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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 -->
## Usage (Sentence-Transformers)
Using this model becomes easy when y... | null | null | null | null | null | null | null | [
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## 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 72B Qwen2 m... | null | [
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<!-- 200823 -->
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<p style="margin-bottom: 0; margin-top: 0;">
<strong>See <a href="https://huggingface.co/collections/unsloth/gemma-3-67d12b7e8816ec6efa7e4e5b">our collection</a> for all versions of Gemma 3 including GGUF, 4-bit & 16-bit formats.</strong>
</p>
<p style="margin-bottom: 0;">
<em><a href="https://docs.... | null | [
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The ToxiGuardrail is a fine-tuned version of [RoBERTa](https://huggingface.co/roberta-base) that can be used to score the toxicity and potential harm of a sentence.
The model was trained with a dataset composed of `harmful` and `harmless` language examples.
## Details
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The problem is formulated as multilabel classification with the following classes:
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<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 | [
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You should only use this repository if you have been granted access to the model by filling out [this form](https://docs.google.com/forms/d/e/1FAIpQLSfqNECQnMkycAp2jP4Z9TFX0cGR4uf7b_fBxjY_OjhJILlKGA/v... | null | [
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} | {"base_model": "Qwen/Qwen3-Coder-30B-A3B-Instruct", "datasets": null, "eval_results": null, "language": null, "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": "https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct/blob/main/LICENSE", "metrics": null, "model_name": null, ... | null | null | [
"apache-2.0",
"https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct/blob/main/LICENSE"
] | null | null | 30,532,122,624 | null | null | [
"qwen3_moe",
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"text-generation"
] | [
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] | [
"text"
] | [
"text"
] |
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"id": "distilbert/distilroberta-base"
}
],
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} | 37,854 | 1,137,595 | False | 2022-03-02T23:29:05Z | 2025-04-11T08:47:21Z | sentence-transformers | 1 | 0 | null | text-ranking | {"parameters": {"I64": 514, "F32": 82119169}, "total": 82119683} | [
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"README.md",
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"onnx/model_O4.onnx",
"onnx/model_qint8_arm64.o... | f62e7a4b20b97195c2868e53ec59126df5eac743 | [
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"transformers",
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"en",
"dataset:sentence-transformers/quora-duplicates",
"base_model:distilbert/distilroberta-base",
"base_model:quantized:distilbert/distilroberta... | 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": ["distilbert/distilroberta-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-ran... | null | null | [
"apache-2.0"
] | [
"sentence-transformers/quora-duplicates"
] | [
"en"
] | 82,119,683 | null | null | [
"roberta",
"AutoModelForSequenceClassification",
"RobertaForSequenceClassification"
] | [
"text-ranking",
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
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"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 350 | naver-clova-ix/donut-base-finetuned-cord-v2 | naver-clova-ix | null | 19,649 | 1,178,699 | False | 2022-07-19T01:53:24Z | 2022-08-13T08:28:13Z | transformers | 119 | 0 | null | image-to-text | null | [
".gitattributes",
".gitignore",
"README.md",
"added_tokens.json",
"config.json",
"preprocessor_config.json",
"pytorch_model.bin",
"sentencepiece.bpe.model",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | 8003d433113256b4ce3a0f5bf604b29ff78a7451 | [
"transformers",
"pytorch",
"vision-encoder-decoder",
"image-text-to-text",
"donut",
"image-to-text",
"vision",
"arxiv:2111.15664",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["VisionEncoderDecoderModel"], "model_type": "vision-encoder-decoder", "tokenizer_config": {"bos_token": "<s>", "cls_token": "<s>", "eos_token": "</s>", "mask_token": {"__type": "AddedToken", "content": "<mask>", "lstrip": true, "normalized": true, "rstrip": false, "single_word": false}, "pad_token": ... | {
"auto_model": "AutoModelForImageTextToText",
"custom_class": null,
"pipeline_tag": "image-text-to-text",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["donut", "image-to-text", "vision"]} | # Donut (base-sized model, fine-tuned on CORD)
Donut model fine-tuned on CORD. It was introduced in the paper [OCR-free Document Understanding Transformer](https://arxiv.org/abs/2111.15664) by Geewok et al. and first released in [this repository](https://github.com/clovaai/donut).
Disclaimer: The team releasing Donu... | null | [
"mit"
] | null | null | null | null | null | [
"AutoModelForImageTextToText",
"vision-encoder-decoder",
"VisionEncoderDecoderModel"
] | [
"image-text-to-text",
"image-to-text"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"text"
] |
651fb0a64376ae05d62c18dd | 5b0c1a1193420318 | 98eb4ad3932ce4a1 | 32,100 | BPE | 31,740 | false | true | true | true | 102 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 326 | mesolitica/emotion-analysis-nanot5-small-malaysian-cased | mesolitica | null | 374 | 1,002,563 | False | 2023-10-06T07:00:54Z | 2023-10-06T07:01:31Z | transformers | 0 | 0 | null | text-classification | {"parameters": {"F32": 41625350}, "total": 41625350} | [
".gitattributes",
"added_tokens.json",
"config.json",
"merges.txt",
"model.safetensors",
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"tokenizer.json",
"tokenizer_config.json",
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] | 63d2ab0734afbef58b902c0d75a7d9d1ea49b090 | [
"transformers",
"safetensors",
"t5",
"text-classification",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["T5ForSequenceClassification"], "model_type": "t5", "tokenizer_config": {"bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "pad_token": "<s>", "unk_token": "<|endoftext|>"}} | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | null | null | null | null | null | null | 41,625,350 | null | null | [
"t5",
"AutoModelForSequenceClassification",
"T5ForSequenceClassification"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
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621ffdc136468d709f1802dd | d82c716f01e57f52 | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 62 | sentence-transformers/nli-distilroberta-base-v2 | sentence-transformers | null | 25,438 | 1,062,671 | False | 2022-03-02T23:29:05Z | 2025-03-06T13:28:12Z | sentence-transformers | 1 | 0 | null | sentence-similarity | {"parameters": {"I64": 514, "F32": 82118400}, "total": 82118914} | [
".gitattributes",
"1_Pooling/config.json",
"README.md",
"config.json",
"config_sentence_transformers.json",
"flax_model.msgpack",
"merges.txt",
"model.safetensors",
"modules.json",
"onnx/model.onnx",
"onnx/model_O1.onnx",
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"onnx/model_O3.onnx",
"onnx/model_O4.onnx",
... | cc35a0bfb6251228a6fb8c797bca5fef0ece3c1d | [
"sentence-transformers",
"pytorch",
"tf",
"jax",
"onnx",
"safetensors",
"openvino",
"roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["RobertaModel"], "model_type": "roberta", "tokenizer_config": {"unk_token": {"content": "<unk>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "bos_token": {"content": "<s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": t... | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": "sentence-transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "sentence-similarity", "tags": ["sentence-transformers", "feature-extraction",... | # sentence-transformers/nli-distilroberta-base-v2
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | null | [
"apache-2.0"
] | null | null | 82,118,914 | null | null | [
"roberta",
"AutoModel",
"RobertaModel"
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"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
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"Strip",
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 351 | meta-llama/Prompt-Guard-86M | meta-llama | null | 24,567 | 1,161,786 | manual | 2024-07-21T19:18:13Z | 2025-11-12T21:27:00Z | transformers | 318 | 0 | null | text-classification | {"parameters": {"F32": 278811651}, "total": 278811651} | [
".gitattributes",
"LICENSE",
"Prompt-Guard-20240715180000.md5",
"README.md",
"USE_POLICY.md",
"config.json",
"model.safetensors",
"prompt_guard_visual.png",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | 1209add6ca7d9c1d815171b8e5571587fe3e7b03 | [
"transformers",
"safetensors",
"deberta-v2",
"text-classification",
"facebook",
"meta",
"pytorch",
"llama",
"llama-3",
"en",
"license:llama3.1",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["DebertaV2ForSequenceClassification"], "model_type": "deberta-v2", "tokenizer_config": {"bos_token": "[CLS]", "cls_token": "[CLS]", "eos_token": "[SEP]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en"], "library_name": null, "license": "llama3.1", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-classification", "tags": ["facebook", "meta", "pytorch", "llama", "llama-3"], "widget": [{"... | null | null | [
"llama3.1"
] | null | [
"en"
] | 278,811,651 | null | null | [
"AutoModelForSequenceClassification",
"deberta-v2",
"DebertaV2ForSequenceClassification"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
663238fcd5677e328794061c | 803e3c8528c55bf1 | a1f1bd381dcb9329 | 128,256 | BPE | 280,147 | false | true | true | true | 256 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 352 | DeepMount00/Llama-3-8b-Ita | DeepMount00 | {
"models": [
{
"_id": "661f97d48e7f3438386f755d",
"id": "meta-llama/Meta-Llama-3-8B"
}
],
"relation": "finetune"
} | 13,982 | 1,056,933 | False | 2024-05-01T12:43:40Z | 2025-06-11T10:04:06Z | transformers | 31 | 0 | [{"name": "Llama-3-8b-Ita", "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": 75.3, "name": "strict accuracy"... | 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",
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"model-00004-of-00004.safetensors",
"model.safetensors.index.json",
"special_tokens_map.json",
"tokenizer.json",
"... | f95f9e25647bab95bf7966d8dbef2a6c48b5557a | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"it",
"en",
"dataset:DeepMount00/llm_ita_ultra",
"base_model:meta-llama/Meta-Llama-3-8B",
"base_model:finetune:meta-llama/Meta-Llama-3-8B",
"license:llama3",
"model-index",
"text-generation-inference",
"endpoints_... | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": "<|begin_of_text|>", "chat_template": "{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|e... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "meta-llama/Meta-Llama-3-8B", "datasets": ["DeepMount00/llm_ita_ultra"], "language": ["it", "en"], "library_name": "transformers", "license": "llama3", "model-index": [{"name": "Llama-3-8b-Ita", "results": [{"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "IFEval (0-Sh... | ---
**💡 Found this resource helpful?** Creating and maintaining open source AI models and datasets requires significant computational resources. If this work has been valuable to you, consider [supporting my research](https://buymeacoffee.com/michele.montebovi) to help me continue building tools that benefit the enti... | null | [
"llama3"
] | [
"DeepMount00/llm_ita_ultra"
] | [
"it",
"en"
] | 8,030,261,248 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
621ffdc136468d709f17d81a | de0f98cd504e94de | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 317 | lidiya/bart-large-xsum-samsum | lidiya | null | 14,631 | 1,025,317 | False | 2022-03-02T23:29:05Z | 2023-03-16T22:44:01Z | transformers | 38 | 0 | [{"name": "bart-large-xsum-samsum", "results": [{"task": {"name": "Abstractive Text Summarization", "type": "abstractive-text-summarization"}, "dataset": {"name": "SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization", "type": "samsum"}, "metrics": [{"name": "Validation ROUGE-1", "type": "rou... | summarization | {"parameters": {"F32": 406340696}, "total": 406340696} | [
".gitattributes",
"README.md",
"config.json",
"merges.txt",
"model.safetensors",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | c8b48dc25a40cb404609646bff222a7d26731367 | [
"transformers",
"pytorch",
"safetensors",
"bart",
"text2text-generation",
"seq2seq",
"summarization",
"en",
"dataset:samsum",
"license:apache-2.0",
"model-index",
"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"
} | {"datasets": ["samsum"], "language": "en", "license": "apache-2.0", "tags": ["bart", "seq2seq", "summarization"], "widget": [{"text": "Hannah: Hey, do you have Betty's number?\nAmanda: Lemme check\nAmanda: Sorry, can't find it.\nAmanda: Ask Larry\nAmanda: He called her last time we were at the park together\nHannah: I ... | ## `bart-large-xsum-samsum`
This model was obtained by fine-tuning `facebook/bart-large-xsum` on [Samsum](https://huggingface.co/datasets/samsum) dataset.
## Usage
```python
from transformers import pipeline
summarizer = pipeline("summarization", model="lidiya/bart-large-xsum-samsum")
conversation = '''Hannah: Hey, do... | null | [
"apache-2.0"
] | [
"samsum"
] | [
"en"
] | 406,340,696 | null | null | [
"bart",
"BartForConditionalGeneration",
"AutoModelForSeq2SeqLM"
] | [
"text2text-generation",
"summarization"
] | [
"text"
] | [
"text"
] | [
"text"
] |
6576a87588805b3ba1386ea0 | e23b6bdbe047bb7e | 8bd7d71767538b82 | 32,000 | BPE | 61,249 | true | false | true | true | 3 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 46 | TinyLlama/TinyLlama-1.1B-intermediate-step-1195k-token-2.5T | TinyLlama | null | 994 | 1,000,120 | False | 2023-12-11T06:13:09Z | 2023-12-29T06:04:50Z | transformers | 53 | 0 | null | text-generation | {"parameters": {"F32": 1100048384}, "total": 1100048384} | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
"model.safetensors",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer.model",
"tokenizer_config.json"
] | 706bc2851338c4a89bb212e96ff23f9cc1ebde1d | [
"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"en",
"dataset:cerebras/SlimPajama-627B",
"dataset:bigcode/starcoderdata",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": {"__type": "AddedToken", "content": "<s>", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false}, "eos_token": {"__type": "AddedToken", "content": "</s>", "lstrip": false, "normalized": false, "rstrip... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["cerebras/SlimPajama-627B", "bigcode/starcoderdata"], "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} | <div align="center">
# TinyLlama-1.1B
</div>
https://github.com/jzhang38/TinyLlama
The TinyLlama project aims to **pretrain** a **1.1B Llama model on 3 trillion tokens**. With some proper optimization, we can achieve this within a span of "just" 90 days using 16 A100-40G GPUs 🚀🚀. The training has started on 2023-0... | null | [
"apache-2.0"
] | [
"cerebras/SlimPajama-627B",
"bigcode/starcoderdata"
] | [
"en"
] | 1,100,048,384 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
65761a82eb4b4e0bcf5a7294 | ac285ea8ed6ed749 | 0cdc2927ce4cf866 | 32,002 | BPE | 61,249 | true | false | true | true | 5 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 353 | llava-hf/vip-llava-7b-hf | llava-hf | null | 1,330 | 997,627 | False | 2023-12-10T20:07:30Z | 2025-01-27T11:27:25Z | transformers | 16 | 0 | null | image-text-to-text | {"parameters": {"F16": 7080214528}, "total": 7080214528} | [
".gitattributes",
"README.md",
"added_tokens.json",
"chat_template.json",
"config.json",
"generation_config.json",
"model-00001-of-00003.safetensors",
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"model-00003-of-00003.safetensors",
"model.safetensors.index.json",
"preprocessor_config.json",
"processor_... | d060fe36b7e550f6884342432561ea6afaea8482 | [
"transformers",
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"vipllava",
"image-text-to-text",
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"conversational",
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"arxiv:2312.00784",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["VipLlavaForConditionalGeneration"], "model_type": "vipllava", "processor_config": {"chat_template": "{% for message in messages %}{% if message['role'] == 'user' %}{{ '###Human: '}}{% elif message['role'] == 'assistant' %}{{ '###' + message['role'].title() + ': '}}{% endif %}{# Render all images fir... | {
"auto_model": "AutoModelForImageTextToText",
"custom_class": null,
"pipeline_tag": "image-text-to-text",
"processor": "AutoProcessor"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en"], "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "image-text-to-text", "tags": ["vision", "image-text-to-text"], "arxiv": 2312.00784} | # VipLLaVA Model Card
Below is the model card of VipLlava model 7b, which is copied from the original Llava model card that you can find [here](https://huggingface.co/liuhaotian/llava-v1.5-13b).
