_tokens.json",
"onnx/config.json",
"onnx/model.onnx",
"onnx/special_tokens_map.json",
"onnx/spm.model",
"onnx/tokenizer.json",
"onnx/tokenizer_config.json",
"special_tokens_map.json",
"spm.mo... | 8e7e5af5983a0ddb1a5b45a38b129ab69e2258e8 | [
"transformers",
"onnx",
"safetensors",
"deberta-v2",
"text-classification",
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"en",
"arxiv:2312.17543",
"base_model:microsoft/deberta-v3-base",
"base_model:quantized:microsoft/deberta-v3-base",
"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": "microsoft/deberta-v3-base", "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "zero-shot-classification", "tags": ["text-classification", "zero-sho... | # Model description: deberta-v3-base-zeroshot-v2.0
## zeroshot-v2.0 series of models
Models in this series are designed for efficient zeroshot classification with the Hugging Face pipeline.
These models can do classification without training data and run on both GPUs and CPUs.
An overview of the latest zeroshot cla... | null | [
"mit"
] | null | [
"en"
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interpretability research [(see paper)](https://arxiv.org/pdf/2304.01373.pdf).
It contains two sets of eight models of sizes
70M, 160M, 410M, 1B, 1.4B, 2.8B, 6.9B, and 12B. For each size, there are two
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] | null | {"architectures": ["LongT5ForConditionalGeneration"], "model_type": "longt5"} | {
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LongT5 model pre-trained on English language. The model was introduced in the paper [LongT5: Efficient Text-To-Text Transformer for Long Sequences](https://arxiv.org/pdf/2112.07916.pdf) by Guo et al. and first released in [the LongT5 repository](https://github.com/google-re... | null | [
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<img alt="gpt-oss-120b" src="https://raw.githubusercontent.com/openai/gpt-oss/main/docs/gpt-oss-120b.svg">
</p>
<p align="center">
<a href="https://gpt-oss.com"><strong>Try gpt-oss</strong></a> ·
<a href="https://cookbook.openai.com/topic/gpt-oss"><strong>Guides</strong></a> ·
<a href="https... | null | [
"apache-2.0"
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"models": [
{
"_id": "661d224c9945e0962d45fb97",
"id": "microsoft/WizardLM-2-8x22B"
}
],
"relation": "quantized"
} | 10 | 250,083 | False | 2024-04-16T11:06:56Z | 2025-02-25T04:27:46Z | transformers | 13 | 0 | null | text-generation | {"parameters": {"I32": 140223971328, "F16": 396662784}, "total": 140620634112} | [
".gitattributes",
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"model-0... | c7c31d3cb26127de433111dfa3d2962df5d06ecf | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
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"quantized",
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"AWQ",
"conversational",
"base_model:microsoft/WizardLM-2-8x22B",
"base_model:quantized:microsoft/WizardLM-2-8x22B",
"text-generation-inference",
"awq",
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] | null | {"architectures": ["MixtralForCausalLM"], "model_type": "mixtral", "quantization_config": {"bits": 4, "quant_method": "awq"}, "tokenizer_config": {"bos_token": "<s>", "chat_template": "{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{{ messages[0]['content'].strip() }}{% else %}{% set loop... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
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} | {"base_model": "microsoft/WizardLM-2-8x22B", "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": "WizardLM-2-8x22B-AWQ", "pipeline_tag": "text-generation", "tags": ["finetuned", "quantized", "4-bit", ... | # Description
[MaziyarPanahi/home-AWQ](https://huggingface.co/MaziyarPanahi/home-AWQ) is a quantized (AWQ) version of [microsoft/WizardLM-2-8x22B](https://huggingface.co/microsoft/WizardLM-2-8x22B)
## How to use
### Install the necessary packages
```
pip install --upgrade accelerate autoawq transformers
```
### Exam... | null | null | null | null | 140,620,634,112 | null | null | [
"mixtral",
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"id": "meta-llama/Meta-Llama-3-70B"
}
],
"relation": "finetune"
} | 1,439 | 267,051 | False | 2024-06-04T12:20:49Z | 2025-04-20T02:17:28Z | transformers | 86 | 1 | null | text-generation | {"parameters": {"BF16": 8030277632}, "total": 8030277632} | [
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"dataset:EleutherAI/proof-pile-2",
"dataset:meta-... | {"total": 2795443200, "architecture": "stablelm", "context_length": 16384, "bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>"} | {"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",
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Please note: For commercial use, please refer to https://stability.ai/license.
## Model Description
`stable-code-3b` is a 2.7B billion parameter decoder-only language model pre-trained on 1.3 trillion tokens of diverse textual and code datasets. `stable-code-3b` is trained on 18 programming langua... | null | [
"other"
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"base_model:Qwen/Qwen2-VL-7B-Instruct",
"base_model:finetune:Qwen/Qwen2-VL-7B-Instruct",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["Qwen2VLForConditionalGeneration"], "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'] != 'system' %}<|im_start|>system\nYou are a helpfu... | {
"auto_model": "AutoModelForImageTextToText",
"custom_class": null,
"pipeline_tag": "image-text-to-text",
"processor": "AutoProcessor"
} | {"base_model": ["Qwen/Qwen2-VL-7B-Instruct"], "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "image-to-text", "tags": null} | # Qwen2-VL-7B-Captioner-Relaxed
## Introduction
Qwen2-VL-7B-Captioner-Relaxed is an instruction-tuned version of [Qwen2-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct), an advanced multimodal large language model. This fine-tuned version is based on a hand-curated dataset for text-to-image models, p... | null | [
"apache-2.0"
] | null | [
"en"
] | 8,291,375,616 | null | null | [
"AutoModelForImageTextToText",
"qwen2_vl",
"Qwen2VLForConditionalGeneration"
] | [
"image-text-to-text",
"image-to-text"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"text"
] |
621ffdc136468d709f17a221 | 35f8a93a304f6b78 | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 6 | cross-encoder/quora-roberta-base | cross-encoder | {
"models": [
{
"_id": "621ffdc036468d709f174350",
"id": "FacebookAI/roberta-base"
}
],
"relation": "quantized"
} | 13,738 | 299,690 | False | 2022-03-02T23:29:05Z | 2025-04-11T08:44:19Z | sentence-transformers | 1 | 0 | null | text-ranking | {"parameters": {"I64": 514, "F32": 124646401}, "total": 124646915} | [
".gitattributes",
"CEBinaryClassificationEvaluator_Quora-dev_results.csv",
"README.md",
"config.json",
"flax_model.msgpack",
"merges.txt",
"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.o... | 86e2599701ff4f66fba25493db9602f884f73f16 | [
"sentence-transformers",
"pytorch",
"jax",
"onnx",
"safetensors",
"openvino",
"roberta",
"text-classification",
"transformers",
"text-ranking",
"en",
"dataset:sentence-transformers/quora-duplicates",
"base_model:FacebookAI/roberta-base",
"base_model:quantized:FacebookAI/roberta-base",
"l... | null | {"architectures": ["RobertaForSequenceClassification"], "model_type": "roberta", "tokenizer_config": {"bos_token": "<s>", "cls_token": "<s>", "eos_token": "</s>", "mask_token": "<mask>", "pad_token": "<pad>", "sep_token": "</s>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"base_model": ["FacebookAI/roberta-base"], "datasets": ["sentence-transformers/quora-duplicates"], "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",... | null | null | [
"apache-2.0"
] | [
"sentence-transformers/quora-duplicates"
] | [
"en"
] | 124,646,915 | null | null | [
"roberta",
"AutoModelForSequenceClassification",
"RobertaForSequenceClassification"
] | [
"text-ranking",
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
636a445437d9329b4a00d168 | d2fa5f0f83f56ae9 | 8a01c02d4d0a401c | 119,547 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 657 | NbAiLab/nb-sbert-base | NbAiLab | null | 67,276 | 350,865 | False | 2022-11-08T11:58:12Z | 2024-10-24T12:33:35Z | sentence-transformers | 23 | 0 | null | sentence-similarity | {"parameters": {"I64": 512, "F32": 177853440}, "total": 177853952} | [
".gitattributes",
"1_Pooling/config.json",
"README.md",
"config.json",
"config_sentence_transformers.json",
"eval/similarity_evaluation_sts-dev_results.csv",
"model.safetensors",
"modules.json",
"pytorch_model.bin",
"sentence_bert_config.json",
"similarity_evaluation_sts-test_results.csv",
"sp... | 26567595914b5f4b04ec871b5814db989ca261b9 | [
"sentence-transformers",
"pytorch",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"no",
"dataset:NbAiLab/mnli-norwegian",
"license:apache-2.0",
"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",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": "NbAiLab/mnli-norwegian", "eval_results": null, "language": ["no"], "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "sentence-similarity", "tags": ["sentence-transformers", "feature-extractio... | # NB-SBERT-BASE
NB-SBERT-BASE is a [SentenceTransformers](https://www.SBERT.net) model trained on a [machine translated version of the MNLI dataset](https://huggingface.co/datasets/NbAiLab/mnli-norwegian), starting from [nb-bert-base](https://huggingface.co/NbAiLab/nb-bert-base).