Check out also the Google C... | null | null | null | [
"en"
] | 7,080,214,528 | null | null | [
"AutoModelForImageTextToText",
"vipllava",
"VipLlavaForConditionalGeneration"
] | [
"image-text-to-text"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"text"
] |
6649c024e74442492218cf6a | 38805d8eca7d7c72 | af1fea6666013553 | 128,256 | BPE | 280,147 | false | true | true | true | 256 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 354 | openbmb/MiniCPM-Llama3-V-2_5 | openbmb | null | 68,449 | 1,270,784 | False | 2024-05-19T09:02:28Z | 2025-01-15T10:28:12Z | transformers | 1,411 | 2 | null | image-text-to-text | {"parameters": {"F16": 8537092336}, "total": 8537092336} | [
".gitattributes",
"README.md",
"assets/MiniCPM-Llama3-V-2.5-benchmark.png",
"assets/MiniCPM-Llama3-V-2.5-peformance.png",
"assets/gif_cases/1-4.gif",
"assets/gif_cases/meal_plan.gif",
"assets/gif_cases/ticket.gif",
"assets/minicpmv-llama3-v2.5/case_OCR_en.png",
"assets/minicpmv-llama3-v2.5/case_comp... | fd7f352fac0e06d0d818b23f98e3ec8c64267a57 | [
"transformers",
"safetensors",
"minicpmv",
"feature-extraction",
"minicpm-v",
"vision",
"ocr",
"custom_code",
"image-text-to-text",
"conversational",
"multilingual",
"dataset:openbmb/RLAIF-V-Dataset",
"region:us"
] | null | {"architectures": ["MiniCPMV"], "auto_map": {"AutoConfig": "configuration_minicpm.MiniCPMVConfig", "AutoModel": "modeling_minicpmv.MiniCPMV", "AutoModelForCausalLM": "modeling_minicpmv.MiniCPMV"}, "model_type": "minicpmv", "tokenizer_config": {"bos_token": "<|begin_of_text|>", "chat_template": "{% set loop_messages = m... | {
"auto_model": "AutoModel",
"custom_class": "modeling_minicpmv.MiniCPMV",
"pipeline_tag": "feature-extraction",
"processor": null
} | {"base_model": null, "datasets": ["openbmb/RLAIF-V-Dataset"], "eval_results": null, "language": ["multilingual"], "library_name": "transformers", "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "image-text-to-text", "tags": ["minicpm-v", "vision", "ocr",... | <h1>A GPT-4V Level Multimodal LLM on Your Phone</h1>
[GitHub](https://github.com/OpenBMB/MiniCPM-V) | [Demo](https://huggingface.co/spaces/openbmb/MiniCPM-Llama3-V-2_5) | <a href="https://github.com/OpenBMB/MiniCPM-V/blob/main/docs/wechat.md" target="_blank"> WeChat</a>
## News <!-- omit in toc -->
#### 📌 Pinned
... | null | null | [
"openbmb/RLAIF-V-Dataset"
] | [
"multilingual"
] | 8,537,092,336 | null | null | [
"AutoModel",
"MiniCPMV",
"minicpmv",
"modeling_minicpmv.MiniCPMV"
] | [
"image-text-to-text",
"feature-extraction"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"text",
"embeddings"
] |
659bea3d539c808e8484cb42 | 7a9379003b337737 | 9ff9438e6447a43c | 32,000 | Unigram | null | true | true | true | true | 3 | Sequence | Metaspace | Metaspace | [
"Lowercase",
"Replace",
"Replace",
"Strip",
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 27 | google/siglip-base-patch16-256 | google | null | 121,672 | 1,279,600 | False | 2024-01-08T12:27:41Z | 2024-09-26T08:21:52Z | transformers | 6 | 0 | null | zero-shot-image-classification | {"parameters": {"F32": 203202050}, "total": 203202050} | [
".gitattributes",
"README.md",
"config.json",
"model.safetensors",
"preprocessor_config.json",
"special_tokens_map.json",
"spiece.model",
"tokenizer.json",
"tokenizer_config.json"
] | b078df89e446d623010d890864d4207fe6399f61 | [
"transformers",
"safetensors",
"siglip",
"zero-shot-image-classification",
"vision",
"arxiv:2303.15343",
"arxiv:2209.06794",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["SiglipModel"], "model_type": "siglip", "tokenizer_config": {"eos_token": "</s>", "pad_token": "</s>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModelForZeroShotImageClassification",
"custom_class": null,
"pipeline_tag": "zero-shot-image-classification",
"processor": "AutoProcessor"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["vision"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_image... | # SigLIP (base-sized model)
SigLIP model pre-trained on WebLi at resolution 256x256. It was introduced in the paper [Sigmoid Loss for Language Image Pre-Training](https://arxiv.org/abs/2303.15343) by Zhai et al. and first released in [this repository](https://github.com/google-research/big_vision).
Disclaimer: The t... | null | [
"apache-2.0"
] | null | null | 203,202,050 | null | null | [
"AutoModelForZeroShotImageClassification",
"siglip",
"SiglipModel"
] | [
"zero-shot-image-classification"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"logits"
] |
67449274b27e90952b736b13 | 35f8a93a304f6b78 | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 6 | trl-internal-testing/tiny-BartModel | trl-internal-testing | null | 31,057 | 1,137,336 | False | 2024-11-25T15:06:28Z | 2025-12-19T19:12:23Z | transformers | 0 | 0 | null | feature-extraction | {"parameters": {"F32": 3106968}, "total": 3106968} | [
".gitattributes",
"README.md",
"config.json",
"merges.txt",
"model.safetensors",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | cb0a45b3f2560e9e087fdbaf539d7643f960ffca | [
"transformers",
"safetensors",
"bart",
"feature-extraction",
"trl",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["BartModel"], "model_type": "bart", "tokenizer_config": {"bos_token": "<s>", "cls_token": "<s>", "eos_token": "</s>", "mask_token": "<mask>", "pad_token": "<pad>", "sep_token": "</s>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": "transformers", "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["trl"]} | # Tiny BartModel
This is a minimal model built for unit tests in the [TRL](https://github.com/huggingface/trl) library. | null | null | null | null | 3,106,968 | null | null | [
"AutoModel",
"bart",
"BartModel"
] | [
"feature-extraction"
] | [
"multimodal"
] | [
"text"
] | [
"embeddings"
] |
621ffdc136468d709f1802f2 | d82c716f01e57f52 | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 62 | sentence-transformers/roberta-base-nli-stsb-mean-tokens | sentence-transformers | null | 5,857 | 1,015,367 | False | 2022-03-02T23:29:05Z | 2025-03-06T13:24:33Z | sentence-transformers | 0 | 0 | null | sentence-similarity | {"parameters": {"I64": 514, "F32": 124645632}, "total": 124646146} | [
".gitattributes",
"1_Pooling/config.json",
"README.md",
"added_tokens.json",
"config.json",
"config_sentence_transformers.json",
"flax_model.msgpack",
"merges.txt",
"model.safetensors",
"modules.json",
"onnx/model.onnx",
"onnx/model_O1.onnx",
"onnx/model_O2.onnx",
"onnx/model_O3.onnx",
"... | 637539d0f7482cdd043040c1b59a7270b5e0ecc6 | [
"sentence-transformers",
"pytorch",
"tf",
"jax",
"onnx",
"safetensors",
"openvino",
"roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["RobertaModel"], "model_type": "roberta", "tokenizer_config": {"unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>", "sep_token": "</s>", "cls_token": "<s>", "pad_token": "<pad>", "mask_token": "<mask>"}} | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": "sentence-transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "sentence-similarity", "tags": ["sentence-transformers", "feature-extraction",... | **⚠️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: [SBERT.net - Pretrained Models](https://www.sbert.net/docs/pretrained_models.html)**
# sentence-transformers/roberta-base-nli-stsb-mean-tokens
This is a [sent... | null | [
"apache-2.0"
] | null | null | 124,646,146 | null | null | [
"roberta",
"AutoModel",
"RobertaModel"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
66f4970900d2de868719bfe2 | 1e0a2493163c0c63 | 6622292dca275a2d | 128,256 | BPE | 280,147 | false | true | true | true | 256 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 18 | mlx-community/Llama-3.2-3B-Instruct | mlx-community | {
"models": [
{
"_id": "66eaef786865fea1324edb5d",
"id": "meta-llama/Llama-3.2-3B-Instruct"
}
],
"relation": "finetune"
} | 866 | 989,904 | False | 2024-09-25T23:04:41Z | 2024-09-25T23:53:13Z | transformers | 9 | 0 | null | text-generation | {"parameters": {"F16": 3212749824}, "total": 3212749824} | [
".gitattributes",
"README.md",
"config.json",
"model-00001-of-00002.safetensors",
"model-00002-of-00002.safetensors",
"model.safetensors.index.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | 96e4e2ca7926fab2eb52b1cdea46a934af630f0a | [
"transformers",
"safetensors",
"llama",
"text-generation",
"facebook",
"meta",
"pytorch",
"llama-3",
"mlx",
"conversational",
"en",
"de",
"fr",
"it",
"pt",
"hi",
"es",
"th",
"base_model:meta-llama/Llama-3.2-3B-Instruct",
"base_model:finetune:meta-llama/Llama-3.2-3B-Instruct",
... | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": "<|begin_of_text|>", "chat_template": "{{- bos_token }}\n{%- if custom_tools is defined %}\n {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message is defined %}\n {%- set tools_in_user_messag... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "meta-llama/Llama-3.2-3B-Instruct", "datasets": null, "eval_results": null, "language": ["en", "de", "fr", "it", "pt", "hi", "es", "th"], "library_name": "transformers", "license": "llama3.2", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-generatio... | # mlx-community/Llama-3.2-3B-Instruct
The Model [mlx-community/Llama-3.2-3B-Instruct](https://huggingface.co/mlx-community/Llama-3.2-3B-Instruct) was converted to MLX format from [meta-llama/Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) using mlx-lm version **0.18.2**.
## Use with ml... | null | [
"llama3.2"
] | null | [
"en",
"de",
"fr",
"it",
"pt",
"hi",
"es",
"th"
] | 3,212,749,824 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
64b698b6807ef63cbb5ab159 | 71140a663949f258 | 8a01c02d4d0a401c | 119,547 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 9 | suno/bark-small | suno | null | 49,879 | 1,148,233 | False | 2023-07-18T13:50:46Z | 2023-11-10T10:11:12Z | transformers | 255 | 0 | null | text-to-speech | null | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
"pytorch_model.bin",
"speaker_embeddings/announcer_coarse_prompt.npy",
"speaker_embeddings/announcer_fine_prompt.npy",
"speaker_embeddings/announcer_semantic_prompt.npy",
"speaker_embeddings/de_speaker_0_coarse_prompt.npy",
"s... | 1dbd7a128513b8ae4a4e2130fed57b7ac9da5bcd | [
"transformers",
"pytorch",
"bark",
"text-to-audio",
"audio",
"text-to-speech",
"en",
"de",
"es",
"fr",
"hi",
"it",
"ja",
"ko",
"pl",
"pt",
"ru",
"tr",
"zh",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["BarkModel"], "model_type": "bark", "tokenizer_config": {"cls_token": "[CLS]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
"auto_model": "AutoModelForTextToWaveform",
"custom_class": null,
"pipeline_tag": "text-to-audio",
"processor": "AutoProcessor"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en", "de", "es", "fr", "hi", "it", "ja", "ko", "pl", "pt", "ru", "tr", "zh"], "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-to-speech", "tags": ["ba... | # Bark
Bark is a transformer-based text-to-audio model created by [Suno](https://www.suno.ai).
Bark can generate highly realistic, multilingual speech as well as other audio - including music,
background noise and simple sound effects. The model can also produce nonverbal
communications like laughing, sighing and c... | null | [
"mit"
] | null | [
"en",
"de",
"es",
"fr",
"hi",
"it",
"ja",
"ko",
"pl",
"pt",
"ru",
"tr",
"zh"
] | null | null | null | [
"AutoModelForTextToWaveform",
"BarkModel",
"bark"
] | [
"text-to-speech",
"text-to-audio"
] | [
"text",
"audio"
] | [
"text"
] | [
"audio"
] |
651f1cdc372c0f3dc7f0bc49 | fc29ccd738d51f6b | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 1 | TaylorAI/gte-tiny | TaylorAI | null | 49,001 | 1,318,331 | False | 2023-10-05T20:30:20Z | 2023-10-07T05:20:49Z | sentence-transformers | 140 | 0 | [{"name": "gte_tiny", "results": [{"task": {"type": "Classification"}, "dataset": {"type": "mteb/amazon_counterfactual", "name": "MTEB AmazonCounterfactualClassification (en)", "config": "en", "split": "test", "revision": "e8379541af4e31359cca9fbcf4b00f2671dba205"}, "metrics": [{"type": "accuracy", "value": 71.76119402... | sentence-similarity | null | [
".gitattributes",
"1_Pooling/config.json",
"README.md",
"added_tokens.json",
"config.json",
"config_sentence_transformers.json",
"model.safetensors",
"modules.json",
"onnx/model.onnx",
"onnx/model_optimized.onnx",
"onnx/model_quantized.onnx",
"pytorch_model.bin",
"sentence_bert_config.json",... | 4cc5e73d86a67c601897257b467187234aa3bca3 | [
"sentence-transformers",
"pytorch",
"onnx",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"mteb",
"model-index",
"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"
} | {"pipeline_tag": "sentence-similarity", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers", "mteb"], "model-index": [{"name": "gte_tiny", "results": [{"task": {"type": "Classification"}, "dataset": {"name": "MTEB AmazonCounterfactualClassification (en)", "type": "mteb/amazon_c... | # {gte-tiny}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
It is distilled from `thenlper/gte-small`, with comparable (slightly worse) performance at around half the s... | null | null | null | null | null | null | null | [
"BertModel",
"AutoModel",
"bert"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
6583f217bef870ffa49ab6bf | 827f6451ef99df30 | dd84a2b1ff97657d | 250,002 | Unigram | null | true | true | true | true | 5 | Precompiled | Sequence | Metaspace | [
"Precompiled"
] | [
"WhitespaceSplit",
"Metaspace"
] | [
"Metaspace"
] | 355 | danielheinz/e5-base-sts-en-de | danielheinz | null | 53,693 | 1,188,754 | False | 2023-12-21T08:06:47Z | 2024-01-14T17:37:54Z | transformers | 17 | 0 | [{"name": "e5-base-sts-en-de", "results": [{"task": {"type": "semantic textual similarity"}, "dataset": {"type": "stsb_multi_mt", "name": "stsb_multi_mt"}, "metrics": [{"type": "spearmanr", "value": 0.904, "verified": false}]}]}] | feature-extraction | {"parameters": {"F32": 278043648}, "total": 278043648} | [
".gitattributes",
"1_Pooling/config.json",
"README.md",
"config.json",
"config_sentence_transformers.json",
"model.safetensors",
"modules.json",
"sentence_bert_config.json",
"sentencepiece.bpe.model",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | a24243f57b37ce45fe5c9e5afd8904c03abe9757 | [
"transformers",
"safetensors",
"xlm-roberta",
"feature-extraction",
"de",
"dataset:deutsche-telekom/ger-backtrans-paraphrase",
"dataset:paws-x",
"dataset:stsb_multi_mt",
"license:mit",
"model-index",
"text-embeddings-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["XLMRobertaModel"], "model_type": "xlm-roberta", "tokenizer_config": {"bos_token": "<s>", "cls_token": "<s>", "eos_token": "</s>", "mask_token": "<mask>", "pad_token": "<pad>", "sep_token": "</s>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"datasets": ["deutsche-telekom/ger-backtrans-paraphrase", "paws-x", "stsb_multi_mt"], "language": ["de"], "license": "mit", "model-index": [{"name": "e5-base-sts-en-de", "results": [{"task": {"type": "semantic textual similarity"}, "dataset": {"name": "stsb_multi_mt", "type": "stsb_multi_mt"}, "metrics": [{"type": "sp... | **INFO**: The model is being continuously updated.
The model is a [multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) model fine-tuned with the task of semantic textual similarity in mind.
## Model Training
The model has been fine-tuned on the German subsets of the following datasets:
- [Germ... | null | [
"mit"
] | [
"deutsche-telekom/ger-backtrans-paraphrase",
"paws-x",
"stsb_multi_mt"
] | [
"de"
] | 278,043,648 | null | null | [
"XLMRobertaModel",
"AutoModel",
"xlm-roberta"
] | [
"feature-extraction"
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"multimodal"
] | [
"text"
] | [
"embeddings"
] |
66d55cfabb76fb26ee8018a8 | 34093184d10f76a9 | 35b001f3a491f9f9 | 151,646 | BPE | 151,387 | true | true | true | true | 3 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
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"ByteLevel"
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## Table of Contents
1. [Model Summary](##model-summary)
2. [Use](##use)
3. [Limitations](##limitations)
4. [Training](##training)
5. [License](##license)
6. [Citation](##citation)
## Model Summary
The LLaVA-Video models are 7/72B parameter models trained on [LLaVA-Video-178K](https://huggin... | null | [
"apache-2.0"
] | [
"lmms-lab/LLaVA-OneVision-Data",
"lmms-lab/LLaVA-Video-178K"
] | [
"en"
] | 8,030,348,832 | null | [
"accuracy"
] | [
"AutoModelForCausalLM",
"LlavaQwenForCausalLM",
"llava"
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"video-text-to-text",
"text-generation"
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"text",
"vision"
] | [
"text",
"image"
] | [
"text"
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"Split",
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"ByteLevel"
] | 356 | nvidia/Llama-3_1-Nemotron-51B-Instruct | nvidia | null | 898 | 988,524 | False | 2024-09-22T07:24:59Z | 2025-07-06T15:52:27Z | transformers | 209 | 0 | null | text-generation | null | [
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"text"
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6627fb0ed73c212b7ed647dc | 41a000f2f9024404 | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 13 | mr8488/distilroberta-finetuned-financial-news-sentiment-analysis | mr8488 | null | 8 | 978,208 | False | 2024-04-23T18:16:46Z | 2024-04-23T18:51:37Z | transformers | 2 | 0 | [{"name": "distilRoberta-financial-sentiment", "results": [{"task": {"name": "Text Classification", "type": "text-classification"}, "dataset": {"name": "financial_phrasebank", "type": "financial_phrasebank", "args": "sentences_allagree"}, "metrics": [{"name": "Accuracy", "type": "accuracy", "value": 0.9923008849557522,... | text-classification | {"parameters": {"F32": 82120707}, "total": 82120707} | [
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] | null | {"architectures": ["RobertaForSequenceClassification"], "model_type": "roberta", "tokenizer_config": {"bos_token": "<s>", "cls_token": "<s>", "eos_token": "</s>", "mask_token": "<mask>", "pad_token": "<pad>", "sep_token": "</s>", "unk_token": "<unk>"}} | {
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should probably proofread and complete it, then remove this comment. -->
<div style="text-align:center;width:250px;height:250px;">
<img src="https://huggingface.co/mrm8488/distilroberta-finetuned-fina... | null | [
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"text"
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621ffdc136468d709f17def0 | 492a9f4f584c75aa | dd84a2b1ff97657d | 250,002 | Unigram | null | true | true | true | true | 5 | Precompiled | Sequence | Metaspace | [
"Precompiled"
] | [
"WhitespaceSplit",
"Metaspace"
] | [
"Metaspace"
] | 5 | microsoft/infoxlm-base | microsoft | null | 40,390 | 1,213,694 | False | 2022-03-02T23:29:05Z | 2021-08-04T11:42:14Z | transformers | 8 | 0 | null | fill-mask | null | [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"sentencepiece.bpe.model",
"tokenizer.json"
] | c67f260d5635cdeef35864fd2ce369d24eca1b34 | [
"transformers",
"pytorch",
"xlm-roberta",
"fill-mask",
"arxiv:2007.07834",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["XLMRobertaForMaskedLM"], "model_type": "xlm-roberta"} | {
"auto_model": "AutoModelForMaskedLM",
"custom_class": null,
"pipeline_tag": "fill-mask",
"processor": "AutoTokenizer"
} | null | # InfoXLM
**InfoXLM** (NAACL 2021, [paper](https://arxiv.org/pdf/2007.07834.pdf), [repo](https://github.com/microsoft/unilm/tree/master/infoxlm), [model](https://huggingface.co/microsoft/infoxlm-base)) InfoXLM: An Information-Theoretic Framework for Cross-Lingual Language Model Pre-Training.
**MD5**
```
b9d214025837... | null | null | null | null | null | null | null | [
"AutoModelForMaskedLM",
"xlm-roberta",
"XLMRobertaForMaskedLM"
] | [
"fill-mask"
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"text"
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"text"
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621ffdc136468d709f17b61e | 53ecf467267e2824 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 0 | google/electra-base-generator | google | null | 1,294 | 980,531 | False | 2022-03-02T23:29:05Z | 2024-02-29T10:20:15Z | transformers | 8 | 0 | null | fill-mask | null | [
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] | null | {"architectures": ["ElectraForMaskedLM"], "model_type": "electra", "tokenizer_config": {}} | {
"auto_model": "AutoModelForMaskedLM",
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} | {"base_model": null, "datasets": null, "eval_results": null, "language": "en", "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null, "thumbnail": "https://huggingface.co/front/thumbnails/google.png"} | ## ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators
**ELECTRA** is a new method for self-supervised language representation learning. It can be used to pre-train transformer networks using relatively little compute. ELECTRA models are trained to distinguish "real" input tokens vs "fake" inp... | null | [
"apache-2.0"
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66ecec52fcc1c455f81a91a4 | 848b60b6cd304b65 | ffc911e785abe60e | 151,665 | BPE | 151,387 | true | true | true | true | 22 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
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"ByteLevel"
] | 15 | Qwen/Qwen2.5-Coder-7B-Instruct-AWQ | Qwen | {
"models": [
{
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}
],
"relation": "quantized"
} | 653,427 | 3,751,801 | False | 2024-09-20T03:30:26Z | 2024-11-18T12:54:24Z | transformers | 21 | 1 | null | text-generation | {"parameters": {"I32": 6525288448, "F16": 1090328064}, "total": 7615616512} | [
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"auto_model": "AutoModelForCausalLM",
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"pipeline_tag": "text-generation",
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} | {"base_model": ["Qwen/Qwen2.5-Coder-7B-Instruct"], "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": "https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct/blob/main/LICENSE", "metrics": null, "model_name": null, "p... | # Qwen2.5-Coder-7B-Instruct-AWQ
## Introduction
Qwen2.5-Coder is the latest series of Code-Specific Qwen large language models (formerly known as CodeQwen). As of now, Qwen2.5-Coder has covered six mainstream model sizes, 0.5, 1.5, 3, 7, 14, 32 billion parameters, to meet the needs of different developers. Qwen2.5-Co... | null | [
"apache-2.0",
"https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct/blob/main/LICENSE"
] | null | [
"en"
] | 7,615,616,512 | null | null | [
"AutoModelForCausalLM",
"Qwen2ForCausalLM",
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"text"
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621ffdc136468d709f17dae4 | 634a9186a6003172 | 5f9f736c7dc6fbfa | 28,996 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 8 | m3hrdadfi/typo-detector-distilbert-en | m3hrdadfi | null | 114 | 1,004,813 | False | 2022-03-02T23:29:05Z | 2021-06-16T16:14:20Z | transformers | 11 | 0 | null | token-classification | null | [
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## Dataset Information
For this specific task, I used [NeuSpell](https://github.com/neuspell/neuspell) corpus as my raw data.
## Evaluation
The following tables summarize the scores obtained by model overall and per each class.
| # | precision | recall | f1-score | support |
|:------... | null | null | null | [
"en"
] | null | null | null | [
"AutoModelForTokenClassification",
"distilbert",
"DistilBertForTokenClassification"
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"token-classification"
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6525743a774dcb91035915d9 | 5afd7d59d2aa5e01 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 125 | llmrails/ember-v1 | llmrails | null | 15,077 | 1,054,902 | False | 2023-10-10T15:56:42Z | 2024-08-21T04:49:13Z | sentence-transformers | 63 | 0 | [{"name": "ember_v1", "results": [{"task": {"type": "Classification"}, "dataset": {"type": "mteb/amazon_counterfactual", "name": "MTEB AmazonCounterfactualClassification (en)", "config": "en", "split": "test", "revision": "e8379541af4e31359cca9fbcf4b00f2671dba205"}, "metrics": [{"type": "accuracy", "value": 76.05970149... | feature-extraction | {"parameters": {"F32": 335141888}, "total": 335141888} | [
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This model has been trained on an extensive corpus of text pairs that encompass a broad spectrum of domains, including finance, science, medicine, law, and various others. During the training process, we incorporated techniques derived from the [RetroMAE](https://arxiv.org/abs/2205.120... | null | [
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"en"
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"multimodal"
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"logits",
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65c2936f1bcc84e918c02927 | 925bc1b18c2f0c84 | 963b5f887d151645 | 49,154 | BPE | 48,891 | false | true | false | true | 21 | null | Sequence | ByteLevel | null | [
"Digits",
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] | [
"ByteLevel"
] | 357 | codesage/codesage-large | codesage | null | 58 | 966,593 | False | 2024-02-06T20:15:43Z | 2024-12-28T22:18:24Z | transformers | 24 | 0 | null | null | null | [
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] | null | {"architectures": ["CodeSage"], "auto_map": {"AutoConfig": "config_codesage.CodeSageConfig", "AutoTokenizer": "tokenization_codesage.CodeSageTokenizer", "AutoModel": "modeling_codesage.CodeSageModel", "AutoModelForMaskedLM": "modeling_codesage.CodeSageForMaskedLM", "AutoModelForSequenceClassification": "modeling_codesa... | {
"auto_model": "CodeSage",
"custom_class": null,
"pipeline_tag": null,
"processor": null
} | {"base_model": null, "datasets": ["bigcode/the-stack-dedup"], "eval_results": null, "language": ["code"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | ## CodeSage-Large
### Updates
* [12/2024] <span style="color:blue">We are excited to announce the release of the CodeSage V2 model family with largely improved performance and flexible embedding dimensions!</span> Please check out our [models](https://huggingface.co/codesage) and [blogpost](https://code-representation... | null | [
"apache-2.0"
] | [
"bigcode/the-stack-dedup"
] | [
"code"
] | null | null | null | [
"CodeSage"
] | [
null
] | null | null | null |
685ffb0a9c4d599d2a98bc2c | b38a3bb0e1e173c8 | 302e80cadffaf147 | 151,343 | BPE | 318,088 | false | true | true | true | 14 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 358 | zai-org/GLM-4.1V-9B-Thinking | zai-org | {
"models": [
{
"_id": "67f3bd8e0dad3893b2eee15e",
"id": "zai-org/GLM-4-9B-0414"
}
],
"relation": "finetune"
} | 426,320 | 2,429,430 | False | 2025-06-28T14:24:10Z | 2025-10-25T12:52:10Z | transformers | 773 | 0 | null | image-text-to-text | {"parameters": {"BF16": 10292777472}, "total": 10292777472} | [
".gitattributes",
"README.md",
"chat_template.jinja",
"config.json",
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"model.safetensors.index.json",
"notebook.ipynb",
"prep... | 9e9a4c5e94f4a095c353f4152d520a2644a553b2 | [
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"glm4v",
"image-text-to-text",
"reasoning",
"conversational",
"en",
"zh",
"arxiv:2507.01006",
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"base_model:finetune:zai-org/GLM-4-9B-0414",
"license:mit",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["Glm4vForConditionalGeneration"], "model_type": "glm4v", "tokenizer_config": {"eos_token": "<|endoftext|>", "pad_token": "<|endoftext|>"}, "chat_template_jinja": "[gMASK]<sop>\n{%- for msg in messages %}\n {%- if msg.role == 'system' %}\n<|system|>\n{{ msg.content }}\n {%- elif msg.role == 'use... | {
"auto_model": "AutoModelForImageTextToText",
"custom_class": null,
"pipeline_tag": "image-text-to-text",
"processor": "AutoProcessor"
} | {"base_model": ["zai-org/GLM-4-9B-0414"], "datasets": null, "eval_results": null, "language": ["en", "zh"], "library_name": "transformers", "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "image-text-to-text", "tags": ["reasoning"]} | null | null | [
"mit"
] | null | [
"en",
"zh"
] | 10,292,777,472 | null | null | [
"AutoModelForImageTextToText",
"glm4v",
"Glm4vForConditionalGeneration"
] | [
"image-text-to-text"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"text"
] |
621ffdc136468d709f180307 | da443ce982f05a4c | dd84a2b1ff97657d | 250,002 | Unigram | null | true | true | true | true | 5 | Sequence | Metaspace | Metaspace | [
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 51 | sentence-transformers/xlm-r-distilroberta-base-paraphrase-v1 | sentence-transformers | null | 2,280 | 972,707 | False | 2022-03-02T23:29:05Z | 2025-03-06T13:21:23Z | sentence-transformers | 1 | 0 | null | sentence-similarity | {"parameters": {"I64": 514, "F32": 278043648}, "total": 278044162} | [
".gitattributes",
"1_Pooling/config.json",
"README.md",
"config.json",
"config_sentence_transformers.json",
"model.safetensors",
"modules.json",
"onnx/model.onnx",
"onnx/model_O1.onnx",
"onnx/model_O2.onnx",
"onnx/model_O3.onnx",
"onnx/model_O4.onnx",
"onnx/model_qint8_arm64.onnx",
"onnx/m... | da45da457830903b91ec80da60611e6ba5846008 | [
"sentence-transformers",
"pytorch",
"tf",
"onnx",
"safetensors",
"openvino",
"xlm-roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["XLMRobertaModel"], "model_type": "xlm-roberta", "tokenizer_config": {"bos_token": "<s>", "eos_token": "</s>", "sep_token": "</s>", "cls_token": "<s>", "unk_token": "<unk>", "pad_token": "<pad>", "mask_token": {"content": "<mask>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": ... | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": "sentence-transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "sentence-similarity", "tags": ["sentence-transformers", "feature-extraction",... | # sentence-transformers/xlm-r-distilroberta-base-paraphrase-v1
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becom... | null | [
"apache-2.0"
] | null | null | 278,044,162 | null | null | [
"XLMRobertaModel",
"AutoModel",
"xlm-roberta"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
6591dd37a02954c9825f00f1 | e23b6bdbe047bb7e | 8bd7d71767538b82 | 32,000 | BPE | 61,249 | true | false | true | true | 3 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 46 | TheBloke/TinyLlama-1.1B-Chat-v1.0-GPTQ | TheBloke | {
"models": [
{
"_id": "658fb85235c41262d661dc48",
"id": "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
}
],
"relation": "quantized"
} | 53,947 | 1,177,056 | False | 2023-12-31T21:29:27Z | 2023-12-31T21:33:52Z | transformers | 14 | 0 | null | text-generation | {"parameters": {"I32": 968884224, "BF16": 131164160, "F16": 394240}, "total": 1100442624} | [
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"special_tokens_map.json",
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"tokenizer_config.json"
] | 9d4580af0f21bccafd762dcc50d0c7bac6273584 | [
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"conversational",
"en",
"dataset:cerebras/SlimPajama-627B",
"dataset:bigcode/starcoderdata",
"dataset:OpenAssistant/oasst_top1_2023-08-25",
"base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"base_model:quantized:TinyLlama/TinyLlama-1.1B-C... | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "quantization_config": {"bits": 4, "quant_method": "gptq"}, "tokenizer_config": {"bos_token": "<s>", "chat_template": "{% for message in messages %}\n{% if message['role'] == 'user' %}\n{{ '<|user|>\n' + message['content'] + eos_token }}\n{% elif message['r... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "datasets": ["cerebras/SlimPajama-627B", "bigcode/starcoderdata", "OpenAssistant/oasst_top1_2023-08-25"], "eval_results": null, "language": ["en"], "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name... | <!-- markdownlint-disable MD041 -->
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content:... | null | [
"apache-2.0"
] | [
"cerebras/SlimPajama-627B",
"bigcode/starcoderdata",
"OpenAssistant/oasst_top1_2023-08-25"
] | [
"en"
] | 1,100,442,624 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
621ffdc136468d709f179e86 | 71140a663949f258 | 8a01c02d4d0a401c | 119,547 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 9 | castorini/mdpr-question-nq | castorini | null | 251 | 958,335 | False | 2022-03-02T23:29:05Z | 2021-08-20T15:07:57Z | transformers | 2 | 0 | null | feature-extraction | null | [
".gitattributes",
"config.json",
"pytorch_model.bin",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | b560ea82c60118c04b388a339f1c089c2a720c70 | [
"transformers",
"pytorch",
"dpr",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["DPRQuestionEncoder"], "model_type": "dpr", "tokenizer_config": {}} | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | null | null | null | null | null | null | null | null | null | [
"AutoModel",
"dpr",
"DPRQuestionEncoder"
] | [
"feature-extraction"
] | [
"multimodal"
] | [
"text"
] | [
"embeddings"
] |
66a66ece20c98ff27c393394 | 9ed94cd3800ce449 | 52bca1c62bcdabb1 | 8,192 | BPE | 8,109 | false | true | false | false | 4 | null | ByteLevel | null | null | [
"ByteLevel"
] | null | 359 | HadrianSecurity/subwiz | HadrianSecurity | null | 220,205 | 3,450,750 | False | 2024-07-28T16:16:14Z | 2025-10-28T16:37:27Z | transformers | 2 | 0 | null | null | null | [
".gitattributes",
"README.md",
"config.json",
"model.pt",
"model.py",
"model_v2.pt",
"tokenizer.json",
"tokenizer_v2.json"
] | 9a2c505d0312ad6938b27d9b4338020fe37883e8 | [
"transformers",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | {} | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": null,
"processor": null
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | Model parameters for Hadrian Security's [subwiz](https://github.com/hadriansecurity/subwiz) tool.