The model maps sentences & paragraphs... | null | [
"apache-2.0"
] | [
"NbAiLab/mnli-norwegian"
] | [
"no"
] | 177,853,952 | null | null | [
"BertModel",
"AutoModel",
"bert"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
66f46c0ec2dc7d75299fd55d | 5b8a6156b5b6e013 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 37 | Marqo/dunzhang-stella_en_400M_v5 | Marqo | null | 171 | 249,714 | False | 2024-09-25T20:01:18Z | 2024-11-25T00:25:59Z | sentence-transformers | 10 | 0 | [{"name": "stella_en_400M_v5", "results": [{"dataset": {"config": "en", "name": "MTEB AmazonCounterfactualClassification (en)", "revision": "e8379541af4e31359cca9fbcf4b00f2671dba205", "split": "test", "type": "mteb/amazon_counterfactual"}, "metrics": [{"type": "accuracy", "value": 92.35820895522387, "verified": false},... | sentence-similarity | {"parameters": {"F32": 435188736}, "total": 435188736} | [
".gitattributes",
"README.md",
"config.json",
"config_sentence_transformers.json",
"configuration.py",
"model.safetensors",
"modeling.py",
"modules.json",
"pytorch_model.bin",
"sentence_bert_config.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | d6e3171ee274a277a37caee66525e818987ec2a0 | [
"sentence-transformers",
"pytorch",
"safetensors",
"new",
"feature-extraction",
"mteb",
"transformers",
"sentence-similarity",
"custom_code",
"license:mit",
"model-index",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["NewModel"], "auto_map": {"AutoConfig": "configuration.NewConfig", "AutoModel": "modeling.NewModel"}, "model_type": "new", "tokenizer_config": {"cls_token": "[CLS]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
"auto_model": "AutoModel",
"custom_class": "modeling.NewModel",
"pipeline_tag": "feature-extraction",
"processor": null
} | {"license": "mit", "tags": ["mteb", "sentence-transformers", "transformers", "sentence-similarity"], "model-index": [{"name": "stella_en_400M_v5", "results": [{"task": {"type": "Classification"}, "dataset": {"name": "MTEB AmazonCounterfactualClassification (en)", "type": "mteb/amazon_counterfactual", "config": "en", "s... | ## Marqo Stella v2
This model is similar to the original [Dunzhang stella 400m model](https://huggingface.co/dunzhang/stella_en_400M_v5), with a fused matryoshka layer. The hierarchical structuring from a Matryoshka Layer reduces the computational overhead for generating embeddings, while leaving relevance metrics unc... | null | [
"mit"
] | null | null | 435,188,736 | null | null | [
"new",
"NewModel",
"AutoModel",
"modeling.NewModel"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
6877f4e8fc2c55f6fe4b82c3 | fce4c9c0fb3bed35 | 1e78464774d502ca | 128,256 | BPE | 280,147 | false | true | true | true | 256 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 658 | OpenMed/OpenMed-NER-ChemicalDetect-EuroMed-212M | OpenMed | null | 102,963 | 681,137 | False | 2025-07-16T18:52:24Z | 2025-08-05T09:32:41Z | transformers | 4 | 0 | null | token-classification | {"parameters": {"BF16": 211768323}, "total": 211768323} | [
".gitattributes",
"README.md",
"config.json",
"configuration_eurobert.py",
"model.safetensors",
"modeling_eurobert.py",
"openmed_vs_sota_grouped_bars.png",
"special_tokens_map.json",
"test_results.json",
"tokenizer.json",
"tokenizer_config.json"
] | 5ab702762c0b54489e3274695c5a241900b172f7 | [
"transformers",
"safetensors",
"eurobert",
"token-classification",
"named-entity-recognition",
"biomedical-nlp",
"chemical-entity-recognition",
"drug-discovery",
"pharmacology",
"chemistry",
"chem",
"custom_code",
"en",
"arxiv:2508.01630",
"license:apache-2.0",
"endpoints_compatible",
... | null | {"architectures": ["EuroBertForTokenClassification"], "auto_map": {"AutoConfig": "configuration_eurobert.EuroBertConfig", "AutoModel": "modeling_eurobert.EuroBertModel", "AutoModelForMaskedLM": "modeling_eurobert.EuroBertForMaskedLM", "AutoModelForPreTraining": "modeling_eurobert.EuroBertPreTrainedModel", "AutoModelFor... | {
"auto_model": "AutoModelForTokenClassification",
"custom_class": null,
"pipeline_tag": "token-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": null, "tags": ["token-classification", "named-entity-recognition", "biomedical-nlp", "transform... | null | null | [
"apache-2.0"
] | null | [
"en"
] | 211,768,323 | null | null | [
"AutoModelForTokenClassification",
"EuroBertForTokenClassification",
"eurobert"
] | [
"token-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
621ffdc136468d709f1810f7 | 18d8f9e7a7759550 | 4bea97215fbe3368 | 32,102 | Unigram | null | true | true | true | true | 105 | Sequence | Metaspace | Metaspace | [
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 659 | tscholak/1zha5ono | tscholak | null | 56 | 248,616 | False | 2022-03-02T23:29:05Z | 2022-01-10T21:50:11Z | transformers | 4 | 0 | null | null | null | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"optimizer.pt",
"pytorch_model.bin",
"rng_state_0.pth",
"rng_state_1.pth",
"rng_state_2.pth",
"rng_state_3.pth",
"scheduler.pt",
"special_tokens_map.json",
"spiece.model",
"tokenizer.json",
"tokenizer_config.json",
"tra... | cb38df00e41169d44053dd24405d229856fdfb06 | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"text2sql",
"en",
"dataset:spider",
"arxiv:2109.05093",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["T5ForConditionalGeneration"], "model_type": "t5", "tokenizer_config": {"eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>"}} | {
"auto_model": "AutoModelForSeq2SeqLM",
"custom_class": null,
"pipeline_tag": "text2text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["spider"], "eval_results": null, "language": ["en"], "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": ["spider"], "model_name": null, "pipeline_tag": null, "tags": ["text2sql"], "thumbnail": "https://repository-images.githubuserconte... | ## tscholak/1zha5ono
Fine-tuned weights for [PICARD - Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models](https://arxiv.org/abs/2109.05093) based on [t5.1.1.lm100k.base](https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md#lm-adapted-t5... | null | [
"apache-2.0"
] | [
"spider"
] | [
"en"
] | null | null | [
"spider"
] | [
"t5",
"T5ForConditionalGeneration",
"AutoModelForSeq2SeqLM"
] | [
"text2text-generation"
] | null | null | null |
65b7910f4b0992989b21a090 | d49b8fd5deacd284 | 3aba7be11585fdda | 122,753 | BPE | 171,540 | true | false | true | true | 3 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 220 | openbmb/MiniCPM-2B-sft-bf16 | openbmb | null | 36,542 | 394,999 | False | 2024-01-29T11:50:39Z | 2024-09-07T05:28:42Z | transformers | 124 | 0 | null | text-generation | null | [
".gitattributes",
"README.md",
"config.json",
"configuration_minicpm.py",
"generation_config.json",
"modeling_minicpm.py",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer.model",
"tokenizer_config.json"
] | 4ec16344ac13e6ef5010aeecaa533369ac8eb53c | [
"transformers",
"pytorch",
"text-generation",
"MiniCPM",
"ModelBest",
"THUNLP",
"conversational",
"custom_code",
"en",
"zh",
"region:us"
] | null | {"architectures": ["MiniCPMForCausalLM"], "auto_map": {"AutoConfig": "configuration_minicpm.MiniCPMConfig", "AutoModel": "modeling_minicpm.MiniCPMModel", "AutoModelForCausalLM": "modeling_minicpm.MiniCPMForCausalLM", "AutoModelForSeq2SeqLM": "modeling_minicpm.MiniCPMForCausalLM", "AutoModelForSequenceClassification": "... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": "modeling_minicpm.MiniCPMForCausalLM",
"pipeline_tag": "text-generation",
"processor": null
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en", "zh"], "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["MiniCPM", "ModelBest", "THUNLP"]} | <div align="center">
<h1>
MiniCPM
</h1>
</div>
<p align="center">
<a href="https://shengdinghu.notion.site/MiniCPM-c805a17c5c8046398914e47f0542095a?pvs=4" target="_blank">MiniCPM 技术报告</a><a href="https://shengdinghu.notion.site/MiniCPM-Unveiling-the-Potential-of-End-side-Large-Language-Models-d4d3a8c426424654a4e80e4... | null | null | null | [
"en",
"zh"
] | null | null | null | [
"modeling_minicpm.MiniCPMForCausalLM",
"AutoModelForCausalLM",
"MiniCPMForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
665dc5089047f0631dd34d15 | 34093184d10f76a9 | 35b001f3a491f9f9 | 151,646 | BPE | 151,387 | true | true | true | true | 3 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 53 | Qwen/Qwen2-72B-Instruct-GPTQ-Int4 | Qwen | {
"models": [
{
"_id": "66555421d17e141dcb225f7f",
"id": "Qwen/Qwen2-72B-Instruct"
}
],
"relation": "quantized"
} | 182 | 249,909 | False | 2024-06-03T13:28:40Z | 2024-08-21T10:27:09Z | transformers | 33 | 0 | null | text-generation | {"parameters": {"F16": 2499616768, "I32": 70464307200}, "total": 72963923968} | [
".gitattributes",
"LICENSE",
"README.md",
"config.json",
"generation_config.json",
"merges.txt",
"model-00001-of-00011.safetensors",
"model-00002-of-00011.safetensors",
"model-00003-of-00011.safetensors",
"model-00004-of-00011.safetensors",
"model-00005-of-00011.safetensors",
"model-00006-of-0... | 9d456ed2d2e4fceeb4c0602e190ec85a2a7ac44a | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"chat",
"conversational",
"en",
"arxiv:2309.00071",
"base_model:Qwen/Qwen2-72B-Instruct",
"base_model:quantized:Qwen/Qwen2-72B-Instruct",
"license:other",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"gptq",
"r... | null | {"architectures": ["Qwen2ForCausalLM"], "model_type": "qwen2", "quantization_config": {"bits": 4, "quant_method": "gptq"}, "tokenizer_config": {"bos_token": null, "chat_template": "{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system\nYou are a helpful assistant.<|i... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "Qwen/Qwen2-72B-Instruct", "datasets": null, "eval_results": null, "language": ["en"], "library_name": null, "license": "other", "license_name": "tongyi-qianwen", "license_link": "https://huggingface.co/Qwen/Qwen2-72B-Instruct-GPTQ-Int4/blob/main/LICENSE", "metrics": null, "model_name": null, "pipeline_t... | # Qwen2-72B-Instruct-GPTQ-Int4
## Introduction
Qwen2 is the new series of Qwen large language models. For Qwen2, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters, including a Mixture-of-Experts model. This repo contains the instruction-tuned 7... | null | [
"other",
"tongyi-qianwen",
"https://huggingface.co/Qwen/Qwen2-72B-Instruct-GPTQ-Int4/blob/main/LICENSE"
] | null | [
"en"
] | 72,963,923,968 | null | null | [
"AutoModelForCausalLM",
"Qwen2ForCausalLM",
"qwen2"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
6616f382b76458bae3fe6789 | 548b9582de92b026 | fadac82906efdc90 | 32,768 | BPE | 58,980 | false | true | true | true | 771 | null | Metaspace | Sequence | null | [
"Metaspace"
] | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 660 | mistral-community/Mixtral-8x22B-v0.1-AWQ | mistral-community | {
"models": [
{
"_id": "661633bef8ea04242917ca15",
"id": "v2ray/Mixtral-8x22B-v0.1"
}
],
"relation": "quantized"
} | 538 | 250,436 | False | 2024-04-10T20:16:02Z | 2024-07-05T06:22:20Z | transformers | 36 | 0 | null | text-generation | {"parameters": {"I32": 140223971328, "F16": 396662784}, "total": 140620634112} | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
"mixtral-8x22b.jpeg",
"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.... | bef89de18b3815a74fea958fd8c1d106114252a1 | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"quantized",
"4-bit",
"AWQ",
"moe",
"fr",
"it",
"de",
"es",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us",
"base_model:v2ray/Mixtral-8x22B-v0.1",
... | null | {"architectures": ["MixtralForCausalLM"], "model_type": "mixtral", "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": "v2ray/Mixtral-8x22B-v0.1", "datasets": null, "eval_results": null, "language": ["en", "es", "de", "it", "fr"], "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": "Mixtral-8x22B-v0.1-AWQ", "pipeline_tag": "text-generation", "tags": ["quantize... | <img src="./mixtral-8x22b.jpeg" width="600" />
# Mixtral-8x22B-v0.1-AWQ
On April 10th, [@MistralAI](https://huggingface.co/mistralai) released a model named "Mixtral 8x22B," an 176B MoE via magnet link (torrent):
- 176B MoE with ~40B active
- Context length of 65k tokens
- The base model can be fine-tuned
- Requires... | null | null | null | [
"en",
"es",
"de",
"it",
"fr"
] | 140,620,634,112 | null | null | [
"mixtral",
"MixtralForCausalLM",
"AutoModelForCausalLM"
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"text-generation"
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"text"
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"text"
] | [
"text"
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66a21cde5652fd2458a8b004 | 4f7b990ab0ca0a11 | 2e6d6e9dc2d2c5c2 | 32,003 | BPE | 58,980 | true | false | true | true | 6 | Sequence | null | Sequence | [
"Prepend",
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"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 661 | BAAI/bge-en-icl | BAAI | null | 1,670 | 255,973 | False | 2024-07-25T09:37:34Z | 2025-01-15T02:43:23Z | sentence-transformers | 135 | 0 | [{"name": "bge-en-icl", "results": [{"dataset": {"config": "en", "name": "MTEB AmazonCounterfactualClassification (en)", "revision": "e8379541af4e31359cca9fbcf4b00f2671dba205", "split": "test", "type": "mteb/amazon_counterfactual"}, "metrics": [{"type": "accuracy", "value": 93.1492537313433, "verified": false}, {"type"... | feature-extraction | {"parameters": {"F32": 7110672384}, "total": 7110672384} | [
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} | {"license": "apache-2.0", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers", "mteb"], "model-index": [{"name": "bge-en-icl", "results": [{"task": {"type": "Classification"}, "dataset": {"name": "MTEB AmazonCounterfactualClassification (en)", "type": "mteb/amazon_counterfactua... | <h1 align="center">FlagEmbedding</h1>
For more details please refer to our Github: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding).