See [model.py](https://github.com/hadriansecurity/subwiz/blob/main/subwiz/model.py) for inferencing code. | null | [
"mit"
] | null | null | null | null | null | [
"AutoModel"
] | [
null
] | null | null | null |
644a09a8111b3bf6878a18e1 | 4d769ab76e27dfe1 | ef4806e2ade9022f | 32,100 | Unigram | null | true | true | true | true | 103 | Sequence | Metaspace | Metaspace | [
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 360 | Salesforce/safety-flan-t5-base | Salesforce | null | 43 | 953,967 | False | 2023-04-27T05:35:36Z | 2025-01-14T18:57:22Z | transformers | 8 | 0 | null | null | null | [
".gitattributes",
"README.md",
"config.json",
"optimizer.bin",
"pytorch_model.bin",
"random_states_0.pkl",
"random_states_1.pkl",
"scheduler.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | f1e458b226b2bbcf9ea847bcdda3798f91f03216 | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["T5ForConditionalGeneration"], "model_type": "t5", "tokenizer_config": {"eos_token": "</s>", "pad_token": "<pad>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModelForSeq2SeqLM",
"custom_class": null,
"pipeline_tag": "text2text-generation",
"processor": "AutoTokenizer"
} | null | # Model Details
## Model Description
- **Model type:** Language model
- **License:** CC BY-NC
- **Related Models:** [All FLAN-T5 Checkpoints](https://huggingface.co/models?search=flan-t5)
- **Resources for more information:**
- [GitHub Repo](https://github.com/salesforce/AuditNLG)
# Usage
Find below some example... | null | null | null | null | null | null | null | [
"t5",
"T5ForConditionalGeneration",
"AutoModelForSeq2SeqLM"
] | [
"text2text-generation"
] | null | null | null |
6645bc3b7eebfc1c76ce062a | 8b5c8462c7a92e57 | 4a714e39d07d2a5f | 50,325 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 361 | ilsilfverskiold/classify-news-category-iptc | ilsilfverskiold | {
"models": [
{
"_id": "629f7c9310066215dce89650",
"id": "KB/bert-base-swedish-cased"
}
],
"relation": "finetune"
} | 79 | 952,372 | False | 2024-05-16T07:56:43Z | 2024-05-16T10:45:14Z | transformers | 2 | 0 | [{"name": "news_category_classification", "results": []}] | text-classification | {"parameters": {"F32": 124703248}, "total": 124703248} | [
".gitattributes",
"README.md",
"all_results.json",
"config.json",
"eval_results.json",
"merges.txt",
"model.safetensors",
"runs/May16_07-34-56_add8af2633c6/events.out.tfevents.1715844897.add8af2633c6.851.0",
"runs/May16_07-34-56_add8af2633c6/events.out.tfevents.1715845950.add8af2633c6.851.1",
"run... | 9673acac4152f78a79b3b9a6de39619af88249f1 | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:KB/bert-base-swedish-cased",
"base_model:finetune:KB/bert-base-swedish-cased",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["BertForSequenceClassification"], "model_type": "bert", "tokenizer_config": {"cls_token": "[CLS]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"base_model": "KB/bert-base-swedish-cased", "datasets": null, "eval_results": [], "language": null, "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": ["accuracy", "f1", "precision", "recall"], "model_name": "news_category_classification", "pipeline_tag": null, "tags": ["gene... | <!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# News Category Classification for IPTC NewsCodes
This model is a fine-tuned version of [KB/bert-base-swedish-cased](https://huggin... | null | null | null | null | 124,703,248 | null | [
"accuracy",
"f1",
"precision",
"recall"
] | [
"BertForSequenceClassification",
"bert",
"AutoModelForSequenceClassification"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
66bae7d1115bf3ff56f2ab8e | 7679cd48bb106d89 | ebd7f7bf136c21b9 | 128,256 | BPE | 280,147 | false | true | true | true | 256 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 362 | NousResearch/Hermes-3-Llama-3.1-405B | NousResearch | {
"models": [
{
"_id": "6695cd12321386ed51d7bc22",
"id": "meta-llama/Llama-3.1-405B"
}
],
"relation": "finetune"
} | 135 | 952,511 | False | 2024-08-13T04:57:53Z | 2024-10-08T08:01:44Z | transformers | 265 | 2 | [{"name": "Hermes-3-Llama-3.1-405B", "results": []}] | text-generation | null | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
"model-00001-of-00191.safetensors",
"model-00002-of-00191.safetensors",
"model-00003-of-00191.safetensors",
"model-00004-of-00191.safetensors",
"model-00005-of-00191.safetensors",
"model-00006-of-00191.safetensors",
"model-0... | b9fe8ede1f5075f171b2c0a235e20ae4aba33767 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"Llama-3",
"instruct",
"finetune",
"chatml",
"gpt4",
"synthetic data",
"distillation",
"function calling",
"json mode",
"axolotl",
"roleplaying",
"chat",
"conversational",
"en",
"arxiv:2408.11857",
"base_model:meta-llam... | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": "<|begin_of_text|>", "chat_template": [{"name": "default", "template": "{{bos_token}}{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system\nYou are a helpful assistant.<|... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "meta-llama/Meta-Llama-3.1-405B", "datasets": null, "eval_results": [], "language": ["en"], "library_name": "transformers", "license": "llama3", "license_name": null, "license_link": null, "metrics": null, "model_name": "Hermes-3-Llama-3.1-405B", "pipeline_tag": null, "tags": ["Llama-3", "instruct", "fin... | # Hermes 3 - Llama-3.1 405B
## Model Description
Hermes 3 405B is the latest flagship model in the Hermes series of LLMs by Nous Research, and the first full parameter finetune since the release ... | null | [
"llama3"
] | null | [
"en"
] | null | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
67d2b9ca8e0458b2204c8eb3 | 46323c0f61df476d | 1fe366e13272caa0 | 262,145 | BPE | 514,906 | true | true | true | true | 6,415 | Replace | Split | Sequence | [
"Replace"
] | [
"Split"
] | [
"Replace",
"ByteFallback",
"Fuse"
] | 49 | unsloth/gemma-3-1b-it-unsloth-bnb-4bit | unsloth | {
"models": [
{
"_id": "67ced65c9b9a3df71008da90",
"id": "google/gemma-3-1b-it"
}
],
"relation": "quantized"
} | 26,183 | 1,076,735 | False | 2025-03-13T10:56:10Z | 2025-05-12T07:52:59Z | transformers | 10 | 0 | null | text-generation | {"parameters": {"F32": 89818, "BF16": 326012032, "U8": 694993968}, "total": 1021095818} | [
".gitattributes",
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"model.safetensors",
"special_tokens_map.json",
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"tokenizer.model",
"tokenizer_config.json"
] | bfae90ad90f41a9be81bd68255d733bb80c97159 | [
"transformers",
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"gemma3_text",
"text-generation",
"unsloth",
"gemma3",
"gemma",
"google",
"conversational",
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"arxiv:1905.07830",
"arxiv:1905.10044",
"arxiv:1911.11641",
"arxiv:1904.09728",
"arxiv:1705.03551",
"arxiv:1911.01547",
"arxiv:1907.10641",
"arxiv:1903.0... | null | {"architectures": ["Gemma3ForCausalLM"], "model_type": "gemma3_text", "quantization_config": {"load_in_4bit": true, "load_in_8bit": false, "quant_method": "bitsandbytes"}, "tokenizer_config": {"bos_token": "<bos>", "chat_template": "{{ bos_token }}\n{%- if messages[0]['role'] == 'system' -%}\n {%- if messages[0]['co... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "google/gemma-3-1b-it", "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "gemma", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["unsloth", "transformers", "gemma3", "gemma", "google"]} | <div>
<p style="margin-bottom: 0; margin-top: 0;">
<strong>See <a href="https://huggingface.co/collections/unsloth/gemma-3-67d12b7e8816ec6efa7e4e5b">our collection</a> for all versions of Gemma 3 including GGUF, 4-bit & 16-bit formats.</strong>
</p>
<p style="margin-bottom: 0;">
<em>Unsloth's <a href="htt... | null | [
"gemma"
] | null | [
"en"
] | 1,021,095,818 | null | null | [
"gemma3_text",
"AutoModelForCausalLM",
"Gemma3ForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
64be43d901f1983a8691fed6 | f67b6b695a239825 | 9f935814c19fa3c7 | 32,006 | BPE | 61,249 | true | false | true | true | 9 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 363 | OpenAssistant/llama2-13b-orca-8k-3319 | OpenAssistant | null | 847 | 945,479 | False | 2023-07-24T09:26:49Z | 2023-07-27T11:04:21Z | transformers | 133 | 0 | null | text-generation | null | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"generation_config.json",
"llama2/.gitattributes",
"llama2/LICENSE",
"llama2/Notice",
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"llama2/Responsible-Use-Guide.pdf",
"llama2/USE_POLICY.md",
"pytorch_model-00001-of-00007.bin",
"pytorch_model-00002-of... | b02bf240968a60674d9e1afc73f83868ae52fee0 | [
"transformers",
"pytorch",
"llama",
"text-generation",
"sft",
"en",
"dataset:ehartford/dolphin",
"dataset:shahules786/orca-chat",
"dataset:togethercomputer/RedPajama-Data-1T",
"dataset:atom-in-the-universe/fanfics-10k-50k",
"arxiv:2306.02707",
"license:other",
"text-generation-inference",
... | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": {"__type": "AddedToken", "content": "<s>", "lstrip": false, "normalized": true, "rstrip": false, "single_word": false}, "eos_token": {"__type": "AddedToken", "content": "</s>", "lstrip": false, "normalized": true, "rstrip":... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["ehartford/dolphin", "shahules786/orca-chat", "togethercomputer/RedPajama-Data-1T", "atom-in-the-universe/fanfics-10k-50k"], "eval_results": null, "language": ["en"], "library_name": null, "license": "other", "license_name": null, "license_link": null, "metrics": null, "model_name": nu... | # llama2-13b-orca-8k-3319
## Model Description
This model is a fine-tuning of Meta's Llama2 13B model with 8K context size on a long-conversation variant of the Dolphin dataset ([orca-chat](https://huggingface.co/datasets/shahules786/orca-chat)).
Note: **At least Huggingface Transformers [4.31.0](https://pypi.org/pr... | null | [
"other"
] | [
"ehartford/dolphin",
"shahules786/orca-chat",
"togethercomputer/RedPajama-Data-1T",
"atom-in-the-universe/fanfics-10k-50k"
] | [
"en"
] | null | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
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"text"
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"text"
] | [
"text"
] |
661cbedd81d268fe299b2578 | bcedfbbaedf61cc5 | 94507c3d22ac2826 | 60,516 | BPE | 39,382 | true | true | true | false | 5 | Sequence | Whitespace | null | [
"NFC",
"Lowercase"
] | [
"Whitespace"
] | null | 334 | jinaai/jina-reranker-v1-turbo-en | jinaai | null | 103,163 | 1,081,936 | False | 2024-04-15T05:45:01Z | 2025-09-10T09:50:56Z | transformers | 66 | 0 | null | text-ranking | {"parameters": {"BF16": 37771777}, "total": 37771777} | [
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"merges.txt",
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"... | null | {"architectures": ["JinaBertModel"], "auto_map": {"AutoConfig": "configuration_bert.JinaBertConfig", "AutoModel": "modeling_bert.JinaBertModel", "AutoModelForMaskedLM": "modeling_bert.JinaBertForMaskedLM", "AutoModelForQuestionAnswering": "modeling_bert.JinaBertForQuestionAnswering", "AutoModelForSequenceClassification... | {
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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-ranking", "tags": ["reranker", "cross-encoder", "transformers.js", "sentence-tr... | <br><br>
<p align="center">
<img src="https://huggingface.co/datasets/jinaai/documentation-images/resolve/main/logo.webp" alt="Jina AI: Your Search Foundation, Supercharged!" width="150px">
</p>
<p align="center">
<b>Trained by <a href="https://jina.ai/"><b>Jina AI</b></a>.</b>
</p>
# jina-reranker-v1-turbo-en
This... | null | [
"apache-2.0"
] | null | [
"en"
] | 37,771,777 | null | null | [
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"AutoModel",
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"feature-extraction",
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"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
66e97b67e675dca066a6d08b | 848b60b6cd304b65 | ffc911e785abe60e | 151,665 | BPE | 151,387 | true | true | true | true | 22 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
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"ByteLevel"
] | 15 | Qwen/Qwen2.5-7B-Instruct-GPTQ-Int4 | Qwen | {
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} | 72,917 | 1,340,555 | False | 2024-09-17T12:51:51Z | 2024-10-18T02:59:11Z | transformers | 29 | 0 | null | text-generation | {"parameters": {"I32": 6525288448, "F16": 1090328064}, "total": 7615616512} | [
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"endpoints_compatibl... | 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/Qwen2.5-7B-Instruct", "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": "https://huggingface.co/Qwen/Qwen2.5-7B-Instruct-GPTQ-Int4/blob/main/LICENSE", "metrics": null, "model_name": null, "pipel... | # Qwen2.5-7B-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 | [
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] | null | [
"en"
] | 7,615,616,512 | null | null | [
"AutoModelForCausalLM",
"Qwen2ForCausalLM",
"qwen2"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
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"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 15 | Qwen/Qwen2.5-32B-Instruct-GPTQ-Int4 | Qwen | {
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{
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"id": "Qwen/Qwen2.5-32B-Instruct"
}
],
"relation": "quantized"
} | 455,618 | 1,993,304 | False | 2024-09-17T12:52:55Z | 2024-10-09T12:27:43Z | transformers | 40 | 0 | null | text-generation | {"parameters": {"I32": 31205621760, "F16": 1558254592}, "total": 32763876352} | [
".gitattributes",
"LICENSE",
"README.md",
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"license:apache-2.0",
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"endpoints_compati... | 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/Qwen2.5-32B-Instruct", "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": "https://huggingface.co/Qwen/Qwen2.5-32B-Instruct-GPTQ-Int4/blob/main/LICENSE", "metrics": null, "model_name": null, "pip... | # Qwen2.5-32B-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/Qwen2.5-32B-Instruct-GPTQ-Int4/blob/main/LICENSE"
] | null | [
"en"
] | 32,763,876,352 | null | null | [
"AutoModelForCausalLM",
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"text-generation"
] | [
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] | [
"text"
] | [
"text"
] |
66326bf19fcb8095d521512e | 53ecf467267e2824 | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 0 | avsolatorio/NoInstruct-small-Embedding-v0 | avsolatorio | null | 5,499 | 976,314 | False | 2024-05-01T16:21:05Z | 2024-05-04T02:11:03Z | sentence-transformers | 24 | 0 | [{"name": "NoInstruct-small-Embedding-v0", "results": [{"task": {"type": "Classification"}, "dataset": {"type": "mteb/amazon_counterfactual", "name": "MTEB AmazonCounterfactualClassification (en)", "config": "en", "split": "test", "revision": "e8379541af4e31359cca9fbcf4b00f2671dba205"}, "metrics": [{"type": "accuracy",... | sentence-similarity | {"parameters": {"F32": 33360000}, "total": 33360000} | [
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"... | b38747000553d8268915c95a55fc87e707c9aadd | [
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"license:mit",
"model-index",
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"endpoints_compatible",
"deploy:azure",
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] | null | {"architectures": ["BertModel"], "model_type": "bert", "tokenizer_config": {"cls_token": "[CLS]", "mask_token": "[MASK]", "pad_token": "[PAD]", "sep_token": "[SEP]", "unk_token": "[UNK]"}} | {
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} | {"language": ["en"], "library_name": "sentence-transformers", "license": "mit", "pipeline_tag": "sentence-similarity", "tags": ["feature-extraction", "mteb", "sentence-similarity", "sentence-transformers", "transformers"], "model-index": [{"name": "NoInstruct-small-Embedding-v0", "results": [{"task": {"type": "Classifi... | <h1 align="center">NoInstruct small Embedding v0</h1>
*NoInstruct Embedding: Asymmetric Pooling is All You Need*
This model has improved retrieval performance compared to the [avsolatorio/GIST-small-Embedding-v0](https://huggingface.co/avsolatorio/GIST-small-Embedding-v0) model.
One of the things that the `GIST` fam... | null | [
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"en"
] | 33,360,000 | null | null | [
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6557b95586fbe7506ea142cc | 91642f76e1681385 | ab089c4bfa2e1ada | 50,001 | Unigram | null | true | true | true | true | 5 | NFKC | Sequence | Metaspace | [
"NFKC"
] | [
"WhitespaceSplit",
"Metaspace"
] | [
"Metaspace"
] | 364 | sdadas/mmlw-roberta-base | sdadas | null | 3,378 | 1,113,972 | False | 2023-11-17T19:04:53Z | 2026-02-12T11:05:02Z | sentence-transformers | 3 | 0 | [{"name": "mmlw-roberta-base", "results": [{"task": {"type": "Clustering"}, "dataset": {"type": "PL-MTEB/8tags-clustering", "name": "MTEB 8TagsClustering", "config": "default", "split": "test", "revision": "None"}, "metrics": [{"type": "v_measure", "value": 33.08463724780795, "verified": false}]}, {"task": {"type": "Cl... | sentence-similarity | {"parameters": {"F32": 124442880}, "total": 124442880} | [
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"pipeline_tag": "feature-extraction",
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} | {"language": "pl", "license": "apache-2.0", "pipeline_tag": "sentence-similarity", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers", "mteb"], "widget": [{"source_sentence": "zapytanie: Jak do\u017cy\u0107 100 lat?", "sentences": ["Trzeba zdrowo si\u0119 od\u017cywia\u0107 i ... | <h1 align="center">MMLW-roberta-base</h1>
MMLW (muszę mieć lepszą wiadomość) are neural text encoders for Polish.