**BGE-EN-ICL** primarily demonstrates the following capabilities:
- In-context learning ability: By providing few-shot examples in the query, it can significantly enhance t... | null | [
"apache-2.0"
] | null | null | 7,110,672,384 | null | null | [
"AutoModel",
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"sentence-similarity",
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"multimodal"
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"logits",
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6797d5fa103218dc515b0fc5 | 36935258f89e8e9e | 83d37c6e8ee56b77 | 151,665 | BPE | 151,387 | true | true | true | true | 22 | NFC | Sequence | ByteLevel | [
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"models": [
{
"_id": "678e15048143a819dd01a3c1",
"id": "deepseek-ai/DeepSeek-R1-Distill-Qwen-32B"
}
],
"relation": "quantized"
} | 1,221 | 250,800 | False | 2025-01-27T18:52:42Z | 2025-01-29T17:12:17Z | transformers | 2 | 0 | null | text-generation | {"parameters": {"BF16": 1562907648, "F8_E4M3": 31205621760}, "total": 32768529408} | [
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"license:mit",
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"compressed-t... | null | {"architectures": ["Qwen2ForCausalLM"], "model_type": "qwen2", "quantization_config": {"quant_method": "compressed-tensors"}, "tokenizer_config": {"bos_token": "<\uff5cbegin\u2581of\u2581sentence\uff5c>", "chat_template": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{%... | {
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} | {"base_model": ["deepseek-ai/DeepSeek-R1-Distill-Qwen-32B"], "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": "text-generation", "tags": ["chat"]} | # Model Overview
- **Model Optimizations:**
- **Weight quantization:** FP8
- **Activation quantization:** FP8
- **Release Date:** 1/28/2025
Quantized version of [deepseek-ai/DeepSeek-R1-Distill-Qwen-32B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B/) to FP8 data type, ready for inference with S... | null | [
"mit"
] | null | [
"en"
] | 32,768,529,408 | null | null | [
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"Replace",
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] | cfc3a78e491db11ed6fd461d9ce8ba921de01602 | [
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"en",
"license:gemma",
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] | null | {"architectures": ["Gemma2ForCausalLM"], "model_type": "gemma2", "quantization_config": {"load_in_4bit": true, "load_in_8bit": false, "quant_method": "bitsandbytes"}, "tokenizer_config": {"bos_token": "<bos>", "chat_template": "{{ bos_token }}{% if messages[0]['role'] == 'system' %}{{ raise_exception('System role not s... | {
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`pip install git+https://github.com/huggingface/transformers.git`
# Finetune Gemma 2, Llama 3.1, Mistral 2-5x faster with 70% less memory via Unsloth!
Directly quantized 4bit model with `bitsandbytes`.
We have a Google Colab Tesla T4 notebook for **Gemma 2 (2B)** here... | null | [
"gemma"
] | null | [
"en"
] | 2,677,836,404 | null | null | [
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"ByteLevel"
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<div class="flex">
<div
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I've recently stumbled upon a very comprehensive dataset on measurement of hate speech from my alma mater, UC Berkeley. It aggregated social media comments from Youtube, Reddit, and Twitter.
The interes... | null | [
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## Model Description
`ReasonEval-7B` is a 7B parameter decoder-only language model fine-tuned from [`WizardMath-7B-V1.1`](https://huggingface.co/WizardLM/WizardMath-7B-V1.1). Given a mathematical problem and the solution, `ReasonEval-7B` assesses the problem-solving process in a step-by... | null | [
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"Split",
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This is a minimal model built for unit tests in the [TRL](https://github.com/huggingface/trl) library. | null | null | null | null | 2,453,368 | null | null | [
"AutoModelForCausalLM",
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] | 46 | jphme/Llama-2-13b-chat-german | jphme | null | 825 | 251,074 | False | 2023-07-21T23:01:14Z | 2023-10-06T12:52:01Z | transformers | 63 | 0 | null | text-generation | null | [
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} | {"base_model": null, "datasets": ["Christoph911/German-legal-SQuAD", "philschmid/test_german_squad"], "eval_results": null, "language": ["de", "en"], "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-generation", "tags": ["pytor... | **Please Check out EM German, our new german-speaking LLM model family with significantly improved capabilites. EM German is available in Llama2 7b,13b and 70b and Mistral- and LeoLM-based versions! All information and download links can be found [here](https://github.com/jphme/EM_German/blob/main/README.md).**
# Lla... | null | null | [
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"dataset:togethercomputer/RedPajama-Data-1T",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["GPTNeoXForCausalLM"], "model_type": "gpt_neox", "tokenizer_config": {"bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "unk_token": "<|endoftext|>"}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
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} | {"base_model": null, "datasets": ["togethercomputer/RedPajama-Data-1T"], "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} | # RedPajama-INCITE-Base-3B-v1
RedPajama-INCITE-Base-3B-v1 was developed by Together and leaders from the open-source AI community including Ontocord.ai, ETH DS3Lab, AAI CERC, Université de Montréal, MILA - Québec AI Institute, Stanford Center for Research on Foundation Models (CRFM), Stanford Hazy Research research gr... | null | [
"apache-2.0"
] | [
"togethercomputer/RedPajama-Data-1T"
] | [
"en"
] | null | null | null | [
"gpt_neox",
"AutoModelForCausalLM",
"GPTNeoXForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
66eb4111d346952f35760f30 | 848b60b6cd304b65 | ffc911e785abe60e | 151,665 | BPE | 151,387 | true | true | true | true | 22 | NFC | Sequence | ByteLevel | [
"NFC"
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"Split",
"ByteLevel"
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"ByteLevel"
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"models": [
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"_id": "66ec161712bd743cfe94a0f4",
"id": "Qwen/Qwen2.5-Math-7B-Instruct"
}
],
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} | 69 | 245,850 | False | 2024-09-18T21:07:29Z | 2024-09-18T21:16:14Z | transformers | 0 | 0 | null | text-generation | null | [
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] | b144b9b952f8aa481cadb47e2eb6151d18a4de55 | [
"transformers",
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"region:... | null | {"architectures": ["Qwen2ForCausalLM"], "model_type": "qwen2", "quantization_config": {"bits": 4}, "tokenizer_config": {"bos_token": null, "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n ... | {
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} | {"base_model": "Qwen/Qwen2.5-Math-7B-Instruct", "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": "https://huggingface.co/Qwen/Qwen2.5-Math-7B-Instruct/blob/main/LICENSE", "metrics": null, "model_name": null, "pipel... | # mlx-community/Qwen2.5-Math-7B-Instruct-4bit
The Model [mlx-community/Qwen2.5-Math-7B-Instruct-4bit](https://huggingface.co/mlx-community/Qwen2.5-Math-7B-Instruct-4bit) was converted to MLX format from [Qwen/Qwen2.5-Math-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Math-7B-Instruct) using mlx-lm version **0.18.2*... | null | [
"apache-2.0",
"https://huggingface.co/Qwen/Qwen2.5-Math-7B-Instruct/blob/main/LICENSE"
] | null | [
"en"
] | null | null | null | [
"AutoModelForCausalLM",
"Qwen2ForCausalLM",
"qwen2"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
67d81cd9d4db484fd75e3b97 | d1b17ca2d3ca5ffe | 83d37c6e8ee56b77 | 151,665 | BPE | 151,387 | true | true | true | true | 22 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 75 | Skywork/Skywork-R1V-38B | Skywork | null | 73,696 | 519,615 | False | 2025-03-17T13:00:09Z | 2025-08-12T07:36:18Z | transformers | 127 | 0 | null | image-text-to-text | {"parameters": {"BF16": 38392158592}, "total": 38392158592} | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"configuration.json",
"configuration_skywork_chat.py",
"configuration_skywork_lm2.py",
"configuration_skywork_vit.py",
"conversation.py",
"eval.jpeg",
"generation_config.json",
"logo.jpeg",
"model-00001-of-00016.safetensors",... | 363f7c8e12c537b1ef468193597f9e58f998eab9 | [
"transformers",
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"image-text-to-text",
"conversational",
"custom_code",
"arxiv:2504.05599",
"license:mit",
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] | null | {"architectures": ["SkyworkR1VChatModel"], "auto_map": {"AutoConfig": "configuration_skywork_chat.SkyworkChatConfig", "AutoModel": "modeling_skywork_chat.SkyworkChatModel", "AutoModelForCausalLM": "modeling_skywork_chat.SkyworkChatModel"}, "model_type": "skywork_chat", "tokenizer_config": {"bos_token": {"__type": "Adde... | {
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} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": "transformers", "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "image-text-to-text", "tags": null} | # Skywork-R1V
<div align="center">
<img src="skywork-logo.png" alt="Introduction Image" width="500" height="400">
</div>
## 📖 [Technical Report](https://arxiv.org/abs/2504.05599) | 💻 [GitHub](https://github.com/SkyworkAI/Skywork-R1V) | 🌐 [ModelScope](https://modelscope.cn/models/Skywork/Skywork-R1V-38B)
<di... | null | [
"mit"
] | null | null | 38,392,158,592 | null | null | [
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] | [
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null
] | [
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] | [
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"image"
] | [
"text"
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{
"_id": "6891e3bb084ce75acffb033d",
"id": "Qwen/Qwen3-4B-Instruct-2507"
}
],
"relation": "quantized"
} | 1,425 | 302,327 | False | 2025-08-06T16:27:02Z | 2025-08-06T16:27:20Z | transformers | 1 | 0 | null | text-generation | {"parameters": {"I64": 504, "I32": 457712640, "BF16": 806493696}, "total": 1264206840} | [
".gitattributes",
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"reg... | null | {"architectures": ["Qwen3ForCausalLM"], "model_type": "qwen3", "quantization_config": {"quant_method": "compressed-tensors"}, "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... | {
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} | {"base_model": ["Qwen/Qwen3-4B-Instruct-2507"], "datasets": null, "eval_results": null, "language": null, "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": "https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507/blob/main/LICENSE", "metrics": null, "model_name": null, "pipeline_... | # Qwen3-4B-Instruct-2507
<a href="https://chat.qwen.ai" target="_blank" style="margin: 2px;">
<img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;"/>
</a>
## Highlights
We introduce the updated version of the **Qwen3... | null | [
"apache-2.0",
"https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507/blob/main/LICENSE"
] | null | null | 1,264,206,840 | null | null | [
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] | [
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] | [
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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 | 29,368,320 | null | null | [
"AutoModel",
"UltravoxModel",
"ultravox",
"ultravox_model.UltravoxModel"
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] | 57 | Qwen/Qwen2-VL-7B | Qwen | null | 22,863 | 310,049 | False | 2024-09-05T07:15:10Z | 2025-01-12T02:13:50Z | transformers | 64 | 0 | null | image-text-to-text | {"parameters": {"BF16": 8291375616}, "total": 8291375616} | [
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"model-00005-of-00005.safetenso... | e61834264a23db10c06dc4f566dac5634c7ca024 | [
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"safetensors",
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"image-text-to-text",
"multimodal",
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"en",
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"text-generation-inference",
"endpoints_compatible",
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] | null | {"architectures": ["Qwen2VLForConditionalGeneration"], "model_type": "qwen2_vl", "processor_config": {"chat_template": "{% if messages is string %}{{ messages }}{% else %}{% for content in messages %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}<|vision_start|><|image_pad|><|vision... | {
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"custom_class": null,
"pipeline_tag": "image-text-to-text",
"processor": "AutoProcessor"
} | {"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": "image-text-to-text", "tags": ["multimodal"]} | # Qwen2-VL-7B
## Introduction
We're excited to unveil **Qwen2-VL**, the latest iteration of our Qwen-VL model, representing nearly a year of innovation.