This is a distilled model that can be used to generate embeddings applicable to many tasks such as semantic similarity, clustering, information retrieval. The model can also serve as a base for further fin... | null | [
"apache-2.0"
] | null | [
"pl"
] | 124,442,880 | null | null | [
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"feature-extraction"
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"text",
"multimodal"
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"logits",
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"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 18 | nvidia/Llama-3.1-Nemotron-Nano-8B-v1 | nvidia | null | 264,232 | 1,371,550 | False | 2025-03-16T03:13:15Z | 2025-10-15T16:20:41Z | transformers | 221 | 0 | null | text-generation | {"parameters": {"BF16": 8030261248}, "total": 8030261248} | [
".gitattributes",
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... | 54641c1611fcff44fa4865626462445e0a153fc7 | [
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"endpoints_compatible",
"deploy:azure",
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] | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": "<|begin_of_text|>", "chat_template": "{%- if messages[0]['role'] == 'system' -%}{%- set system_message = messages[0]['content'] | trim -%}{%- set messages = messages[1:] -%}{%- else -%}{%- set system_message = '' -%}{%- en... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "other", "license_name": "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-3.1-Nemotron-Nano-8B-v1
## Model Overview
Llama-3.1-Nemotron-Nano-8B-v1 is a large language model (LLM) which is a derivative of [Meta Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) (AKA the reference model). It is a reasoning model that is post trained for reasoning, human ... | null | [
"other",
"nvidia-open-model-license",
"https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/"
] | null | [
"en"
] | 8,030,261,248 | null | null | [
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"text"
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"text"
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667928bd4b1e6619161a4366 | 54895f2d8faef315 | e6ffdc5dd5f1fd86 | 256,000 | BPE | 580,604 | true | true | true | true | 249 | Replace | Split | Sequence | [
"Replace"
] | [
"Split"
] | [
"Replace",
"ByteFallback",
"Fuse"
] | 50 | google/gemma-2-27b | google | null | 10,813 | 973,608 | manual | 2024-06-24T08:05:17Z | 2024-08-07T18:26:09Z | transformers | 211 | 0 | null | text-generation | null | [
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"id": "google-bert/bert-base-multilingual-cased"
}
],
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} | 90 | 933,951 | False | 2023-09-05T20:39:44Z | 2023-09-05T21:00:38Z | transformers | 2 | 0 | [{"name": "quote-model-BERTm-v1", "results": []}] | token-classification | null | [
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should probably proofread and complete it, then remove this comment. -->
# quote-model-BERTm-v1
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multil... | null | [
"apache-2.0"
] | null | null | null | null | [
"precision",
"recall",
"f1",
"accuracy"
] | [
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"BertForTokenClassification",
"bert"
] | [
"token-classification"
] | [
"text"
] | [
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] | [
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"ByteLevel"
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}
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} | 3,085 | 946,671 | False | 2025-05-28T20:04:32Z | 2025-11-21T17:03:21Z | transformers | 241 | 1 | null | text-generation | null | [
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"text-generation-inference... | null | {"architectures": ["Qwen2ForCausalLM"], "model_type": "qwen2", "tokenizer_config": {"bos_token": "<\uff5cbegin\u2581of\u2581sentence\uff5c>", "chat_template": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% set ns = namespace(is_first=false, is_tool=false, is_output_fi... | {
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<span style="font-family: default; font-size: 1.5em;">Nemotron-Research-Reasoning-Qwen-1.5B</span>
<div>
🚀 The leading generalist reasoning model for cutting-edge research and development 🌟
</div>
</div>
![Comparison between DeepSeek-R1-1.5B and Nemotron-Research-Reasoning-Qwen-1.5B](./assets/de... | null | [
"cc-by-nc-4.0"
] | null | [
"en"
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"ByteLevel"
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}
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} | 150 | 932,488 | False | 2025-02-23T23:11:13Z | 2025-10-14T22:12:43Z | transformers | 25 | 0 | null | text-generation | {"parameters": {"BF16": 7615616512}, "total": 7615616512} | [
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} | {"base_model": ["Qwen/Qwen2.5-Math-7B"], "datasets": ["internlm/Lean-Workbook", "internlm/Lean-Github", "AI-MO/NuminaMath-CoT"], "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_t... | null | null | [
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679ef6c56c76922a90b6fa7d | 1e0a2493163c0c63 | 6622292dca275a2d | 128,256 | BPE | 280,147 | false | true | true | true | 256 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 18 | unsloth/Meta-Llama-3.1-8B-Instruct-unsloth-bnb-4bit | unsloth | {
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"id": "meta-llama/Llama-3.1-8B-Instruct"
}
],
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} | 57,139 | 1,434,472 | False | 2025-02-02T04:38:29Z | 2025-02-15T10:26:56Z | transformers | 4 | 0 | null | text-generation | {"parameters": {"F32": 475344, "BF16": 1227100160, "U8": 7015836210}, "total": 8243411714} | [
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] | 0db785ab56c082e30ae7dea3645d45465fbb5797 | [
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"license:llama3.1",
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"endpoints... | 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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"pipeline_tag": "text-generation",
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} | {"base_model": "meta-llama/Llama-3.1-8B-Instruct", "datasets": null, "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "llama3.1", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["llama-3", "llama", "meta", "facebook", "u... | <div>
<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 GGUF & 4-bit formats.</strong>
</p>
<p style="margin-bottom: 0;">
<em>Unsloth's <a href="h... | null | [
"llama3.1"
] | null | [
"en"
] | 8,243,411,714 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
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] | [
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] | [
"text"
] | [
"text"
] |
6615980cff4377f38866ac97 | c88b36417e2a82ee | dd84a2b1ff97657d | 250,002 | Unigram | null | true | true | true | true | 5 | Precompiled | Sequence | Metaspace | [
"Precompiled"
] | [
"WhitespaceSplit",
"Metaspace"
] | [
"Metaspace"
] | 64 | Iceland/french-xml-model-a | Iceland | {
"models": [
{
"_id": "621ffdc036468d709f174364",
"id": "FacebookAI/xlm-roberta-base"
}
],
"relation": "finetune"
} | 1 | 1 | False | 2024-04-09T19:33:32Z | 2024-04-09T19:58:56Z | transformers | 0 | 0 | [{"name": "french-xml-model-a", "results": []}] | token-classification | {"parameters": {"F32": 277459977}, "total": 277459977} | [
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"sentencepiece.bpe.model",
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"training_args.bin"
] | a90f9bf098df17a3497036d8e08e485ab227616a | [
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"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["XLMRobertaForTokenClassification"], "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": "AutoModelForTokenClassification",
"custom_class": null,
"pipeline_tag": "token-classification",
"processor": "AutoTokenizer"
} | {"base_model": "FacebookAI/xlm-roberta-base", "datasets": null, "eval_results": [], "language": null, "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": ["precision", "recall", "f1", "accuracy"], "model_name": "french-xml-model-a", "pipeline_tag": null, "tags": ["generated_fr... | <!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# french-xml-model-a
This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-robe... | null | [
"mit"
] | null | null | 277,459,977 | null | [
"precision",
"recall",
"f1",
"accuracy"
] | [
"XLMRobertaForTokenClassification",
"AutoModelForTokenClassification",
"xlm-roberta"
] | [
"token-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
621ffdc136468d709f1802fd | f02914cc3fa57434 | 84fb34b2e1636fc5 | 30,527 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 41 | sentence-transformers/stsb-mpnet-base-v2 | sentence-transformers | null | 6,077 | 960,247 | False | 2022-03-02T23:29:05Z | 2025-08-19T10:26:48Z | sentence-transformers | 13 | 0 | null | sentence-similarity | {"parameters": {"I64": 514, "F32": 109486464}, "total": 109486978} | [
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"onnx/model_qint8_arm64.onnx",
"onnx/m... | d53b7fa83023c7dcdd264e370885fde0aa3b845d | [
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"transformers",
"text-embeddings-inference",
"arxiv:1908.10084",
"license:apache-2.0",
"endpoints_compatible",
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] | null | {"architectures": ["MPNetModel"], "model_type": "mpnet", "tokenizer_config": {"bos_token": {"content": "<s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "eos_token": {"content": "</s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, ... | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": "sentence-transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "sentence-similarity", "tags": ["sentence-transformers", "feature-extraction",... | # sentence-transformers/stsb-mpnet-base-v2
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you hav... | null | [
"apache-2.0"
] | null | null | 109,486,978 | null | null | [
"MPNetModel",
"AutoModel",
"mpnet"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
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] |
66a1a281858a536116f5b296 | c6de6331832e687a | 6622292dca275a2d | 128,256 | BPE | 280,147 | false | true | true | true | 256 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 142 | mlx-community/Meta-Llama-3.1-405B-4bit | mlx-community | null | 518 | 929,762 | False | 2024-07-25T00:55:29Z | 2024-07-31T23:51:51Z | transformers | 5 | 0 | null | text-generation | {"parameters": {"F16": 12819054592, "U32": 51259637760}, "total": 64078692352} | [
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"hi",
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] | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": "<|begin_of_text|>", "chat_template": "{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|e... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en", "de", "fr", "it", "pt", "hi", "es", "th"], "library_name": "transformers", "license": "llama3.1", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-generation", "tags": ["facebook", "meta... | # mlx-community/Meta-Llama-3.1-405B-4bit
The Model [mlx-community/Meta-Llama-3.1-405B-4bit](https://huggingface.co/mlx-community/Meta-Llama-3.1-405B-4bit) was converted to MLX format from [meta-llama/Meta-Llama-3.1-405B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-405B-Instruct) using mlx-lm version **0.... | null | [
"llama3.1"
] | null | [
"en",
"de",
"fr",
"it",
"pt",
"hi",
"es",
"th"
] | 64,078,692,352 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
65771f670331de48cb336603 | 0f357c0a73b2b178 | 3e14d786edfd85c9 | 32,032 | BPE | 58,980 | true | false | true | true | 35 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 366 | GAIR/Abel-7B-002 | GAIR | null | 13,348 | 979,669 | False | 2023-12-11T14:40:39Z | 2024-08-28T12:06:50Z | transformers | 14 | 0 | null | text-generation | null | [
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} | null | -----|------------|----------|--------------|-----------|----------------|---------|----------|---------------|----------|
|Abel-7B-002 | **80.44** | **29.46** | **69.78** |77.67 |**55.95** |77.67 |**55.05** |77.72 |61.19 |
|Abel-7B-001 |59.74 |13 |1.21 |57.67 |9.3 |53.32 |38.97 |63.51|40.59 |
|MetaMath-Mistral-7B|77.7... | null | null | null | null | null | null | null | [
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} | 40 | 924,522 | False | 2025-04-30T12:22:24Z | 2025-04-30T12:25:09Z | transformers | 7 | 0 | null | text-generation | {"parameters": {"BF16": 7615616512}, "total": 7615616512} | [
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"base_model:fi... | 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",
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"pipeline_tag": "text-generation",
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} | {"base_model": "Qwen/Qwen2.5-7B-Instruct", "datasets": null, "eval_results": null, "language": ["zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": ... | # Finetune Llama 3.1, Gemma 2, Mistral 2-5x faster with 70% less memory via Unsloth!
We have a Qwen 2.5 (all model sizes) [free Google Colab Tesla T4 notebook](https://colab.research.google.com/drive/1Kose-ucXO1IBaZq5BvbwWieuubP7hxvQ?usp=sharing).
Also a [Qwen 2.5 conversational style notebook](https://colab.resear... | null | [
"apache-2.0"
] | null | [
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] | 7,615,616,512 | null | null | [
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"Metaspace"
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] | 274 | jbochi/madlad400-3b-mt | jbochi | null | 8,635 | 946,232 | False | 2023-09-21T00:28:38Z | 2024-01-10T15:00:28Z | transformers | 154 | 0 | null | translation | {"parameters": {"F32": 2940374016}, "total": 2940374016} | [
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# Table of Contents
0. [TL;DR](#TL;DR)
1. [Model Details](#model-details)
2. [Usage](#usage)
3. [Uses](#uses)
4. [Bias, Risks, and Limitations](#bias-risks-and-limitations)
5. [Training Details](#training-details)
6. [Evaluation](#evaluation)
7. [Environmental Impact](#environmental... | null | [
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"text"
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"ByteLevel"
] | [
"ByteLevel"
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"models": [
{
"_id": "675356c50d1ab72d70dd8fde",
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}
],
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} | 49,080 | 1,203,204 | False | 2024-12-06T19:56:54Z | 2025-04-16T14:57:57Z | transformers | 168 | 0 | null | text-generation | 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-base"], "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-3.1"], ... | # Granite-3.1-8B-Instruct
**Model Summary:**
Granite-3.1-8B-Instruct is a 8B parameter long-context instruct model finetuned from Granite-3.1-8B-Base using a combination of open source instruction datasets with permissive license and internally collected synthetic datasets tailored for solving long context problems. T... | null | [
"apache-2.0"
] | null | null | null | null | null | [
"granite",
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] | [
"text-generation"
] | [
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] | [
"text"
] | [
"text"
] |
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"NFC"
] | [
"Split",
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] | 15 | Qwen/Qwen3-14B-Base | Qwen | null | 87,152 | 1,181,167 | False | 2025-04-28T05:07:11Z | 2025-07-26T03:45:33Z | transformers | 51 | 1 | null | text-generation | {"parameters": {"BF16": 14768307200}, "total": 14768307200} | [
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"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse"
] | 106 | mlx-community/gemma-2-9b-it-4bit | mlx-community | null | 16,158 | 956,168 | False | 2024-06-27T15:18:31Z | 2024-11-06T22:55:46Z | transformers | 2 | 0 | null | text-generation | null | [
".gitattributes",
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"config.json",
"model.safetensors",
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"tokenizer_config.json"
] | ff12eb39da2cd9b3b0f4b4f9ffb274603f05bb29 | [
"transformers",
"safetensors",
"gemma2",
"text-generation",
"conversational",
"mlx",
"license:gemma",
"text-generation-inference",
"endpoints_compatible",
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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": null, "datasets": null, "eval_results": null, "language": null, "library_name": "transformers", "license": "gemma", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-generation", "tags": ["conversational", "mlx"], "extra_gated_heading": "Access Gemma o... | # mlx-community/gemma-2-9b-it-4bit
The Model [mlx-community/gemma-2-9b-it-4bit](https://huggingface.co/mlx-community/gemma-2-9b-it-4bit) was converted to MLX format from [google/gemma-2-9b-it](https://huggingface.co/google/gemma-2-9b-it) using mlx-lm version **0.15.0**.
## Use with mlx
```bash
pip install mlx-lm
```... | null | [
"gemma"
] | null | null | null | null | null | [
"AutoModelForCausalLM",
"gemma2",
"Gemma2ForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
624d55bab58d1313f8b775ff | 492a9f4f584c75aa | dd84a2b1ff97657d | 250,002 | Unigram | null | true | true | true | true | 5 | Precompiled | Sequence | Metaspace | [
"Precompiled"
] | [
"WhitespaceSplit",
"Metaspace"
] | [
"Metaspace"
] | 5 | MilaNLProc/xlm-emo-t | MilaNLProc | null | 2,310 | 928,050 | False | 2022-04-06T08:56:26Z | 2023-03-27T17:52:36Z | transformers | 11 | 1 | null | text-classification | null | [
".gitattributes",
"README.md",
"config.json",
"pytorch_model.bin",
"sentencepiece.bpe.model",
"tokenizer.json",
"training_args.bin"
] | a6ee7c9fad08d60204e7ae437d41d392381496f0 | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"emotion",
"emotion-analysis",
"multilingual",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["XLMRobertaForSequenceClassification"], "model_type": "xlm-roberta"} | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
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} | {"base_model": null, "datasets": null, "eval_results": null, "language": "multilingual", "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["emotion", "emotion-analysis", "multilingual"], "widget": [{"text": "Guarda! ci ... | #
[Federico Bianchi](https://federicobianchi.io/) •
[Debora Nozza](http://dnozza.github.io/) •
[Dirk Hovy](http://www.dirkhovy.com/)
## Abstract
Detecting emotion in text allows social and computational scientists to study how people behave and react to online events. However, developing these tools for different lan... | null | null | null | [
"multilingual"
] | null | null | null | [
"AutoModelForSequenceClassification",
"XLMRobertaForSequenceClassification",
"xlm-roberta"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
65cac17519683f9817b6e2b7 | 2d0341bd3a73bae6 | ef4806e2ade9022f | 32,100 | Unigram | null | true | true | true | true | 103 | Sequence | Metaspace | Metaspace | [
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 32 | roborovski/superprompt-v1 | roborovski | null | 9,369 | 1,474,641 | False | 2024-02-13T01:10:13Z | 2024-07-03T15:15:04Z | transformers | 91 | 0 | null | null | {"parameters": {"F32": 76961152}, "total": 76961152} | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
"model.safetensors",
"spiece.model",
"tokenizer.json",
"tokenizer_config.json"
] | 64ec0168f6b14d389bdfa0699eb9beb442bbd60e | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"en",
"license:mit",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["T5ForConditionalGeneration"], "model_type": "t5", "tokenizer_config": {"eos_token": "</s>", "pad_token": "<pad>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModelForSeq2SeqLM",
"custom_class": null,
"pipeline_tag": "text2text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en"], "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["text2text-generation"], "widget": [{"text": "Expand the following prompt to add more d... | # SuperPrompt
A T5 model fine-tuned on the SuperPrompt dataset for upsampling text prompts to more detailed descriptions. Meant to be used as a pre-generation step for text-to-image models that benefit from more detailed prompts.
**Usage**
```bash
pip install transformers
```
```python
from transformers import T5To... | null | [
"mit"
] | null | [
"en"
] | 76,961,152 | null | null | [
"t5",
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"AutoModelForSeq2SeqLM"
] | [
"text2text-generation"
] | null | null | null |
671a01a72d1a46a37f22db3e | 3f30eeeed6bb7fe3 | ffc911e785abe60e | 151,665 | BPE | 151,387 | true | true | true | true | 22 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 368 | jason9693/Qwen2.5-1.5B-apeach | jason9693 | null | 213,616 | 1,539,594 | False | 2024-10-24T08:13:27Z | 2024-10-24T08:17:52Z | transformers | 5 | 0 | null | text-classification | {"parameters": {"F32": 1543717376}, "total": 1543717376} | [
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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 | 1,543,717,376 | null | null | [
"Qwen2ForSequenceClassification",
"AutoModelForSequenceClassification",
"qwen2"
] | [
"text-classification"
] | [
"text"
] | [
"text"
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"logits"
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"NFC"
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"ByteLevel"
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"models": [
{
"_id": "688b1597e5e83e19d1b3238a",
"id": "Qwen/Qwen3-Coder-30B-A3B-Instruct"
}
],
"relation": "quantized"
} | 196,644 | 1,661,635 | False | 2025-08-01T14:32:44Z | 2025-08-01T14:33:43Z | transformers | 6 | 1 | null | text-generation | {"parameters": {"BF16": 954333184, "U32": 4770611200}, "total": 30532122624} | [
".gitattributes",
"README.md",
"added_tokens.json",
"chat_template.jinja",
"config.json",
"generation_config.json",
"merges.txt",
"model-00001-of-00004.safetensors",
"model-00002-of-00004.safetensors",
"model-00003-of-00004.safetensors",
"model-00004-of-00004.safetensors",
"model.safetensors.i... | a7d91f62488598d4725c5363a73d0a8f0e1b9329 | [
"transformers",
"safetensors",
"qwen3_moe",
"text-generation",
"mlx",
"conversational",
"base_model:Qwen/Qwen3-Coder-30B-A3B-Instruct",
"base_model:quantized:Qwen/Qwen3-Coder-30B-A3B-Instruct",
"license:apache-2.0",
"endpoints_compatible",
"5-bit",
"region:us"
] | null | {"architectures": ["Qwen3MoeForCausalLM"], "model_type": "qwen3_moe", "quantization_config": {"bits": 5}, "tokenizer_config": {"bos_token": null, "eos_token": "<|im_end|>", "pad_token": "<|endoftext|>", "unk_token": null, "chat_template": "{% macro render_item_list(item_list, tag_name='required') %}\n {%- if item_li... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "Qwen/Qwen3-Coder-30B-A3B-Instruct", "datasets": null, "eval_results": null, "language": null, "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": "https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct/blob/main/LICENSE", "metrics": null, "model_name": null, ... | null | null | [
"apache-2.0",
"https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct/blob/main/LICENSE"
] | null | null | 30,532,122,624 | null | null | [
"qwen3_moe",
"AutoModelForCausalLM",
"Qwen3MoeForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
66756e635eaa9dd299b9da87 | b8db556f1f02dec9 | 7f171e98bdd0da5f | 83,828 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 79 | sergeyzh/rubert-tiny-turbo | sergeyzh | {
"models": [
{
"_id": "621ffdc136468d709f17a149",
"id": "cointegrated/rubert-tiny2"
}
],
"relation": "finetune"
} | 36,640 | 1,089,156 | False | 2024-06-21T12:13:23Z | 2024-07-31T19:39:14Z | sentence-transformers | 30 | 0 | [{"name": "sergeyzh/rubert-tiny-turbo", "results": [{"dataset": {"config": "default", "name": "MTEB AILACasedocs (default)", "revision": "4106e6bcc72e0698d714ea8b101355e3e238431a", "split": "test", "type": "mteb/AILA_casedocs"}, "metrics": [{"type": "main_score", "value": 7.432999999999999, "verified": false}, {"type":... | sentence-similarity | {"parameters": {"F32": 29193768}, "total": 29193768} | [
".gitattributes",
"1_Pooling/config.json",
"README.md",
"config.json",
"model.safetensors",
"modules.json",
"sentence_bert_config.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.txt"
] | 93769a3baad2b037e5c2e4312fccf6bcfe082bf1 | [
"sentence-transformers",
"safetensors",
"bert",
"feature-extraction",
"russian",
"pretraining",
"embeddings",
"tiny",
"sentence-similarity",
"transformers",
"mteb",
"ru",
"dataset:IlyaGusev/gazeta",
"dataset:zloelias/lenta-ru",
"base_model:cointegrated/rubert-tiny2",
"base_model:finetu... | 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": "cointegrated/rubert-tiny2", "datasets": ["IlyaGusev/gazeta", "zloelias/lenta-ru"], "language": ["ru"], "license": "mit", "pipeline_tag": "sentence-similarity", "tags": ["russian", "pretraining", "embeddings", "tiny", "feature-extraction", "sentence-similarity", "sentence-transformers", "transformers", "... | Быстрая модель BERT для расчетов эмбеддингов предложений на русском языке. Модель основана на [cointegrated/rubert-tiny2](https://huggingface.co/cointegrated/rubert-tiny2) - имеет аналогичные размеры контекста (2048), ембеддинга (312) и быстродействие.
## Использование
```Python
from sentence_transformers imp... | null | [
"mit"
] | [
"IlyaGusev/gazeta",
"zloelias/lenta-ru"
] | [
"ru"
] | 29,193,768 | null | null | [
"BertModel",
"AutoModel",
"bert"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
682c23893c5e5e8582f6098c | ea3ee092c08c9d64 | 2f8c3374367f63f8 | 131,072 | BPE | 269,443 | false | true | true | true | 1,000 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 369 | sarvamai/sarvam-m | sarvamai | {
"models": [
{
"_id": "67d75f3fd56afd880495e9b6",
"id": "mistralai/Mistral-Small-3.1-24B-Base-2503"
}
],
"relation": "finetune"
} | 3,718 | 919,430 | False | 2025-05-20T06:39:05Z | 2025-05-28T08:11:24Z | transformers | 340 | 0 | null | text-generation | null | [
".gitattributes",
"LICENSE.txt",
"README.md",
"config.json",
"generation_config.json",
"model-00001-of-00010.safetensors",
"model-00002-of-00010.safetensors",
"model-00003-of-00010.safetensors",
"model-00004-of-00010.safetensors",
"model-00005-of-00010.safetensors",
"model-00006-of-00010.safeten... | 01534a53c46f2788e392dbb3d994e0fa8f04d3fd | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"en",
"bn",
"hi",
"kn",
"gu",
"mr",
"ml",
"or",
"pa",
"ta",
"te",
"base_model:mistralai/Mistral-Small-3.1-24B-Base-2503",
"base_model:finetune:mistralai/Mistral-Small-3.1-24B-Base-2503",
"license:apach... | null | {"architectures": ["MistralForCausalLM"], "model_type": "mistral", "tokenizer_config": {"bos_token": "<s>", "chat_template": "{%- set today = strftime_now(\"%Y-%m-%d\") %}\n{%- set default_think_system_message = \"You are a helpful assistant. Think deeply before answering the user's question. Do the thinking inside <th... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": ["mistralai/Mistral-Small-3.1-24B-Base-2503"], "datasets": null, "eval_results": null, "language": ["en", "bn", "hi", "kn", "gu", "mr", "ml", "or", "pa", "ta", "te"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, ... | # Sarvam-M
<p align="center">
<a href="https://dashboard.sarvam.ai/playground"
target="_blank" rel="noopener noreferrer">
<img
src="https://img.shields.io/badge/🚀 Chat on Sarvam Playground-1488CC?style=for-the-badge&logo=rocket"
alt="Chat on Sarvam Playground"
/>
</a>
</p>
# Model I... | null | [
"apache-2.0"
] | null | [
"en",
"bn",
"hi",
"kn",
"gu",
"mr",
"ml",
"or",
"pa",
"ta",
"te"
] | null | null | null | [
"AutoModelForCausalLM",
"mistral",
"MistralForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
63eaab5315821fe67c48b429 | 58967ba68263f0aa | 2f8d5857ae5fa330 | 50,277 | BPE | 50,009 | true | true | true | true | 25 | NFC | ByteLevel | ByteLevel | [
"NFC"
] | [
"ByteLevel"
] | [
"ByteLevel"
] | 86 | EleutherAI/pythia-410m-deduped | EleutherAI | null | 30,020 | 1,024,184 | False | 2023-02-13T21:27:47Z | 2023-07-09T16:05:38Z | transformers | 20 | 0 | null | text-generation | {"parameters": {"F16": 405334208, "U8": 100663296}, "total": 505997504} | [
".gitattributes",
"README.md",
"config.json",
"model.safetensors",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | c4fc8d586d62df497f1f9b69d66d3ca419992d3e | [
"transformers",
"pytorch",
"safetensors",
"gpt_neox",
"text-generation",
"causal-lm",
"pythia",
"en",
"dataset:EleutherAI/the_pile_deduplicated",
"arxiv:2304.01373",
"arxiv:2101.00027",
"arxiv:2201.07311",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"depl... | 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",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["EleutherAI/the_pile_deduplicated"], "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": ["pytorch", "causal-lm", "pythia"]} | The *Pythia Scaling Suite* is a collection of models developed to facilitate
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
models: one trained on the Pile, and ... | null | [
"apache-2.0"
] | [
"EleutherAI/the_pile_deduplicated"
] | [
"en"
] | 505,997,504 | null | null | [
"gpt_neox",
"AutoModelForCausalLM",
"GPTNeoXForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
642e21c893f22bbdacfbf7a4 | fb678317d932ca07 | 643438971d3e2899 | 49,157 | BPE | 48,909 | false | true | false | true | 5 | null | Sequence | ByteLevel | null | [
"Digits",
"ByteLevel"
] | [
"ByteLevel"
] | 139 | bigcode/gpt_bigcode-santacoder | bigcode | null | 68,608 | 1,158,541 | False | 2023-04-06T01:35:04Z | 2023-06-08T09:20:22Z | transformers | 26 | 0 | [{"name": "SantaCoder", "results": [{"task": {"type": "text-generation"}, "dataset": {"type": "nuprl/MultiPL-E", "name": "MultiPL HumanEval (Python)"}, "metrics": [{"name": "pass@1", "type": "pass@1", "value": 0.18, "verified": false}, {"name": "pass@10", "type": "pass@10", "value": 0.29, "verified": false}, {"name": "... | text-generation | {"parameters": {"F16": 1124886528}, "total": 1124886528} | [
".gitattributes",
"README.md",
"config.json",
"model.safetensors",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | 291931872cae83498cf984b16319f47f5e9e7a07 | [
"transformers",
"pytorch",
"safetensors",
"gpt_bigcode",
"text-generation",
"code",
"dataset:bigcode/the-stack",
"license:openrail",
"model-index",
"text-generation-inference",
"deploy:azure",
"region:us"
] | null | {"architectures": ["GPTBigCodeForCausalLM"], "model_type": "gpt_bigcode", "tokenizer_config": {"bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>"}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"datasets": ["bigcode/the-stack"], "language": ["code"], "license": "openrail", "pipeline_tag": "text-generation", "programming_language": ["Java", "JavaScript", "Python"], "inference": false, "model-index": [{"name": "SantaCoder", "results": [{"task": {"type": "text-generation"}, "dataset": {"name": "MultiPL HumanEva... | # SantaCoder
Play with the model on the [SantaCoder Space Demo](https://huggingface.co/spaces/bigcode/santacoder-demo).
# Table of Contents
1. [Model Summary](#model-summary)
2. [Use](#use)
3. [Limitations](#limitations)
4. [Training]... | null | [
"openrail"
] | [
"bigcode/the-stack"
] | [
"code"
] | 1,124,886,528 | null | null | [
"gpt_bigcode",
"AutoModelForCausalLM",
"GPTBigCodeForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
62862f547cc3b7bc1b90315f | 7a4cc4a6090329ff | ab8f40e5a89244c4 | 250,680 | BPE | 250,434 | false | true | true | true | 4 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 31 | bigscience/bloom-1b1 | bigscience | null | 5,102 | 935,622 | False | 2022-05-19T11:51:48Z | 2023-09-26T09:16:38Z | transformers | 66 | 0 | null | text-generation | {"parameters": {"F16": 1065314304}, "total": 1065314304} | [
".gitattributes",
"LICENSE",
"README.md",
"config.json",
"flax_model.msgpack",
"model.safetensors",
"onnx/config.json",
"onnx/decoder_model.onnx",
"onnx/decoder_model.onnx_data",
"onnx/decoder_model_merged.onnx",
"onnx/decoder_model_merged.onnx_data",
"onnx/decoder_with_past_model.onnx",
"on... | eb3dd7399312f5f94fd13f41d2f318117d3eb1e4 | [
"transformers",
"pytorch",
"jax",
"onnx",
"safetensors",
"bloom",
"text-generation",
"ak",
"ar",
"as",
"bm",
"bn",
"ca",
"code",
"en",
"es",
"eu",
"fon",
"fr",
"gu",
"hi",
"id",
"ig",
"ki",
"kn",
"lg",
"ln",
"ml",
"mr",
"ne",
"nso",
"ny",
"or",
"pa",... | null | {"architectures": ["BloomForCausalLM"], "model_type": "bloom", "tokenizer_config": {"unk_token": "<unk>", "eos_token": "</s>", "bos_token": "<s>", "pad_token": "<pad>"}} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["ak", "ar", "as", "bm", "bn", "ca", "code", "en", "es", "eu", "fon", "fr", "gu", "hi", "id", "ig", "ki", "kn", "lg", "ln", "ml", "mr", "ne", "nso", "ny", "or", "pa", "pt", "rn", "rw", "sn", "st", "sw", "ta", "te", "tn", "ts", "tum", "tw", "ur", "... | <h1 style='text-align: center '>BLOOM LM</h1>
<h2 style='text-align: center '><em>BigScience Large Open-science Open-access Multilingual Language Model</em> </h2>
<h3 style='text-align: center '>Model Card</h3>
<img src="https://s3.amazonaws.com/moonup/production/uploads/1657124309515-5f17f0a0925b9863e28ad517.png" al... | null | [
"bigscience-bloom-rail-1.0"
] | null | [
"ak",
"ar",
"as",
"bm",
"bn",
"ca",
"code",
"en",
"es",
"eu",
"fon",
"fr",
"gu",
"hi",
"id",
"ig",
"ki",
"kn",
"lg",
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"ml",
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"ne",
"nso",
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"or",
"pa",
"pt",
"rn",
"rw",
"sn",
"st",
"sw",
"ta",
"te",
"tn",
"ts",
"tum",
"tw",
... | 1,065,314,304 | null | null | [
"AutoModelForCausalLM",
"BloomForCausalLM",
"bloom"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
62c80a03de0489fcc32e3d53 | 7903ca758156b287 | ffae8233e53a99e4 | 256,204 | BPE | 514,098 | true | true | true | true | 207 | Sequence | Metaspace | Metaspace | [
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 60 | facebook/nllb-200-1.3B | facebook | null | 35,227 | 1,038,160 | False | 2022-07-08T10:42:11Z | 2023-02-11T20:19:16Z | transformers | 70 | 0 | null | translation | null | [
".gitattributes",
"README.md",
"config.json",
"generation_config.json",
"pytorch_model.bin",
"sentencepiece.bpe.model",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json"
] | b0de46b488af0cf31749cd8da5ed3171e11b2309 | [
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"nllb",
"translation",
"ace",
"acm",
"acq",
"aeb",
"af",
"ajp",
"ak",
"als",
"am",
"apc",
"ar",
"ars",
"ary",
"arz",
"as",
"ast",
"awa",
"ayr",
"azb",
"azj",
"ba",
"bm",
"ban",
"be",
"bem",
"b... | null | {"architectures": ["M2M100ForConditionalGeneration"], "model_type": "m2m_100", "tokenizer_config": {"bos_token": "<s>", "cls_token": "<s>", "eos_token": "</s>", "mask_token": {"__type": "AddedToken", "content": "<mask>", "lstrip": true, "normalized": true, "rstrip": false, "single_word": false}, "pad_token": "<pad>", "... | {
"auto_model": "AutoModelForSeq2SeqLM",
"custom_class": null,
"pipeline_tag": "text2text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["flores-200"], "eval_results": null, "language": ["ace", "acm", "acq", "aeb", "af", "ajp", "ak", "als", "am", "apc", "ar", "ars", "ary", "arz", "as", "ast", "awa", "ayr", "azb", "azj", "ba", "bm", "ban", "be", "bem", "bn", "bho", "bjn", "bo", "bs", "bug", "bg", "ca", "ceb", "cs", "cjk"... | # NLLB-200
This is the model card of NLLB-200's 1.3B variant.
Here are the [metrics](https://tinyurl.com/nllb200dense1bmetrics) for that particular checkpoint.