> [!Important]
> This is the base pretrained model of Qwen2-VL-7B without instruction tuning.
### What’s New in Qwen2-VL?
#### Key Enhancements:
* **SoTA unders... | 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;">
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"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"base_model": "roberta-base", "datasets": ["dstefa/New_York_Times_Topics"], "license": "mit", "metrics": ["accuracy", "f1", "precision", "recall"], "pipeline_tag": "text-classification", "tags": ["topic", "classification", "news", "roberta"], "widget": [{"text": "Olympic champion Kostas Kederis today left hospital ahe... | # roberta-base_topic_classification_nyt_news
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the NYT News dataset, which contains 256,000 news titles from articles published from 2000 to the present (https://www.kaggle.com/datasets/aryansingh0909/nyt-articles-21m-2000-prese... | null | [
"mit"
] | [
"dstefa/New_York_Times_Topics"
] | null | null | null | [
"accuracy",
"f1",
"precision",
"recall"
] | [
"roberta",
"AutoModelForSequenceClassification",
"RobertaForSequenceClassification"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
664a2ff3f7e6c2f2909ae3f2 | 34093184d10f76a9 | 35b001f3a491f9f9 | 151,646 | BPE | 151,387 | true | true | true | true | 3 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 53 | IAAR-Shanghai/xFinder-qwen1505 | IAAR-Shanghai | null | 36 | 242,824 | False | 2024-05-19T16:59:31Z | 2025-03-26T10:10:37Z | transformers | 7 | 0 | null | text-generation | null | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"generation_config.json",
"license",
"merges.txt",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | 74710b225ed6b7655701d0540d868edc5466e350 | [
"transformers",
"pytorch",
"qwen2",
"text-generation",
"instruction-finetuning",
"conversational",
"en",
"license:cc-by-nc-nd-4.0",
"text-generation-inference",
"region:us"
] | null | {"architectures": ["Qwen2ForCausalLM"], "model_type": "qwen2", "tokenizer_config": {"bos_token": null, "chat_template": "{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system\nYou are a helpful assistant<|im_end|>\n' }}{% endif %}{{'<|im_start|>' + message['role'] + ... | {
"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": "cc-by-nc-nd-4.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["instruction-finetuning"], "inference": false, "task_categories": ["text-ge... | <br>
# xFinder-qwen1505
## Model Details
xFinder-qwen1505 is a model specifically designed for key answer extraction in large language models (LLMs). It is trained by fine-tuning Qwen-1.5-0.5B.
- **Developed by:** [IAAR](https://www.iaar.ac.cn)
- **Fine-tuned from Model:** [Qwen-1.5-0.5B](https://huggingface.co/Qwen/... | null | [
"cc-by-nc-nd-4.0"
] | null | [
"en"
] | null | null | null | [
"AutoModelForCausalLM",
"Qwen2ForCausalLM",
"qwen2"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
67ff92e53b0f623c97ca495d | b647996d6543f730 | 7ab62ce4e32550df | 151,674 | BPE | 151,387 | true | true | true | true | 31 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 172 | OpenGVLab/InternVL3-38B-Instruct | OpenGVLab | {
"models": [
{
"_id": "6800b5e32f1ac745f59d83ed",
"id": "OpenGVLab/InternVL3-38B-Pretrained"
}
],
"relation": "finetune"
} | 1,624 | 255,686 | False | 2025-04-16T11:22:13Z | 2025-09-11T12:51:10Z | transformers | 10 | 0 | null | image-text-to-text | {"parameters": {"BF16": 38388164992}, "total": 38388164992} | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"configuration_intern_vit.py",
"configuration_internvl_chat.py",
"conversation.py",
"examples/image1.jpg",
"examples/image2.jpg",
"examples/red-panda.mp4",
"generation_config.json",
"merges.txt",
"model-00001-of-00016.safeten... | 150ad666e0c733b809742decded1f7484995b99c | [
"transformers",
"safetensors",
"internvl_chat",
"feature-extraction",
"internvl",
"custom_code",
"image-text-to-text",
"conversational",
"multilingual",
"arxiv:2312.14238",
"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/InternVL3-38B-Pretrained"], "datasets": null, "eval_results": null, "language": ["multilingual"], "library_name": "transformers", "license": "apache-2.0", "license_name": "qwen", "license_link": "https://huggingface.co/Qwen/Qwen2.5-72B-Instruct/blob/main/LICENSE", "metrics": null, "model_name... | # InternVL3-38B-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 | [
"apache-2.0",
"qwen",
"https://huggingface.co/Qwen/Qwen2.5-72B-Instruct/blob/main/LICENSE"
] | null | [
"multilingual"
] | 38,388,164,992 | null | null | [
"AutoModel",
"internvl_chat",
"InternVLChatModel",
"modeling_internvl_chat.InternVLChatModel"
] | [
"image-text-to-text",
"feature-extraction"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"text",
"embeddings"
] |
65901484bea632dc947f05aa | 88cd37e502d27a0a | 0e7bc0977f200d1c | 32,000 | BPE | 58,980 | true | false | true | true | 3 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 88 | kekmodel/StopCarbon-10.7B-v5 | kekmodel | null | 13,563 | 304,948 | False | 2023-12-30T13:00:52Z | 2024-01-03T16:58:20Z | transformers | 2 | 0 | null | text-generation | {"parameters": {"F16": 10731524096}, "total": 10731524096} | [
".gitattributes",
"README.md",
"config.json",
"model-00001-of-00003.safetensors",
"model-00002-of-00003.safetensors",
"model-00003-of-00003.safetensors",
"model.safetensors.index.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer.model",
"tokenizer_config.json"
] | 7d59819dce2439f6c83b4f5c21a68aa882ff5ac9 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"merge",
"conversational",
"en",
"license:cc-by-nc-sa-4.0",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": "<s>", "chat_template": "{% for message in messages %}{% if message['role'] == 'system' %}{% if message['content']%}{{'### System:\n' + message['content']+'\n\n'}}{% endif %}{% elif message['role'] == 'user' %}{{'### User:\... | {
"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": "cc-by-nc-sa-4.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["merge"]} | # StopCarbon
This model is an experimental version created using [mergekit](https://github.com/cg123/mergekit).
- merge models
- kyujinpy/Sakura-SOLAR-Instruct
- jeonsworld/CarbonVillain-en-10.7B-v1
- merge_method: slerp
# Prompt Template(s)
```
### User:
{user}
### Assistant:
{asistant}
``` | null | [
"cc-by-nc-sa-4.0"
] | null | [
"en"
] | 10,731,524,096 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
66e9900111ba6d9ff4e740b1 | c1fc0adde7a41a26 | 8386a692be535934 | 51,866 | BPE | 50,000 | false | true | true | true | 1,609 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 594 | kotoba-tech/kotoba-whisper-v2.1 | kotoba-tech | null | 2,465 | 267,898 | False | 2024-09-17T14:19:45Z | 2024-10-23T00:48:35Z | transformers | 19 | 0 | null | automatic-speech-recognition | {"parameters": {"F32": 756405760}, "total": 756405760} | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"generation_config.json",
"kotoba_whisper.py",
"merges.txt",
"model.safetensors",
"normalizer.json",
"pipeline/kotoba_whisper.py",
"pipeline/push_pipeline.py",
"pipeline/test_pipeline.py",
"preprocessor_config.json",
"speci... | 57a9d8ab771a0124706b67d22509bedd07c36187 | [
"transformers",
"safetensors",
"whisper",
"automatic-speech-recognition",
"audio",
"hf-asr-leaderboard",
"ja",
"dataset:japanese-asr/whisper_transcriptions.reazonspeech.all",
"dataset:japanese-asr/whisper_transcriptions.reazonspeech.all.wer_10.0",
"dataset:japanese-asr/whisper_transcriptions.reazo... | 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"
} | {"base_model": null, "datasets": ["japanese-asr/whisper_transcriptions.reazonspeech.all", "japanese-asr/whisper_transcriptions.reazonspeech.all.wer_10.0", "japanese-asr/whisper_transcriptions.reazonspeech.all.wer_10.0.vectorized"], "eval_results": null, "language": "ja", "library_name": "transformers", "license": "apac... | # Kotoba-Whisper-v2.1
_Kotoba-Whisper-v2.1_ is a Japanese ASR model based on [kotoba-tech/kotoba-whisper-v2.0](https://huggingface.co/kotoba-tech/kotoba-whisper-v2.0), with
additional postprocessing stacks integrated as [`pipeline`](https://huggingface.co/docs/transformers/en/main_classes/pipelines). The new features ... | null | [
"apache-2.0"
] | [
"japanese-asr/whisper_transcriptions.reazonspeech.all",
"japanese-asr/whisper_transcriptions.reazonspeech.all.wer_10.0",
"japanese-asr/whisper_transcriptions.reazonspeech.all.wer_10.0.vectorized"
] | [
"ja"
] | 756,405,760 | null | null | [
"WhisperForConditionalGeneration",
"whisper",
"AutoModelForSpeechSeq2Seq"
] | [
"automatic-speech-recognition"
] | [
"multimodal"
] | [
"audio"
] | [
"text"
] |
6883c9b322fc56822dc7a911 | 1e0a2493163c0c63 | 6622292dca275a2d | 128,256 | BPE | 280,147 | false | true | true | true | 256 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 18 | nvidia/Llama-3_3-Nemotron-Super-49B-v1_5 | nvidia | null | 168,822 | 1,021,639 | False | 2025-07-25T18:15:15Z | 2025-10-15T16:21:38Z | transformers | 228 | 0 | null | text-generation | {"parameters": {"BF16": 49867145216}, "total": 49867145216} | [
".gitattributes",
"BIAS.md",
"EXPLAINABILITY.md",
"PRIVACY.md",
"README.md",
"SAFETY&SECURITY.md",
"accuracy_chart.png",
"block_config.py",
"config.json",
"configuration_decilm.py",
"generation_config.json",
"llama_nemotron_toolcall_parser_no_streaming.py",
"model-00001-of-00021.safetensors"... | 420ba7d28211abf116b8b103ab700d92619daf98 | [
"transformers",
"safetensors",
"nemotron-nas",
"text-generation",
"nvidia",
"llama-3",
"pytorch",
"conversational",
"custom_code",
"en",
"arxiv:2411.19146",
"arxiv:2505.00949",
"arxiv:2502.00203",
"license:other",
"region:us"
] | 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": "{% set bos = \"<|begin_of_text|>\" %}{%- set enabl... | {
"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,... | # Llama-3.3-Nemotron-Super-49B-v1.5
## Model Overview
Llama-3.3-Nemotron-Super-49B-v1.5 is a significantly upgraded version of Llama-3.3-Nemotron-Super-49B-v1 and is a large language model (LLM) which is a derivative of Meta Llama-3.3-70B-Instruct (AKA the reference model). It is a re... | null | [
"other",
"nvidia-open-model-license",
"https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/"
] | null | [
"en"
] | 49,867,145,216 | null | null | [
"DeciLMForCausalLM",
"AutoModelForCausalLM",
"modeling_decilm.DeciLMForCausalLM",
"nemotron-nas"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
680bf0bed27b417e49d0d915 | 53ecf467267e2824 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 0 | OrcaDB/bge-base | OrcaDB | null | 3,918 | 254,892 | False | 2025-04-25T20:29:50Z | 2025-06-16T15:53:57Z | sentence-transformers | 0 | 0 | [{"name": "bge-base-en-v1.5", "results": [{"task": {"type": "Classification"}, "dataset": {"type": "mteb/amazon_counterfactual", "name": "MTEB AmazonCounterfactualClassification (en)", "config": "en", "split": "test", "revision": "e8379541af4e31359cca9fbcf4b00f2671dba205"}, "metrics": [{"type": "accuracy", "value": 76.... | feature-extraction | {"parameters": {"F32": 109482240}, "total": 109482240} | [
".gitattributes",
"1_Pooling/config.json",
"README.md",
"config.json",
"config_sentence_transformers.json",
"model.safetensors",
"modules.json",
"sentence_bert_config.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | 7e32c0086a5cbdee316003088a9585b705c127eb | [
"sentence-transformers",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"mteb",
"en",
"arxiv:2401.03462",
"arxiv:2312.15503",
"arxiv:2311.13534",
"arxiv:2310.07554",
"arxiv:2309.07597",
"license:mit",
"model-index",
"text-embeddings-inference",
"e... | 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",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"language": ["en"], "license": "mit", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers", "mteb"], "model-index": [{"name": "bge-base-en-v1.5", "results": [{"task": {"type": "Classification"}, "dataset": {"name": "MTEB AmazonCounterfactualClassification (en)", "type": "mteb/a... | <h1 align="center">FlagEmbedding</h1>
<h4 align="center">
<p>
<a href=#model-list>Model List</a> |
<a href=#frequently-asked-questions>FAQ</a> |
<a href=#usage>Usage</a> |
<a href="#evaluation">Evaluation</a> |
<a href="#train">Train</a> |
<a href="#contact">Conta... | null | [
"mit"
] | null | [
"en"
] | 109,482,240 | null | null | [
"BertModel",
"AutoModel",
"bert"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
6543af0e9b639f21e1b6e7d3 | 41a000f2f9024404 | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 13 | ESGBERT/SocRoBERTa-social | ESGBERT | null | 9,229 | 283,456 | False | 2023-11-02T14:15:42Z | 2025-11-10T16:39:53Z | transformers | 0 | 0 | null | text-classification | null | [
".gitattributes",
"README.md",
"config.json",
"merges.txt",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | 1ce05776d2018ec3c226cba8b8ef288df0faeae5 | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"ESG",
"social",
"en",
"dataset:ESGBERT/social_2k",
"license:apache-2.0",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["RobertaForSequenceClassification"], "model_type": "roberta", "tokenizer_config": {"bos_token": "<s>", "cls_token": "<s>", "eos_token": "</s>", "mask_token": "<mask>", "pad_token": "<pad>", "sep_token": "</s>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["ESGBERT/social_2k"], "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": ["ESG", "social"]} | # Model Card for SocRoBERTa-social
## Model Description
Based on [this paper](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4622514), this is the SocRoBERTa-social language model. A language model that is trained to better classify social texts in the ESG domain.
*Note: We generally recommend choosing the [Soc... | null | [
"apache-2.0"
] | [
"ESGBERT/social_2k"
] | [
"en"
] | null | null | null | [
"roberta",
"AutoModelForSequenceClassification",
"RobertaForSequenceClassification"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
6543a01d8ed3d7ec0418aa95 | 41a000f2f9024404 | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 13 | ESGBERT/EnvRoBERTa-environmental | ESGBERT | null | 149 | 240,745 | False | 2023-11-02T13:11:57Z | 2025-11-10T16:41:33Z | transformers | 5 | 0 | null | text-classification | null | [
".gitattributes",
"README.md",
"config.json",
"merges.txt",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | 001457f94daa217389c64fd562cecb1f0fb37246 | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"ESG",
"environmental",
"en",
"dataset:ESGBERT/environmental_2k",
"license:apache-2.0",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["RobertaForSequenceClassification"], "model_type": "roberta", "tokenizer_config": {"bos_token": "<s>", "cls_token": "<s>", "eos_token": "</s>", "mask_token": "<mask>", "pad_token": "<pad>", "sep_token": "</s>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["ESGBERT/environmental_2k"], "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": ["ESG", "environmental"]} | # Model Card for EnvRoBERTa-environmental
## Model Description
Based on [this paper](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4622514), this is the EnvRoBERTa-environmental language model. A language model that is trained to better classify environmental texts in the ESG domain.
*Note: We generally recomm... | null | [
"apache-2.0"
] | [
"ESGBERT/environmental_2k"
] | [
"en"
] | null | null | null | [
"roberta",
"AutoModelForSequenceClassification",
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] | [
"text-classification"
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"text"
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"text"
] | [
"logits"
] |
6543b024ed476297b37c95b6 | 41a000f2f9024404 | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
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"ByteLevel"
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".gitattributes",
"README.md",
"config.json",
"merges.txt",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | a3a51e548db042fecf106df3acd11a2b4fdd1224 | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"ESG",
"governance",
"en",
"dataset:ESGBERT/governance_2k",
"license:apache-2.0",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["RobertaForSequenceClassification"], "model_type": "roberta", "tokenizer_config": {"bos_token": "<s>", "cls_token": "<s>", "eos_token": "</s>", "mask_token": "<mask>", "pad_token": "<pad>", "sep_token": "</s>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModelForSequenceClassification",
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"pipeline_tag": "text-classification",
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} | {"base_model": null, "datasets": ["ESGBERT/governance_2k"], "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": ["ESG", "governance"]} | # Model Card for GovRoBERTa-governance
## Model Description
Based on [this paper](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4622514), this is the GovRoBERTa-governance language model. A language model that is trained to better classify governance texts in the ESG domain.
*Note: We generally recommend choos... | null | [
"apache-2.0"
] | [
"ESGBERT/governance_2k"
] | [
"en"
] | null | null | null | [
"roberta",
"AutoModelForSequenceClassification",
"RobertaForSequenceClassification"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
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689bdf086396de9ac2c39d01 | 46323c0f61df476d | 1fe366e13272caa0 | 262,145 | BPE | 514,906 | true | true | true | true | 6,415 | Replace | Split | Sequence | [
"Replace"
] | [
"Split"
] | [
"Replace",
"ByteFallback",
"Fuse"
] | 49 | unsloth/gemma-3-270m-it | unsloth | {
"models": [
{
"_id": "688a5f23e7cb4413ca5b8678",
"id": "google/gemma-3-270m-it"
}
],
"relation": "finetune"
} | 26,406 | 433,208 | False | 2025-08-13T00:40:40Z | 2025-08-14T16:54:50Z | transformers | 23 | 1 | null | text-generation | {"parameters": {"BF16": 268098176}, "total": 268098176} | [
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"added_tokens.json",
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] | 23cf460f6bb16954176b3ddcc8d4f250501458a9 | [
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"text-generation",
"gemma3",
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"conversational",
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... | null | {"architectures": ["Gemma3ForCausalLM"], "model_type": "gemma3_text", "tokenizer_config": {"bos_token": "<bos>", "eos_token": "<end_of_turn>", "pad_token": "<pad>", "unk_token": "<unk>", "use_default_system_prompt": false, "chat_template": "{# Unsloth Chat template fixes #}\n{{ bos_token }}\n{%- if messages[0]['role'] ... | {
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} | {"base_model": ["google/gemma-3-270m-it"], "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": "text-generation", "tags": ["gemma3", "unsloth", "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 | null | 268,098,176 | null | null | [
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"Gemma3ForCausalLM"
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"text"
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680be19991d4da0d8579cd09 | e68b0b111c49dac2 | dd84a2b1ff97657d | 250,002 | Unigram | null | true | true | true | true | 5 | Precompiled | Sequence | Metaspace | [
"Precompiled"
] | [
"WhitespaceSplit",
"Metaspace"
] | [
"Metaspace"
] | 28 | OrcaDB/e5-large | OrcaDB | null | 1,524 | 246,116 | False | 2025-04-25T19:25:13Z | 2025-06-16T15:54:17Z | sentence-transformers | 0 | 0 | [{"name": "multilingual-e5-large-instruct", "results": [{"task": {"type": "Classification"}, "dataset": {"type": "mteb/amazon_counterfactual", "name": "MTEB AmazonCounterfactualClassification (en)", "config": "en", "split": "test", "revision": "e8379541af4e31359cca9fbcf4b00f2671dba205"}, "metrics": [{"type": "accuracy"... | feature-extraction | {"parameters": {"F32": 559890432}, "total": 559890432} | [
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"... | null | {"architectures": ["XLMRobertaModel"], "model_type": "xlm-roberta", "tokenizer_config": {"bos_token": "<s>", "cls_token": "<s>", "eos_token": "</s>", "mask_token": "<mask>", "pad_token": "<pad>", "sep_token": "</s>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModel",
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} | {"language": ["multilingual", "af", "am", "ar", "as", "az", "be", "bg", "bn", "br", "bs", "ca", "cs", "cy", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "hu", "hy", "id", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "ku", "ky", "la", "l... | ## Multilingual-E5-large-instruct
[Multilingual E5 Text Embeddings: A Technical Report](https://arxiv.org/pdf/2402.05672).
Liang Wang, Nan Yang, Xiaolong Huang, Linjun Yang, Rangan Majumder, Furu Wei, arXiv 2024
This model has 24 layers and the embedding size is 1024.
## Usage
Below are examples to encode queries a... | null | [
"mit"
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621ffdc136468d709f17ad2c | 5ef9eb7677e33416 | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
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"dataset:quora",
"dataset:paws",
"arxiv:1910.13461",
"license:apache-2.0",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["BartForConditionalGeneration"], "model_type": "bart", "tokenizer_config": {"unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>", "sep_token": "</s>", "cls_token": "<s>", "pad_token": "<pad>", "mask_token": "<mask>"}} | {
"auto_model": "AutoModelForSeq2SeqLM",
"custom_class": null,
"pipeline_tag": "text2text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["quora", "paws"], "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": ["transformers", "bart", "paraphrase", "seq2seq"]} | # BART Paraphrase Model (Large)
A large BART seq2seq (text2text generation) model fine-tuned on 3 paraphrase datasets.