- Information about training algorithms, parameters, fairness constraints or other applied approaches, and features. The exact training algorithm, data and t... | null | [
"cc-by-nc-4.0"
] | [
"flores-200"
] | [
"ace",
"acm",
"acq",
"aeb",
"af",
"ajp",
"ak",
"als",
"am",
"apc",
"ar",
"ars",
"ary",
"arz",
"as",
"ast",
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"ayr",
"azb",
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"ban",
"be",
"bem",
"bn",
"bho",
"bjn",
"bo",
"bs",
"bug",
"bg",
"ca",
"ceb",
"cs",
"cjk",
"ckb",... | null | null | [
"bleu",
"spbleu",
"chrf++"
] | [
"AutoModelForSeq2SeqLM",
"m2m_100",
"M2M100ForConditionalGeneration"
] | [
"text2text-generation",
"translation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
67324cdc25e63c286e6534aa | 848b60b6cd304b65 | ffc911e785abe60e | 151,665 | BPE | 151,387 | true | true | true | true | 22 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 15 | mlx-community/Qwen2.5-Coder-32B-Instruct-4bit | mlx-community | {
"models": [
{
"_id": "672b1f9e956e6880fdb8c1e5",
"id": "Qwen/Qwen2.5-Coder-32B-Instruct"
}
],
"relation": "quantized"
} | 1,600 | 907,130 | False | 2024-11-11T18:28:44Z | 2024-11-11T20:45:34Z | transformers | 12 | 0 | null | text-generation | null | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"merges.txt",
"model-00001-of-00004.safetensors",
"model-00002-of-00004.safetensors",
"model-00003-of-00004.safetensors",
"model-00004-of-00004.safetensors",
"model.safetensors.index.json",
"special_tokens_map.json",
"tokenizer... | d1e3b690c8e225d7795bccddf971ca6be68b2012 | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"code",
"codeqwen",
"chat",
"qwen",
"qwen-coder",
"mlx",
"conversational",
"en",
"base_model:Qwen/Qwen2.5-Coder-32B-Instruct",
"base_model:quantized:Qwen/Qwen2.5-Coder-32B-Instruct",
"license:apache-2.0",
"text-generation-infer... | 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 ... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "Qwen/Qwen2.5-Coder-32B-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-Coder-32B-Instruct/blob/main/LICENSE", "metrics": null, "model_name": null, "p... | # mlx-community/Qwen2.5-Coder-32B-Instruct-4bit
The Model [mlx-community/Qwen2.5-Coder-32B-Instruct-4bit](https://huggingface.co/mlx-community/Qwen2.5-Coder-32B-Instruct-4bit) was converted to MLX format from [Qwen/Qwen2.5-Coder-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-32B-Instruct) using mlx-lm version... | null | [
"apache-2.0",
"https://huggingface.co/Qwen/Qwen2.5-Coder-32B-Instruct/blob/main/LICENSE"
] | null | [
"en"
] | null | null | null | [
"AutoModelForCausalLM",
"Qwen2ForCausalLM",
"qwen2"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
621ffdc036468d709f176d6a | 18ba4da77c5d0489 | defc0c9572427688 | 50,260 | BPE | 49,996 | false | true | true | true | 3 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 370 | Norod78/hebrew-bad_wiki-gpt_neo-tiny | Norod78 | null | 428 | 905,894 | False | 2022-03-02T23:29:04Z | 2023-04-23T16:38:52Z | transformers | 0 | 0 | null | text-generation | {"parameters": {"F32": 81898752, "U8": 6291456}, "total": 88190208} | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"coreml/feature-extraction/float32_model.mlpackage/Data/com.apple.CoreML/model.mlmodel",
"coreml/feature-extraction/float32_model.mlpackage/Data/com.apple.CoreML/weights/weight.bin",
"coreml/feature-extraction/float32_model.mlpackage/Man... | ad9105a0946d671a89763e7fe2b3ae4260083c63 | [
"transformers",
"pytorch",
"coreml",
"safetensors",
"gpt_neo",
"text-generation",
"he",
"arxiv:1910.09700",
"arxiv:2105.09680",
"license:mit",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["GPTNeoForCausalLM"], "model_type": "gpt_neo", "tokenizer_config": {"unk_token": {"content": "<|endoftext|>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "bos_token": {"content": "<|startoftext|>", "single_word": false, "lstrip": false, "rstrip... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": "he", "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null, "thumbnail": "https://avatars1.githubusercontent.com/u/3617152?norod.jpg", "widget":... | # hebrew-bad_wiki-gpt_neo-tiny
## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
- [Evaluation](#evaluation)
- [Environmental Impact](#environmental-impact)
- [How to Get Started With the Model](#how-to-get-st... | null | [
"mit"
] | null | [
"he"
] | 88,190,208 | null | null | [
"gpt_neo",
"AutoModelForCausalLM",
"GPTNeoForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
65ab96c650f85a33ed359a89 | ea0d23b75fcf1975 | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 371 | protectai/distilroberta-base-rejection-v1 | protectai | {
"models": [
{
"_id": "621ffdc036468d709f174349",
"id": "distilbert/distilroberta-base"
}
],
"relation": "quantized"
} | 7,250 | 924,661 | False | 2024-01-20T09:47:50Z | 2024-03-11T13:12:04Z | transformers | 8 | 0 | [{"name": "distilroberta-base-rejection-v1", "results": []}] | text-classification | {"parameters": {"F32": 82119938}, "total": 82119938} | [
".gitattributes",
"README.md",
"config.json",
"distilroberta-base-rejection-v1_emissions.csv",
"emissions.csv",
"merges.txt",
"model.safetensors",
"onnx/config.json",
"onnx/merges.txt",
"onnx/model.onnx",
"onnx/special_tokens_map.json",
"onnx/tokenizer.json",
"onnx/tokenizer_config.json",
... | 65584967c3f22ff7723e5370c65e0e76791e6055 | [
"transformers",
"onnx",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"rejection",
"no_answer",
"chatgpt",
"en",
"dataset:argilla/notus-uf-dpo-closest-rejected",
"arxiv:2308.13387",
"arxiv:2306.03423",
"base_model:distilbert/distilroberta-base",
"base_model:q... | 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": "distilroberta-base", "datasets": ["argilla/notus-uf-dpo-closest-rejected"], "eval_results": [], "language": ["en"], "library_name": null, "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": ["accuracy", "recall", "precision", "f1"], "model_name": "distilroberta-base-rejection... | # Model Card for distilroberta-base-rejection-v1
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on multiple combined datasets of rejections from different LLMs and normal responses from RLHF datasets.
It aims to identify rejections in LLMs when the prompt doesn't... | null | [
"apache-2.0"
] | [
"argilla/notus-uf-dpo-closest-rejected"
] | [
"en"
] | 82,119,938 | null | [
"accuracy",
"recall",
"precision",
"f1"
] | [
"roberta",
"AutoModelForSequenceClassification",
"RobertaForSequenceClassification"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
621ffdc136468d709f1802fe | d82c716f01e57f52 | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 62 | sentence-transformers/stsb-roberta-base-v2 | sentence-transformers | null | 75,458 | 1,156,592 | False | 2022-03-02T23:29:05Z | 2025-03-06T13:36:30Z | sentence-transformers | 5 | 0 | null | sentence-similarity | {"parameters": {"I64": 514, "F32": 124645632}, "total": 124646146} | [
".gitattributes",
"1_Pooling/config.json",
"README.md",
"config.json",
"config_sentence_transformers.json",
"flax_model.msgpack",
"merges.txt",
"model.safetensors",
"modules.json",
"onnx/model.onnx",
"onnx/model_O1.onnx",
"onnx/model_O2.onnx",
"onnx/model_O3.onnx",
"onnx/model_O4.onnx",
... | e912d853e58141c06b0529efae1b46bee1bf2ba1 | [
"sentence-transformers",
"pytorch",
"tf",
"jax",
"onnx",
"safetensors",
"openvino",
"roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["RobertaModel"], "model_type": "roberta", "tokenizer_config": {"unk_token": {"content": "<unk>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "bos_token": {"content": "<s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": t... | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": "feature-extraction",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": "sentence-transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "sentence-similarity", "tags": ["sentence-transformers", "feature-extraction",... | # sentence-transformers/stsb-roberta-base-v2
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you h... | null | [
"apache-2.0"
] | null | null | 124,646,146 | null | null | [
"roberta",
"AutoModel",
"RobertaModel"
] | [
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
67b3a4816de7ff0b4c892679 | 5913c555399afcb0 | e6ffdc5dd5f1fd86 | 256,000 | BPE | 580,604 | true | true | true | true | 249 | Replace | Split | Sequence | [
"Replace"
] | [
"Split"
] | [
"Replace",
"ByteFallback",
"Fuse"
] | 147 | google/siglip2-so400m-patch16-512 | google | null | 43,140 | 1,497,519 | False | 2025-02-17T21:05:05Z | 2025-02-21T04:04:07Z | transformers | 44 | 0 | null | zero-shot-image-classification | {"parameters": {"F32": 1136555698}, "total": 1136555698} | [
".gitattributes",
"README.md",
"config.json",
"model.safetensors",
"preprocessor_config.json",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer.model",
"tokenizer_config.json"
] | ceea1cba8130d8271436da4828633198c176a775 | [
"transformers",
"safetensors",
"siglip",
"vision",
"zero-shot-image-classification",
"arxiv:2502.14786",
"arxiv:2303.15343",
"arxiv:2209.06794",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | {"model_type": "siglip", "tokenizer_config": {"bos_token": "<bos>", "chat_template": "{{ bos_token }}{% if messages[0]['role'] == 'system' %}{{ raise_exception('System role not supported') }}{% endif %}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Convers... | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": null,
"processor": null
} | {"base_model": null, "datasets": null, "eval_results": null, "language": null, "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "zero-shot-image-classification", "tags": ["vision"], "widget": [{"src": "https://huggi... | null | null | [
"apache-2.0"
] | null | null | 1,136,555,698 | null | null | [
"AutoModel",
"siglip"
] | [
null,
"zero-shot-image-classification"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"logits"
] |
621ffdc136468d709f180291 | fc29ccd738d51f6b | 535fa14da0af647c | 30,522 | WordPiece | null | true | true | true | true | 5 | BertNormalizer | BertPreTokenizer | WordPiece | [
"BertNormalizer"
] | [
"BertPreTokenizer"
] | [
"WordPiece"
] | 1 | sentence-transformers/all-MiniLM-L12-v1 | sentence-transformers | null | 6,247 | 917,889 | False | 2022-03-02T23:29:05Z | 2025-03-06T13:19:24Z | sentence-transformers | 13 | 0 | null | sentence-similarity | {"parameters": {"I64": 512, "F32": 33360000}, "total": 33360512} | [
".gitattributes",
"1_Pooling/config.json",
"README.md",
"config.json",
"config_sentence_transformers.json",
"data_config.json",
"model.safetensors",
"modules.json",
"onnx/model.onnx",
"onnx/model_O1.onnx",
"onnx/model_O2.onnx",
"onnx/model_O3.onnx",
"onnx/model_O4.onnx",
"onnx/model_qint8_... | 26ca81849568ce77f97b2f071cfef3995f2e6be7 | [
"sentence-transformers",
"pytorch",
"onnx",
"safetensors",
"openvino",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"en",
"arxiv:1904.06472",
"arxiv:2102.07033",
"arxiv:2104.08727",
"arxiv:1704.05179",
"arxiv:1810.09305",
"license:apache-2.0",
"text-embeddings... | null | {"model_type": "bert", "tokenizer_config": {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}} | {
"auto_model": "AutoModel",
"custom_class": null,
"pipeline_tag": null,
"processor": null
} | {"base_model": null, "datasets": null, "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": "sentence-similarity", "tags": ["sentence-transformers", "feature-extraction",... | # all-MiniLM-L12-v1
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](h... | null | [
"apache-2.0"
] | null | [
"en"
] | 33,360,512 | null | null | [
"AutoModel",
"bert"
] | [
null,
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
66af17be7f4db96a101fec37 | a231e320f7c8f1b0 | 9e934e8aeb2c4673 | 151,666 | BPE | 151,387 | true | true | true | true | 24 | NFC | Sequence | ByteLevel | [
"NFC"
] | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 372 | openbmb/MiniCPM-V-2_6-int4 | openbmb | {
"models": [
{
"_id": "66af1e9d13ac6725105cabf6",
"id": "openbmb/MiniCPM-V-2_6"
}
],
"relation": "quantized"
} | 21,963 | 937,998 | False | 2024-08-04T05:55:10Z | 2025-06-16T06:19:21Z | transformers | 88 | 0 | null | image-text-to-text | {"parameters": {"F32": 511227, "BF16": 1198648048, "U8": 7116282548}, "total": 8315441823} | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"configuration_minicpm.py",
"generation_config.json",
"image_processing_minicpmv.py",
"merges.txt",
"model-00001-of-00002.safetensors",
"model-00002-of-00002.safetensors",
"model.safetensors.index.json",
"modeling_minicpmv.py",... | 06219bd087e8fd1ef899036ff990a6779555f6d9 | [
"transformers",
"safetensors",
"minicpmv",
"feature-extraction",
"minicpm-v",
"vision",
"ocr",
"multi-image",
"video",
"custom_code",
"image-text-to-text",
"conversational",
"multilingual",
"dataset:openbmb/RLAIF-V-Dataset",
"base_model:openbmb/MiniCPM-V-2_6",
"base_model:quantized:ope... | null | {"architectures": ["MiniCPMV"], "auto_map": {"AutoConfig": "configuration_minicpm.MiniCPMVConfig", "AutoModel": "modeling_minicpmv.MiniCPMV", "AutoModelForCausalLM": "modeling_minicpmv.MiniCPMV"}, "model_type": "minicpmv", "quantization_config": {"load_in_4bit": true, "load_in_8bit": false, "quant_method": "bitsandbyte... | {
"auto_model": "AutoModel",
"custom_class": "modeling_minicpmv.MiniCPMV",
"pipeline_tag": "feature-extraction",
"processor": null
} | {"base_model": ["openbmb/MiniCPM-V-2_6"], "datasets": ["openbmb/RLAIF-V-Dataset"], "eval_results": null, "language": ["multilingual"], "library_name": "transformers", "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "image-text-to-text", "tags": ["minicpm... | ## News <!-- omit in toc -->
* [2025.01.14] 🔥🔥 We open source [**MiniCPM-o 2.6**](https://huggingface.co/openbmb/MiniCPM-o-2_6), with significant performance improvement over **MiniCPM-V 2.6**, and support real-time speech-to-speech conversation and multimodal live streaming. Try it now.
## MiniCPM-V 2.6 int4
Thi... | null | null | [
"openbmb/RLAIF-V-Dataset"
] | [
"multilingual"
] | 8,315,441,823 | null | null | [
"AutoModel",
"MiniCPMV",
"minicpmv",
"modeling_minicpmv.MiniCPMV"
] | [
"image-text-to-text",
"feature-extraction"
] | [
"multimodal"
] | [
"text",
"image"
] | [
"text",
"embeddings"
] |
65a14702b240f626413f08b1 | 8ecd8d189729bdd3 | 05c28a9628ffa534 | 61,056 | BPE | 60,795 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 373 | jinaai/jina-embeddings-v2-base-de | jinaai | null | 892,028 | 4,077,798 | False | 2024-01-12T14:04:50Z | 2025-01-06T16:26:47Z | sentence-transformers | 82 | 0 | [{"name": "jina-embeddings-v2-base-de", "results": [{"task": {"type": "Classification"}, "dataset": {"type": "mteb/amazon_counterfactual", "name": "MTEB AmazonCounterfactualClassification (en)", "config": "en", "split": "test", "revision": "e8379541af4e31359cca9fbcf4b00f2671dba205"}, "metrics": [{"type": "accuracy", "v... | feature-extraction | {"parameters": {"F16": 160813824}, "total": 160813824} | [
".gitattributes",
"1_Pooling/config.json",
"README.md",
"config.json",
"config_sentence_transformers.json",
"de_evaluation_results.png",
"merges.txt",
"model.safetensors",
"modules.json",
"onnx/model.onnx",
"onnx/model_fp16.onnx",
"onnx/model_quantized.onnx",
"pytorch_model.bin",
"sentence... | 3f9eede875721714945b6a99a3198299243cf2be | [
"sentence-transformers",
"pytorch",
"onnx",
"safetensors",
"bert",
"fill-mask",
"feature-extraction",
"sentence-similarity",
"mteb",
"transformers",
"transformers.js",
"custom_code",
"de",
"en",
"arxiv:2108.12409",
"arxiv:2402.17016",
"license:apache-2.0",
"model-index",
"text-em... | null | {"architectures": ["JinaBertForMaskedLM"], "auto_map": {"AutoConfig": "jinaai/jina-bert-implementation--configuration_bert.JinaBertConfig", "AutoModelForMaskedLM": "jinaai/jina-bert-implementation--modeling_bert.JinaBertForMaskedLM", "AutoModel": "jinaai/jina-bert-implementation--modeling_bert.JinaBertModel", "AutoMode... | {
"auto_model": "AutoModelForMaskedLM",
"custom_class": "jinaai/jina-bert-implementation--modeling_bert.JinaBertForMaskedLM",
"pipeline_tag": "fill-mask",
"processor": "AutoTokenizer"
} | {"language": ["de", "en"], "license": "apache-2.0", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "mteb", "transformers", "transformers.js"], "inference": false, "model-index": [{"name": "jina-embeddings-v2-base-de", "results": [{"task": {"type": "Classification"}, "dataset": {"name": "... | <!-- TODO: add evaluation results here -->
<br><br>
<p align="center">
<img src="https://huggingface.co/datasets/jinaai/documentation-images/resolve/main/logo.webp" alt="Jina AI: Your Search Foundation, Supercharged!" width="150px">
</p>
<p align="center">
<b>The text embedding set trained by <a href="https://jina.a... | null | [
"apache-2.0"
] | null | [
"de",
"en"
] | 160,813,824 | null | null | [
"JinaBertForMaskedLM",
"AutoModelForMaskedLM",
"bert",
"jinaai/jina-bert-implementation--modeling_bert.JinaBertForMaskedLM"
] | [
"fill-mask",
"sentence-similarity",
"feature-extraction"
] | [
"text",
"multimodal"
] | [
"text"
] | [
"logits",
"embeddings"
] |
621ffdc136468d709f17a4ca | 4462d2f99742096c | d2de3de4f9a47b28 | 50,265 | BPE | 50,004 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 374 | dbmdz/german-gpt2 | dbmdz | null | 9,597 | 918,400 | False | 2022-03-02T23:29:05Z | 2023-12-13T17:41:57Z | transformers | 30 | 0 | null | text-generation | {"parameters": {"F32": 124445952, "U8": 12582912}, "total": 137028864} | [
".gitattributes",
"README.md",
"config.json",
"flax_model.msgpack",
"model.safetensors",
"onnx/added_tokens.json",
"onnx/config.json",
"onnx/decoder_model.onnx",
"onnx/decoder_model_merged.onnx",
"onnx/decoder_with_past_model.onnx",
"onnx/generation_config.json",
"onnx/merges.txt",
"onnx/spe... | ab6efd04479f70d66df40e7bfcb17ba41e9cd6d5 | [
"transformers",
"pytorch",
"tf",
"jax",
"onnx",
"safetensors",
"gpt2",
"text-generation",
"de",
"doi:10.57967/hf/5768",
"license:mit",
"text-generation-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["GPT2LMHeadModel"], "model_type": "gpt2"} | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": "de", "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null, "widget": [{"text": "Heute ist sehr sch\u00f6nes Wetter in"}]} | # German GPT-2 model
In this repository we release (yet another) GPT-2 model, that was trained on various texts for German.