## Model description
The BART model was proposed in [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://arxiv.org/abs/1910.13461... | null | [
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66bbe267006ecb23c207c621 | 1418b90d00935b73 | 3e2cf73d00c28ef1 | 50,281 | BPE | 50,009 | true | true | true | true | 29 | NFC | ByteLevel | ByteLevel | [
"NFC"
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],
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# Model Summary
> OLMoE-1B-7B-Instruct is a Mixture-of-Experts LLM with 1B active and 7B total parameters released in September 2024 (0924) that has been adapted via SFT and DPO from [OLMoE-1B-7B](https://hf.co/allenai/OLMoE-1B-7B-0924). It yields state-of-th... | null | [
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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": "text-generation", "tags": ["gpt", "llm", "large language model", "h2o-llmstudio"], "... | <div style="width: 90%; max-width: 600px; margin: 0 auto; overflow: hidden; background-color: white">
<img src="https://cdn-uploads.huggingface.co/production/uploads/636d18755aaed143cd6698ef/LAzQu_f5WOX7vqKl4yDsY.png"
alt="Slightly cropped image"
style="width: 102%; height: 102%; object-fit: cov... | null | [
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621ffdc136468d709f17e20f | b5870c5809b523b5 | 783bbd5118eec6d5 | 101,122 | Unigram | null | true | true | true | true | 5 | Precompiled | Sequence | Metaspace | [
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] | 665 | moussaKam/mbarthez | moussaKam | null | 123,078 | 486,066 | False | 2022-03-02T23:29:05Z | 2024-02-20T14:35:26Z | transformers | 7 | 0 | null | fill-mask | null | [
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BARThez is pretrained by learning to reconstruct a corrupted input sentence. A corpus of 66GB of french raw text is used to carry out the pretraining. <br>
Unlike already existing BERT-based French language ... | null | [
"apache-2.0"
] | null | [
"fr"
] | null | null | null | [
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"ByteFallback",
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],
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} | 24,326 | 335,102 | manual | 2024-09-25T13:30:02Z | 2024-10-02T15:49:51Z | transformers | 215 | 0 | null | text-generation | {"parameters": {"BF16": 2614341888}, "total": 2614341888} | [
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] | null | {"architectures": ["Gemma2ForCausalLM"], "model_type": "gemma2", "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... | {
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} | {"base_model": "google/gemma-2-2b-it", "datasets": null, "eval_results": null, "language": ["ja"], "library_name": "transformers", "license": "gemma", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-generation", "tags": ["conversational"], "extra_gated_heading": "A... | null | null | [
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] |
669201cda0ef95d085071a4b | cedc024363b89a7b | 1a1bf14c163abd14 | 15,002 | BPE | 24,388 | true | true | true | true | 5 | Sequence | Metaspace | Metaspace | [
"Precompiled",
"Strip",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 667 | 5CD-AI/Vietnamese-Sentiment-visobert | 5CD-AI | null | 91,080 | 1,052,288 | False | 2024-07-13T04:25:49Z | 2024-07-26T17:32:57Z | transformers | 38 | 0 | null | text-classification | {"parameters": {"F32": 97568259}, "total": 97568259} | [
".gitattributes",
"README.md",
"config.json",
"model.safetensors",
"sentencepiece.bpe.model",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | d7738b85200272f10ba8b048d80236c8c582b581 | [
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"sentiment-analysis",
"social-listening",
"vi",
"text-embeddings-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["XLMRobertaForSequenceClassification"], "model_type": "xlm-roberta", "tokenizer_config": {"bos_token": "<s>", "cls_token": "<s>", "eos_token": "</s>", "mask_token": "<mask>", "pad_token": "<pad>", "sep_token": "</s>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["vi"], "library_name": "transformers", "license": null, "license_name": null, "license_link": null, "metrics": ["accuracy", "f1"], "model_name": null, "pipeline_tag": null, "tags": ["sentiment-analysis", "social-listening"]} | # 5CD-ViSoBERT for Vietnamese Sentiment Analysis
<b>YOU ARE TOO BORED AND TIRED OF HAVING TO BUILD A 🇻🇳 VIETNAMESE SENTIMENT ANALYSIS MODEL OVER AND OVER AGAIN?</b>
<b> BOOM! 🤯 NO WORRIES, WE'RE HERE FOR YOU =)) 🔥!</b>
This model is based on our pretrained [5CD-AI/visobert-14gb-corpus](https://huggingface.co/5CD... | null | null | null | [
"vi"
] | 97,568,259 | null | [
"accuracy",
"f1"
] | [
"AutoModelForSequenceClassification",
"XLMRobertaForSequenceClassification",
"xlm-roberta"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
65904ccba78a277803bfb761 | 88cd37e502d27a0a | 0e7bc0977f200d1c | 32,000 | BPE | 58,980 | true | false | true | true | 3 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 88 | jeonsworld/CarbonVillain-en-10.7B-v4 | jeonsworld | null | 13,610 | 301,837 | False | 2023-12-30T17:00:59Z | 2024-04-27T13:52:07Z | transformers | 7 | 0 | [{"name": "CarbonVillain-en-10.7B-v4", "results": [{"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "AI2 Reasoning Challenge (25-Shot)", "type": "ai2_arc", "config": "ARC-Challenge", "split": "test", "args": {"num_few_shot": 25}}, "metrics": [{"type": "acc_norm", "value": 71.25, "nam... | text-generation | {"parameters": {"F16": 10731524096}, "total": 10731524096} | [
".gitattributes",
"README.md",
"config.json",
"model-00001-of-00003.safetensors",
"model-00002-of-00003.safetensors",
"model-00003-of-00003.safetensors",
"model.safetensors.index.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer.model",
"tokenizer_config.json"
] | 57d6ad4d705d336aba228356683d9f221507440a | [
"transformers",
"safetensors",
"llama",
"text-generation",
"merge",
"slerp",
"conversational",
"en",
"license:cc-by-nc-sa-4.0",
"model-index",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": "<s>", "chat_template": "{% for message in messages %}{% if message['role'] == 'system' %}{% if message['content']%}{{'### System:\n' + message['content']+'\n\n'}}{% endif %}{% elif message['role'] == 'user' %}{{'### User:\... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"language": ["en"], "license": "cc-by-nc-sa-4.0", "tags": ["merge", "slerp"], "model-index": [{"name": "CarbonVillain-en-10.7B-v4", "results": [{"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "AI2 Reasoning Challenge (25-Shot)", "type": "ai2_arc", "config": "ARC-Challenge", "split"... | # CarbonVillain
**This is a model created without learning to oppose indiscriminate carbon emissions.**
This model is an experimental version created using [mergekit](https://github.com/cg123/mergekit).
- merge models
- [jeonsworld/CarbonVillain-en-10.7B-v1](https://huggingface.co/jeonsworld/CarbonVillain-en-10.... | null | [
"cc-by-nc-sa-4.0"
] | null | [
"en"
] | 10,731,524,096 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
658c3a047f5ad56419de37db | 88cd37e502d27a0a | 0e7bc0977f200d1c | 32,000 | BPE | 58,980 | true | false | true | true | 3 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 88 | OpenPipe/mistral-ft-optimized-1227 | OpenPipe | {
"models": [
{
"_id": "657494e1d0ed8f5761070faa",
"id": "Intel/neural-chat-7b-v3-3"
}
],
"relation": "finetune"
} | 9,371 | 275,425 | False | 2023-12-27T14:51:48Z | 2024-01-24T01:58:45Z | transformers | 82 | 0 | null | text-generation | {"parameters": {"BF16": 7241732096}, "total": 7241732096} | [
".gitattributes",
"README.md",
"config.json",
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"model.safetensors.index.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer.model",
"tokenizer_config.json"
] | dde431fc1638ad6f93970a21e55a6180b4068916 | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"base_model:Intel/neural-chat-7b-v3-3",
"base_model:finetune:Intel/neural-chat-7b-v3-3",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["MistralForCausalLM"], "model_type": "mistral", "tokenizer_config": {"bos_token": "<s>", "eos_token": "</s>", "pad_token": null, "unk_token": "<unk>", "use_default_system_prompt": false}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": ["teknium/OpenHermes-2.5-Mistral-7B", "Intel/neural-chat-7b-v3-3", "meta-math/MetaMath-Mistral-7B", "openchat/openchat-3.5-1210"], "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_n... | This model is intended to be a strong base suitable for downstream fine-tuning on a variety of tasks. Based on our internal evaluations, we believe it's one of the strongest models for most down-stream tasks. You can read more about our development and evaluation process [here](https://openpipe.ai/blog/mistral-7b-fine-... | null | [
"apache-2.0"
] | null | null | 7,241,732,096 | null | null | [
"AutoModelForCausalLM",
"mistral",
"MistralForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
66e97b326c3ab6aa78b0a7b4 | 848b60b6cd304b65 | ffc911e785abe60e | 151,665 | BPE | 151,387 | true | true | true | true | 22 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 15 | Qwen/Qwen2.5-1.5B-Instruct-GPTQ-Int4 | Qwen | {
"models": [
{
"_id": "66e98dd5899bdb384bd953b4",
"id": "Qwen/Qwen2.5-1.5B-Instruct"
}
],
"relation": "quantized"
} | 3,762 | 250,020 | False | 2024-09-17T12:50:58Z | 2024-10-09T12:27:18Z | transformers | 3 | 0 | null | text-generation | {"parameters": {"I32": 1310195712, "F16": 233518592}, "total": 1543714304} | [
".gitattributes",
"LICENSE",
"README.md",
"config.json",
"generation_config.json",
"merges.txt",
"model.safetensors",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | 4f5a0e31008e2966e8f787964bcc4e1105e03dc3 | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"chat",
"conversational",
"en",
"arxiv:2407.10671",
"base_model:Qwen/Qwen2.5-1.5B-Instruct",
"base_model:quantized:Qwen/Qwen2.5-1.5B-Instruct",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"... | null | {"architectures": ["Qwen2ForCausalLM"], "model_type": "qwen2", "quantization_config": {"bits": 4, "quant_method": "gptq"}, "tokenizer_config": {"bos_token": null, "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "Qwen/Qwen2.5-1.5B-Instruct", "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": "https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct-GPTQ-Int4/blob/main/LICENSE", "metrics": null, "model_name": null, "p... | # Qwen2.5-1.5B-Instruct-GPTQ-Int4
## Introduction
Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters. Qwen2.5 brings the following improvements upon Qwen2:
- Significantly... | null | [
"apache-2.0",
"https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct-GPTQ-Int4/blob/main/LICENSE"
] | null | [
"en"
] | 1,543,714,304 | null | null | [
"AutoModelForCausalLM",
"Qwen2ForCausalLM",
"qwen2"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
621ffdc136468d709f1794d5 | a4ea8f3be4c3e2c7 | 873dd6f00e997067 | 64,000 | BPE | 63,740 | false | true | true | true | 4 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 488 | aubmindlab/aragpt2-large | aubmindlab | null | 490 | 237,483 | False | 2022-03-02T23:29:05Z | 2024-05-29T09:19:07Z | transformers | 10 | 0 | null | text-generation | {"parameters": {"F32": 791621120, "U8": 37748736}, "total": 829369856} | [
".gitattributes",
"LICENSE",
"README.md",
"config.json",
"configuration_aragpt2.py",
"flax_model.msgpack",
"merges.txt",
"model.safetensors",
"modeling_aragpt2.py",
"pytorch_model.bin",
"runs/eval/events.out.tfevents.1608742294.tpu-mother",
"runs/eval_results.txt",
"runs/events.out.tfevents.... | b870598f32c15f993567a09c977cc0e5431d28f0 | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"safetensors",
"aragpt2",
"text-generation",
"custom_code",
"ar",
"dataset:wikipedia",
"dataset:Osian",
"dataset:arabic-billion-words",
"dataset:oscar",
"dataset:Assafir-private",
"arxiv:2012.15520",
"license:other",
"deploy:azure",
... | null | {"architectures": ["AraGPT2LMHeadModel"], "auto_map": {"AutoConfig": "configuration_aragpt2.AraGPT2Config", "AutoModelForCausalLM": "modeling_aragpt2.AraGPT2LMHeadModel", "AutoModel": "modeling_aragpt2.AraGPT2Model"}, "model_type": "aragpt2"} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": "modeling_aragpt2.AraGPT2LMHeadModel",
"pipeline_tag": "text-generation",
"processor": null
} | {"base_model": null, "datasets": ["wikipedia", "Osian", "arabic-billion-words", "oscar", "Assafir-private"], "eval_results": null, "language": "ar", "library_name": null, "license": "other", "license_name": "custom", "license_link": "https://github.com/aub-mind/arabert/blob/master/aragpt2/LICENSE", "metrics": null, "mo... | # Arabic GPT2
<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 | [
"other",
"custom",
"https://github.com/aub-mind/arabert/blob/master/aragpt2/LICENSE"
] | [
"wikipedia",
"Osian",
"arabic-billion-words",
"oscar",
"Assafir-private"
] | [
"ar"
] | 829,369,856 | null | null | [
"AutoModelForCausalLM",
"AraGPT2LMHeadModel",
"modeling_aragpt2.AraGPT2LMHeadModel",
"aragpt2"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
66374df5650f76807b257b98 | 41c90d1e256b6183 | c53d11be473ef3d2 | 128,257 | BPE | 280,147 | false | true | true | true | 257 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 668 | NousResearch/Hermes-2-Theta-Llama-3-8B | NousResearch | {
"models": [
{
"_id": "66303852b8d7220d89548ffc",
"id": "NousResearch/Hermes-2-Pro-Llama-3-8B"
}
],
"relation": "finetune"
} | 10,665 | 280,923 | False | 2024-05-05T09:14:29Z | 2024-09-08T08:11:20Z | transformers | 204 | 0 | [{"name": "Hermes-2-Pro-Llama-3-Instruct-8B-Merge", "results": []}] | text-generation | {"parameters": {"BF16": 8030261248}, "total": 8030261248} | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
"model-00001-of-00004.safetensors",
"model-00002-of-00004.safetensors",
"model-00003-of-00004.safetensors",
"model-00004-of-00004.safetensors",
"model.safetensors.index.json",
"special_tokens_map.json",
"tokenizer.json",
"... | 57a73110702e7b05ba3f39fef36297454c680725 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"Llama-3",
"instruct",
"finetune",
"chatml",
"DPO",
"RLHF",
"gpt4",
"synthetic data",
"distillation",
"function calling",
"json mode",
"axolotl",
"merges",
"conversational",
"en",
"dataset:teknium/OpenHermes-2.5",
"ba... | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": "<|begin_of_text|>", "chat_template": [{"name": "default", "template": "{{bos_token}}{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_g... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "NousResearch/Hermes-2-Pro-Llama-3-8B", "datasets": ["teknium/OpenHermes-2.5"], "eval_results": [], "language": ["en"], "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": "Hermes-2-Pro-Llama-3-Instruct-8B-Merge", "pipeline_tag": null,... | # - Hermes-2 Θ Llama-3 8B
## Model Description
Hermes-2 Θ (Theta) is the first experimental merged model released by [Nous Research](https://nousresearch.com/), in collaboration with Charles Goddard... | null | [
"apache-2.0"
] | [
"teknium/OpenHermes-2.5"
] | [
"en"
] | 8,030,261,248 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
685cc67be5d9c9e4be4f9cf4 | 7428ef9ae5ef868f | 67da9c236fa81925 | 151,669 | BPE | 151,387 | true | true | true | true | 26 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 47 | Skywork/Skywork-Reward-V2-Qwen3-0.6B | Skywork | {
"models": [
{
"_id": "680da718233834890aa01f51",
"id": "Qwen/Qwen3-0.6B"
}
],
"relation": "finetune"
} | 108,442 | 375,790 | False | 2025-06-26T04:03:07Z | 2025-07-06T18:16:12Z | transformers | 12 | 0 | null | text-classification | {"parameters": {"BF16": 596050944}, "total": 596050944} | [
".gitattributes",
"README.md",
"added_tokens.json",
"assets/skywork_logo.png",
"chat_template.jinja",
"config.json",
"merges.txt",
"model.safetensors",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | 8c14a4e9e6321deaf572544339b16b8d6bbe8886 | [
"transformers",
"safetensors",
"qwen3",
"text-classification",
"arxiv:2507.01352",
"base_model:Qwen/Qwen3-0.6B",
"base_model:finetune:Qwen/Qwen3-0.6B",
"license:apache-2.0",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["Qwen3ForSequenceClassification"], "model_type": "qwen3", "tokenizer_config": {"bos_token": null, "eos_token": "<|im_end|>", "pad_token": "<|vision_pad|>", "unk_token": null}, "chat_template_jinja": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0].role == 'system' %}\n ... | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"base_model": ["Qwen/Qwen3-0.6B"], "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": "text-classification", "tags": null} | null | null | [
"apache-2.0"
] | null | null | 596,050,944 | null | null | [
"Qwen3ForSequenceClassification",
"AutoModelForSequenceClassification",
"qwen3"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
644911e1d5d86def91ce19b0 | 3a732ab237c338af | c21f0239d1cf0f86 | 65,024 | BPE | 64,784 | false | true | false | true | 12 | null | Sequence | ByteLevel | null | [
"Punctuation",
"ByteLevel",
"Digits",
"Split"
] | [
"ByteLevel"
] | 45 | tiiuae/falcon-rw-7b | tiiuae | null | 296 | 236,264 | False | 2023-04-26T11:58:25Z | 2024-11-07T06:07:03Z | transformers | 17 | 0 | null | text-generation | {"parameters": {"BF16": 7782359040}, "total": 7782359040} | [
".gitattributes",
"README.md",
"config.json",
"configuration_falcon.py",
"generation_config.json",
"model-00001-of-00002.safetensors",
"model-00002-of-00002.safetensors",
"model.safetensors.index.json",
"modeling_falcon.py",
"pytorch_model-00001-of-00002.bin",
"pytorch_model-00002-of-00002.bin",... | 7cc3a731c0e3a7d3ece6500f3a018aa9432156ea | [
"transformers",
"pytorch",
"safetensors",
"falcon",
"text-generation",
"custom_code",
"en",
"dataset:tiiuae/falcon-refinedweb",
"arxiv:2306.01116",
"arxiv:2005.14165",
"arxiv:2108.12409",
"arxiv:2205.14135",
"license:apache-2.0",
"text-generation-inference",
"region:us"
] | null | {"architectures": ["FalconForCausalLM"], "auto_map": {"AutoConfig": "configuration_falcon.FalconConfig", "AutoModel": "modeling_falcon.FalconModel", "AutoModelForSequenceClassification": "modeling_falcon.FalconForSequenceClassification", "AutoModelForTokenClassification": "modeling_falcon.FalconForTokenClassification",... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["tiiuae/falcon-refinedweb"], "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, "inference": false} | # Falcon-RW-7B
**Falcon-RW-7B is a 7B parameters causal decoder-only model built by [TII](https://www.tii.ae) and trained on 350B tokens of [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb). It is made available under the Apache 2.0 license.**
See the 📓 [paper on arXiv](https://arxiv.org/abs/230... | null | [
"apache-2.0"
] | [
"tiiuae/falcon-refinedweb"
] | [
"en"
] | 7,782,359,040 | null | null | [
"AutoModelForCausalLM",
"falcon",
"FalconForCausalLM"
] | [
"text-generation"
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"doi:10.57967/hf/2786",
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] | null | {"architectures": ["ChameleonForConditionalGeneration"], "model_type": "chameleon", "tokenizer_config": {"bos_token": "<s>", "eos_token": "</s>", "pad_token": "<pad>", "sep_token": "<reserved08706>", "unk_token": "<unk>", "use_default_system_prompt": false}} | {
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Note: This is a huggingface-compatible version of the model converted by [leloy](https://huggingface.co/leloy). Currently only works on [this branch of the Transformers library](https://github.com/leloykun/transformers... | null | [
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] | null | {"architectures": ["GraniteForCausalLM"], "model_type": "granite", "tokenizer_config": {"bos_token": "<|end_of_text|>", "chat_template": "{%- if messages[0]['role'] == 'system' %}\n {%- set system_message = messages[0]['content'] %}\n {%- set loop_messages = messages[1:] %}\n{%- else %}\n {%- set system_messag... | {
"auto_model": "AutoModelForCausalLM",
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} | {"base_model": ["ibm-granite/granite-3.1-8b-instruct"], "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": "text-generation", "tags": ["language", "granite-3.2... | # Granite-3.2-8B-Instruct
**Model Summary:**
Granite-3.2-8B-Instruct is an 8-billion-parameter, long-context AI model fine-tuned for thinking capabilities. Built on top of [Granite-3.1-8B-Instruct](https://huggingface.co/ibm-granite/granite-3.1-8b-instruct), it has been trained using a mix of permissively licensed ope... | null | [
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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" />
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<hr>
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<p align="center">
<a href="https://github.com/01-ai">🐙 GitHub</a> •
<a href="https://discord.gg/hYUwWddeAu">👾 Discord</a> •
<a href="https://twitter... | null | [
"apache-2.0"
] | null | null | 6,061,035,520 | null | null | [
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"... | 25071b093c15c0d1cb2b2876c6deb621b764fcf5 | [
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"text-generation",
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"dataset:tatsu-lab/alpaca",
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"auto_model": "AutoModelForCausalLM",
"custom_class": null,
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} | {"base_model": null, "datasets": ["dmayhem93/ChatCombined", "tatsu-lab/alpaca", "nomic-ai/gpt4all_prompt_generations", "Dahoas/full-hh-rlhf", "jeffwan/sharegpt_vicuna", "HuggingFaceH4/databricks_dolly_15k"], "eval_results": null, "language": ["en"], "library_name": null, "license": "cc-by-nc-sa-4.0", "license_name": nu... | # StableLM-Tuned-Alpha
## Model Description
`StableLM-Tuned-Alpha` is a suite of 3B and 7B parameter decoder-only language models built on top of the `StableLM-Base-Alpha` models and further fine-tuned on various chat and instruction-following datasets.
## Usage
Get started chatting with `StableLM-Tuned-Alpha` by u... | null | [
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} | {"license": "apache-2.0", "tags": ["mteb", "sentence-transformers", "transformers", "Qwen2", "sentence-similarity"], "model-index": [{"name": "gte-qwen2-7B-instruct", "results": [{"task": {"type": "Classification"}, "dataset": {"name": "MTEB AmazonCounterfactualClassification (en)", "type": "mteb/amazon_counterfactual"... | ## gte-Qwen2-1.5B-instruct
**gte-Qwen2-1.5B-instruct** is the latest model in the gte (General Text Embedding) model family. The model is built on [Qwen2-1.5B](https://huggingface.co/Qwen/Qwen2-1.5B) LLM model and use the same training data and strategies as the [gte-Qwen2-7B-instruct](https://huggingface.co/Alibaba-N... | null | [
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"text"
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} | {"base_model": "PygmalionAI/mythalion-13b", "datasets": ["PygmalionAI/PIPPA", "Open-Orca/OpenOrca", "Norquinal/claude_multiround_chat_30k", "jondurbin/airoboros-gpt4-1.4.1", "databricks/databricks-dolly-15k"], "eval_results": null, "language": ["en"], "library_name": null, "license": "llama2", "license_name": null, "li... | <!-- header start -->
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<div style="width: auto; margin-left: auto; margin-right: auto">
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<d... | null | [
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"AutoModelForCausalLM",
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"text"
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"text"
] | [
"text"
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"BertNormalizer"
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"BertPreTokenizer"
] | [
"WordPiece"
] | 0 | OrcaDB/mxbai-large | OrcaDB | null | 1,578 | 239,911 | False | 2025-04-25T20:21:00Z | 2025-06-16T15:54:36Z | sentence-transformers | 0 | 0 | [{"name": "mxbai-angle-large-v1", "results": [{"task": {"type": "Classification"}, "dataset": {"type": "mteb/amazon_counterfactual", "name": "MTEB AmazonCounterfactualClassification (en)", "config": "en", "split": "test", "revision": "e8379541af4e31359cca9fbcf4b00f2671dba205"}, "metrics": [{"type": "accuracy", "value":... | feature-extraction | {"parameters": {"F32": 335141888}, "total": 335141888} | [
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<p align="center">
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"auto_model": "AutoModelForCausalLM",
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# Table of Contents
0. [TL;DR](#TL;DR)
1. [Model Details](#model-details)
2. [Usage](#usage)
3. [Training Details](#training-details)
4. [Evaluation](#evaluation)
# TL;DR
# Mo... | null | [
"other",
"falcon-mamba-7b-license",
"https://falconllm.tii.ae/falcon-mamba-7b-terms-and-conditions.html"
] | [
"tiiuae/falcon-refinedweb",
"HuggingFaceFW/fineweb-edu"
] | [
"en"
] | null | null | null | [
"AutoModelForCausalLM",
"falcon_mamba",
"FalconMambaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
676cf587995a2814663d1c0b | 848b60b6cd304b65 | ffc911e785abe60e | 151,665 | BPE | 151,387 | true | true | true | true | 22 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 15 | SakanaAI/TinySwallow-1.5B | SakanaAI | {
"models": [
{
"_id": "66e98dd5899bdb384bd953b4",
"id": "Qwen/Qwen2.5-1.5B-Instruct"
}
],
"relation": "finetune"
} | 2,995 | 364,968 | False | 2024-12-26T06:19:51Z | 2025-01-30T12:15:58Z | transformers | 35 | 0 | null | text-generation | {"parameters": {"BF16": 1543714304}, "total": 1543714304} | [
".gitattributes",
"LICENSE",
"README.md",
"added_tokens.json",
"config.json",
"generation_config.json",
"merges.txt",
"model.safetensors",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | 535d1322615c9f8171c3d60b10ef6955cc896fe9 | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"ja",
"arxiv:2501.16937",
"base_model:Qwen/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-1.5B-Instruct",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"regi... | null | {"architectures": ["Qwen2ForCausalLM"], "model_type": "qwen2", "tokenizer_config": {"bos_token": null, "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are Qwen, created by Aliba... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": ["Qwen/Qwen2.5-1.5B-Instruct"], "datasets": null, "eval_results": null, "language": ["ja"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-generation", "tags": null} | # TinySwallow-1.5B
🤗 [Models](https://huggingface.co/SakanaAI) | 📚 [Paper](https://arxiv.org/abs/2501.16937) | 📝 [Blog](https://sakana.ai/taid-jp/) | 🐦 [Twitter](https://twitter.com/SakanaAILabs)
**TinySwallow-1.5B** is a Japanese compact language model created through *TAID (Temporally Adaptive Interpolated Dist... | null | [
"apache-2.0"
] | null | [
"ja"
] | 1,543,714,304 | null | null | [
"AutoModelForCausalLM",
"Qwen2ForCausalLM",
"qwen2"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
65dc3441f386d08eb03ccc81 | 88cd37e502d27a0a | 0e7bc0977f200d1c | 32,000 | BPE | 58,980 | true | false | true | true | 3 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 88 | mixtao/MixTAO-7Bx2-MoE-v8.1 | mixtao | null | 19,983 | 328,957 | False | 2024-02-26T06:48:33Z | 2024-10-15T07:44:49Z | transformers | 55 | 0 | [{"name": "MixTAO-7Bx2-MoE-v8.1", "results": [{"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "AI2 Reasoning Challenge (25-Shot)", "type": "ai2_arc", "config": "ARC-Challenge", "split": "test", "args": {"num_few_shot": 25}}, "metrics": [{"type": "acc_norm", "value": 73.81, "name": "... | text-generation | {"parameters": {"BF16": 12879138816}, "total": 12879138816} | [
".gitattributes",
"README.md",
"config.json",
"model-00001-of-00013.safetensors",
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... | 71282580b69b2f9263a502eec0deeafbdfca4089 | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"moe",
"conversational",
"license:apache-2.0",
"model-index",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["MixtralForCausalLM"], "model_type": "mixtral", "tokenizer_config": {"bos_token": "<s>", "chat_template": "{% for message in messages %}\n{% if message['role'] == 'user' %}\n{{ '### Instruction:\n' + message['content'] }}\n{% elif message['role'] == 'system' %}\n{{ '### System:\n' + message['content'... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"license": "apache-2.0", "tags": ["moe"], "model-index": [{"name": "MixTAO-7Bx2-MoE-v8.1", "results": [{"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "AI2 Reasoning Challenge (25-Shot)", "type": "ai2_arc", "config": "ARC-Challenge", "split": "test", "args": {"num_few_shot": 25}}, ... | # MixTAO-7Bx2-MoE
MixTAO-7Bx2-MoE is a Mixture of Experts (MoE).
This model is mainly used for large model technology experiments, and increasingly perfect iterations will eventually create high-level large language models.
### Prompt Template (Alpaca)
```
### Instruction:
<prompt> (without the <>)
### Response:
```
... | null | [
"apache-2.0"
] | null | null | 12,879,138,816 | null | null | [
"mixtral",
"MixtralForCausalLM",
"AutoModelForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
66eb41635fcdca258baa7d40 | 848b60b6cd304b65 | ffc911e785abe60e | 151,665 | BPE | 151,387 | true | true | true | true | 22 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 15 | unsloth/Qwen2.5-1.5B-Instruct | unsloth | {
"models": [
{
"_id": "66e98dd5899bdb384bd953b4",
"id": "Qwen/Qwen2.5-1.5B-Instruct"
}
],
"relation": "finetune"
} | 16,850 | 351,412 | False | 2024-09-18T21:08:51Z | 2025-02-06T02:09:01Z | transformers | 8 | 0 | null | text-generation | {"parameters": {"BF16": 1543714304}, "total": 1543714304} | [
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"README.md",
"added_tokens.json",
"config.json",
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"merges.txt",
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"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | b2e27ed8774d78eb2ee474cfe99d2d3b5fae11e5 | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"unsloth",
"conversational",
"en",
"arxiv:2407.10671",
"base_model:Qwen/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-1.5B-Instruct",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"deploy:az... | null | {"architectures": ["Qwen2ForCausalLM"], "model_type": "qwen2", "tokenizer_config": {"bos_token": null, "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are Qwen, created by Aliba... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "Qwen/Qwen2.5-1.5B-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"]} | # Finetune Llama 3.1, Gemma 2, Mistral 2-5x faster with 70% less memory via Unsloth!
We have a Qwen 2.5 (all model sizes) [free Google Colab Tesla T4 notebook](https://colab.research.google.com/drive/1Kose-ucXO1IBaZq5BvbwWieuubP7hxvQ?usp=sharing).
Also a [Qwen 2.5 conversational style notebook](https://colab.research.... | null | [
"apache-2.0"
] | null | [
"en"
] | 1,543,714,304 | null | null | [
"AutoModelForCausalLM",
"Qwen2ForCausalLM",
"qwen2"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
6744928143f10c4fe91d1c03 | 84d05c6a5fead2ab | 893a35bd62abcf4d | 32,002 | BPE | 58,980 | true | false | true | true | 5 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 61 | trl-internal-testing/tiny-LlavaNextForConditionalGeneration | trl-internal-testing | null | 94,720 | 548,162 | False | 2024-11-25T15:06:41Z | 2026-02-09T07:41:59Z | transformers | 0 | 0 | null | image-text-to-text | {"parameters": {"BF16": 2695872}, "total": 2695872} | [
".gitattributes",
"README.md",
"added_tokens.json",
"chat_template.jinja",
"config.json",
"generation_config.json",
"model.safetensors",
"preprocessor_config.json",
"processor_config.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer.model",
"tokenizer_config.json"
] | 1b30f278154fc7486c547bf69ab34e5c20543706 | [
"transformers",
"safetensors",
"llava_next",
"image-text-to-text",
"trl",
"conversational",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["LlavaNextForConditionalGeneration"], "model_type": "llava_next", "tokenizer_config": {"bos_token": "<s>", "eos_token": "</s>", "pad_token": "<pad>", "unk_token": "<unk>", "use_default_system_prompt": false}, "chat_template_jinja": "{% for message in messages %}{% if message['role'] == 'system' %}{{ ... | {
"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": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["trl"]} | # Tiny LlavaNextForConditionalGeneration
This is a minimal model built for unit tests in the [TRL](https://github.com/huggingface/trl) library. | null | null | null | null | 2,695,872 | null | null | [
"AutoModelForImageTextToText",
"LlavaNextForConditionalGeneration",
"llava_next"
] | [
"image-text-to-text"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"text"
] |
687ea37ca84c7caff224f7f5 | 7428ef9ae5ef868f | 67da9c236fa81925 | 151,669 | BPE | 151,387 | true | true | true | true | 26 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 47 | lmstudio-community/Qwen3-235B-A22B-Instruct-2507-MLX-4bit | lmstudio-community | {
"models": [
{
"_id": "687de260234339fed21e768a",
"id": "Qwen/Qwen3-235B-A22B-Instruct-2507"
}
],
"relation": "quantized"
} | 3,571 | 253,708 | False | 2025-07-21T20:30:52Z | 2025-07-21T20:40:04Z | transformers | 1 | 0 | null | text-generation | {"parameters": {"BF16": 7347449344, "U32": 29386604544}, "total": 235093634560} | [
".gitattributes",
"README.md",
"added_tokens.json",
"chat_template.jinja",
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"merges.txt",
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"model-00003-of-00026.safetensors",
"model-00004-of-00026.safetensors",
"model-00005-of-0002... | 10761c281c9dd6836b41fd625da2475d18ade4c8 | [
"transformers",
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"qwen3_moe",
"text-generation",
"mlx",
"conversational",
"base_model:Qwen/Qwen3-235B-A22B-Instruct-2507",
"base_model:quantized:Qwen/Qwen3-235B-A22B-Instruct-2507",
"license:apache-2.0",
"endpoints_compatible",
"4-bit",
"region:us"
] | null | {"architectures": ["Qwen3MoeForCausalLM"], "model_type": "qwen3_moe", "quantization_config": {"bits": 4}, "tokenizer_config": {"bos_token": null, "eos_token": "<|im_end|>", "pad_token": "<|endoftext|>", "unk_token": null, "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0].role... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "Qwen/Qwen3-235B-A22B-Instruct-2507", "datasets": null, "eval_results": null, "language": null, "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": "https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507/blob/main/LICENSE", "metrics": null, "model_name": null... | null | null | [
"apache-2.0",
"https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507/blob/main/LICENSE"
] | null | null | 235,093,634,560 | null | null | [
"qwen3_moe",
"AutoModelForCausalLM",
"Qwen3MoeForCausalLM"
] | [
"text-generation"
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"text"
] | [
"text"
] | [
"text"
] |
66b67e73621b62a4c02c1541 | 1e0a2493163c0c63 | 6622292dca275a2d | 128,256 | BPE | 280,147 | false | true | true | true | 256 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 18 | Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2 | Orenguteng | null | 3,873 | 369,961 | False | 2024-08-09T20:39:15Z | 2024-09-25T02:49:53Z | transformers | 281 | 4 | [{"name": "Llama-3.1-8B-Lexi-Uncensored-V2", "results": [{"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "IFEval (0-Shot)", "type": "HuggingFaceH4/ifeval", "args": {"num_few_shot": 0}}, "metrics": [{"type": "inst_level_strict_acc and prompt_level_strict_acc", "value": 77.92, "name":... | text-generation | {"parameters": {"BF16": 8030261248}, "total": 8030261248} | [
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"... | f4617caeabd21f1820ac89bd125c80eda70901a7 | [
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"conversational",
"license:llama3.1",
"model-index",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": "<|begin_of_text|>", "chat_template": "{{- bos_token }}\n{%- if custom_tools is defined %}\n {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message is defined %}\n {%- set tools_in_user_messag... | {
"auto_model": "AutoModelForCausalLM",
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VERSION 2 Update Notes:
---
- More compliant
- Smarter
- For best response, use this system prompt (feel free to expand upon it as you wish):
Think step by step with a logical reasoning and intellect... | null | [
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64d4b803abf475a808759c19 | 6949732424a8d297 | 8bd7d71767538b82 | 32,000 | BPE | 61,249 | true | false | true | true | 3 | Sequence | null | Sequence | [
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"id": "pankajmathur/orca_mini_v3_7b"
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"relation": "quantized"
} | 19 | 232,440 | False | 2023-08-10T10:12:19Z | 2023-09-27T12:45:35Z | transformers | 11 | 0 | null | text-generation | {"parameters": {"I32": 6476005376, "F16": 263770112}, "total": 6739775488} | [
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<!-- 200823 -->
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