The model is meant to be an entry point for fine-tuning on other texts, and it is definitely not as good or "dangerous" as the English GPT-3 model. We do not plan extensive PR or staged releases... | null | [
"mit"
] | null | [
"de"
] | 137,028,864 | null | null | [
"GPT2LMHeadModel",
"AutoModelForCausalLM",
"gpt2"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
621ffdc136468d709f17def9 | 861336d2f27759a2 | 7bccc9177292b52a | 50,267 | BPE | 50,000 | false | true | true | true | 7 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 375 | microsoft/markuplm-base | microsoft | null | 225,673 | 1,541,430 | False | 2022-03-02T23:29:05Z | 2022-12-15T13:59:57Z | transformers | 27 | 0 | null | null | null | [
".gitattributes",
"README.md",
"added_tokens.json",
"config.json",
"merges.txt",
"preprocessor_config.json",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | 7b1ef356189e6803bd5b58b305c783fd1d395ae6 | [
"transformers",
"pytorch",
"markuplm",
"en",
"arxiv:2110.08518",
"endpoints_compatible",
"region:us"
] | null | {"architectures": ["MarkupLMForPretraining"], "model_type": "markuplm", "tokenizer_config": {"bos_token": {"__type": "AddedToken", "content": "<s>", "lstrip": false, "normalized": true, "rstrip": false, "single_word": false}, "cls_token": {"__type": "AddedToken", "content": "<s>", "lstrip": false, "normalized": true, "... | {
"auto_model": "MarkupLMForPretraining",
"custom_class": null,
"pipeline_tag": null,
"processor": "AutoProcessor"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": ["en"], "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": null} | # MarkupLM
**Multimodal (text +markup language) pre-training for [Document AI](https://www.microsoft.com/en-us/research/project/document-ai/)**
## Introduction
MarkupLM is a simple but effective multi-modal pre-training method of text and markup language for visually-rich document understanding and information extra... | null | null | null | [
"en"
] | null | null | null | [
"markuplm",
"MarkupLMForPretraining"
] | [
null
] | null | null | null |
621ffdc036468d709f1769d5 | 128f4d40fdb1ae99 | 82ae3e9dca39a0cc | 32,005 | BPE | 66,688 | true | true | true | true | 7 | Sequence | Metaspace | Metaspace | [
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 301 | MilaNLProc/feel-it-italian-sentiment | MilaNLProc | null | 10,518 | 1,115,525 | False | 2022-03-02T23:29:04Z | 2022-08-15T20:35:54Z | transformers | 21 | 0 | null | text-classification | null | [
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"README.md",
"config.json",
"pytorch_model.bin",
"sentencepiece.bpe.model",
"special_tokens_map.json",
"tf_model.h5",
"tokenizer.json",
"tokenizer_config.json",
"trainer_state.json",
"training_args.bin"
] | 98744f71b7b3a47ba00d57b2736c3af794c417ff | [
"transformers",
"pytorch",
"tf",
"camembert",
"text-classification",
"sentiment",
"Italian",
"it",
"text-embeddings-inference",
"endpoints_compatible",
"deploy:azure",
"region:us"
] | null | {"architectures": ["CamembertForSequenceClassification"], "model_type": "camembert", "tokenizer_config": {"bos_token": "<s>", "eos_token": "</s>", "sep_token": "</s>", "cls_token": "<s>", "unk_token": "<unk>", "pad_token": "<pad>", "mask_token": {"content": "<mask>", "single_word": false, "lstrip": true, "rstrip": fals... | {
"auto_model": "AutoModelForSequenceClassification",
"custom_class": null,
"pipeline_tag": "text-classification",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": null, "eval_results": null, "language": "it", "library_name": null, "license": null, "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": null, "tags": ["sentiment", "Italian"]} | # FEEL-IT: Emotion and Sentiment Classification for the Italian Language
## FEEL-IT Python Package
You can find the package that uses this model for emotion and sentiment classification **[here](https://github.com/MilaNLProc/feel-it)** it is meant to be a very simple interface over HuggingFace models.
## License
Us... | null | null | null | [
"it"
] | null | null | null | [
"AutoModelForSequenceClassification",
"camembert",
"CamembertForSequenceClassification"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
64f50aefdc236e299f4370f2 | c88b36417e2a82ee | dd84a2b1ff97657d | 250,002 | Unigram | null | true | true | true | true | 5 | Precompiled | Sequence | Metaspace | [
"Precompiled"
] | [
"WhitespaceSplit",
"Metaspace"
] | [
"Metaspace"
] | 64 | Arunavaonly/Bangla-twoclass-Sentiment-Analyzer | Arunavaonly | {
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{
"_id": "621ffdc036468d709f174364",
"id": "FacebookAI/xlm-roberta-base"
}
],
"relation": "finetune"
} | 118,397 | 1,463,453 | False | 2023-09-03T22:38:39Z | 2024-02-24T14:08:00Z | transformers | 1 | 0 | [{"name": "Bangla-Twoclass-Sentiment-Analyzer", "results": []}] | text-classification | {"parameters": {"F32": 278045955}, "total": 278045955} | [
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] | b2b3ca5db5eaf56a8542ee9deff888d5fa1b5cad | [
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"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"text-embeddings-inference",
"endpoints_compatible",
"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": "xlm-roberta-base", "datasets": null, "eval_results": [], "language": null, "library_name": null, "license": "mit", "license_name": null, "license_link": null, "metrics": ["f1"], "model_name": "Bangla-Twoclass-Sentiment-Analyzer", "pipeline_tag": null, "tags": ["generated_from_trainer"]} | <!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Bangla-Twoclass-Sentiment-Analyzer
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-ba... | null | [
"mit"
] | null | null | 278,045,955 | null | [
"f1"
] | [
"AutoModelForSequenceClassification",
"XLMRobertaForSequenceClassification",
"xlm-roberta"
] | [
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
65521fe64a896912bc8c6ae4 | 35f8a93a304f6b78 | 70bd27c212bc339c | 50,265 | BPE | 50,000 | false | true | true | true | 5 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 6 | protectai/unbiased-toxic-roberta-onnx | protectai | {
"models": [
{
"_id": "621ffdc136468d709f181280",
"id": "unitary/unbiased-toxic-roberta"
}
],
"relation": "quantized"
} | 8,823 | 951,686 | False | 2023-11-13T13:08:54Z | 2024-03-25T12:01:40Z | transformers | 5 | 0 | null | token-classification | null | [
".gitattributes",
"README.md",
"config.json",
"merges.txt",
"model.onnx",
"model_quantized.onnx",
"special_tokens_map.json",
"tokenizer.json",
"tokenizer_config.json",
"vocab.json"
] | 34480fa958f6657ad835c345808475755b6974a7 | [
"transformers",
"onnx",
"roberta",
"text-classification",
"toxicity",
"bias",
"token-classification",
"en",
"base_model:unitary/unbiased-toxic-roberta",
"base_model:quantized:unitary/unbiased-toxic-roberta",
"license:apache-2.0",
"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": "unitary/unbiased-toxic-roberta", "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": "token-classification", "tags": ["toxicity", "bias", "roberta"], "i... | # ONNX version of unitary/unbiased-toxic-roberta
**This model is a conversion of [unitary/unbiased-toxic-roberta](https://huggingface.co/unitary/unbiased-toxic-roberta) to ONNX** format using the [🤗 Optimum](https://huggingface.co/docs/optimum/index) library.
Trained models & code to predict toxic comments on 3 Jigs... | null | [
"apache-2.0"
] | null | [
"en"
] | null | null | null | [
"roberta",
"AutoModelForSequenceClassification",
"RobertaForSequenceClassification"
] | [
"token-classification",
"text-classification"
] | [
"text"
] | [
"text"
] | [
"logits"
] |
66aa7456b76cafa058e215ca | 1e0a2493163c0c63 | 6622292dca275a2d | 128,256 | BPE | 280,147 | false | true | true | true | 256 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 18 | RedHatAI/Meta-Llama-3.1-70B-Instruct-quantized.w4a16 | RedHatAI | {
"models": [
{
"_id": "66969ad27a033bf62173f3e2",
"id": "meta-llama/Llama-3.1-70B-Instruct"
}
],
"relation": "quantized"
} | 69,113 | 1,438,682 | False | 2024-07-31T17:28:54Z | 2025-02-12T16:49:27Z | transformers | 32 | 0 | null | text-generation | {"parameters": {"I32": 68451041280, "F16": 2102665216}, "total": 70553706496} | [
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] | 58eb42279d256f1cbc366dcd69d98d9f2dceff17 | [
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"license:llam... | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "quantization_config": {"bits": 4, "quant_method": "gptq"}, "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_... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": "meta-llama/Meta-Llama-3.1-70B-Instruct", "datasets": null, "eval_results": null, "language": ["en", "de", "fr", "it", "pt", "hi", "es", "th"], "library_name": null, "license": "llama3.1", "license_name": null, "license_link": null, "metrics": null, "model_name": null, "pipeline_tag": "text-generation", ... | null | null | [
"llama3.1"
] | null | [
"en",
"de",
"fr",
"it",
"pt",
"hi",
"es",
"th"
] | 70,553,706,496 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
67f65cc77168f9524be92e01 | 898a7551a427cee1 | 10028293713294b5 | 49,159 | BPE | 48,891 | false | true | true | true | 26 | null | ByteLevel | ByteLevel | null | [
"ByteLevel"
] | [
"ByteLevel"
] | 376 | ibm-granite/granite-3.3-2b-instruct | ibm-granite | {
"models": [
{
"_id": "67f65cb3aa7d255cc3c39a9f",
"id": "ibm-granite/granite-3.3-2b-base"
}
],
"relation": "finetune"
} | 55,198 | 1,386,382 | False | 2025-04-09T11:40:55Z | 2025-05-08T15:01:59Z | transformers | 84 | 0 | null | text-generation | null | [
".gitattributes",
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"vocab.json... | 707f574c62054322f6b5b04b6d075f0a8f05e0f0 | [
"transformers",
"safetensors",
"granite",
"text-generation",
"language",
"granite-3.3",
"conversational",
"base_model:ibm-granite/granite-3.3-2b-base",
"base_model:finetune:ibm-granite/granite-3.3-2b-base",
"license:apache-2.0",
"region:us"
] | null | {"architectures": ["GraniteForCausalLM"], "model_type": "granite", "tokenizer_config": {"bos_token": "<|end_of_text|>", "chat_template": "{# Alias tools -> available_tools #}\n{%- if tools and not available_tools -%}\n {%- set available_tools = tools -%}\n{%- endif -%}\n{%- if messages[0]['role'] == 'system' %}\n ... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": ["ibm-granite/granite-3.3-2b-base"], "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-3.3"], ... | # Granite-3.3-2B-Instruct
**Model Summary:**
Granite-3.3-2B-Instruct is a 2-billion parameter 128K context length language model fine-tuned for improved reasoning and instruction-following capabilities. Built on top of Granite-3.3-2B-Base, the model delivers significant gains on benchmarks for measuring generic perfor... | null | [
"apache-2.0"
] | null | null | null | null | null | [
"granite",
"AutoModelForCausalLM",
"GraniteForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
66217027acf8bd8a819b933f | 803e3c8528c55bf1 | a1f1bd381dcb9329 | 128,256 | BPE | 280,147 | false | true | true | true | 256 | null | Sequence | ByteLevel | null | [
"Split",
"ByteLevel"
] | [
"ByteLevel"
] | 352 | casperhansen/llama-3-70b-instruct-awq | casperhansen | null | 2,543 | 902,895 | False | 2024-04-18T19:10:31Z | 2024-04-19T21:19:18Z | transformers | 70 | 0 | null | text-generation | {"parameters": {"I32": 68451041280, "F16": 2102665216}, "total": 70553706496} | [
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"4-bit",
"awq",
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] | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "quantization_config": {"bits": 4, "quant_method": "awq"}, "tokenizer_config": {"bos_token": "<|begin_of_text|>", "chat_template": "{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] ... | {
"auto_model": "AutoModelForCausalLM",
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"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | null | null | null | null | null | null | 70,553,706,496 | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
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] | [
"text"
] | [
"text"
] |
66b47e7009a06ad236a1556e | 2d0341bd3a73bae6 | ef4806e2ade9022f | 32,100 | Unigram | null | true | true | true | true | 103 | Sequence | Metaspace | Metaspace | [
"Precompiled",
"Replace"
] | [
"Metaspace"
] | [
"Metaspace"
] | 32 | parler-tts/parler-tts-large-v1 | parler-tts | null | 10,342 | 1,132,727 | False | 2024-08-08T08:14:40Z | 2024-11-22T16:17:20Z | transformers | 272 | 1 | null | text-to-speech | {"parameters": {"F32": 2333013362}, "total": 2333013362} | [
".gitattributes",
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"config.json",
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"model.safetensors.index.json",
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"requirements.txt",
"special_tokens_map.json",
"spiece.model",
"tokenizer.... | 50cb4b874c83902f930d7c2e753224c15654f11e | [
"transformers",
"safetensors",
"parler_tts",
"text-generation",
"text-to-speech",
"annotation",
"en",
"dataset:parler-tts/mls_eng",
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"dataset:parler-tts/libritts-r-filtered-speaker-descriptions",
"dataset:parler-tts/mls-eng-speaker-descriptions",
"arxiv... | null | {"architectures": ["ParlerTTSForConditionalGeneration"], "model_type": "parler_tts", "tokenizer_config": {"eos_token": "</s>", "pad_token": "<pad>", "unk_token": "<unk>"}} | {
"auto_model": "AutoModelForSeq2SeqLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": null
} | {"base_model": null, "datasets": ["parler-tts/mls_eng", "parler-tts/libritts_r_filtered", "parler-tts/libritts-r-filtered-speaker-descriptions", "parler-tts/mls-eng-speaker-descriptions"], "eval_results": null, "language": ["en"], "library_name": "transformers", "license": "apache-2.0", "license_name": null, "license_l... | <img src="https://huggingface.co/datasets/parler-tts/images/resolve/main/thumbnail.png" alt="Parler Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
# Parler-TTS Large v1
<a target="_blank" href="https://huggingface.co/spaces/parler-tts/parler_tts">
<img src="https://huggingface.c... | null | [
"apache-2.0"
] | [
"parler-tts/mls_eng",
"parler-tts/libritts_r_filtered",
"parler-tts/libritts-r-filtered-speaker-descriptions",
"parler-tts/mls-eng-speaker-descriptions"
] | [
"en"
] | 2,333,013,362 | null | null | [
"AutoModelForSeq2SeqLM",
"ParlerTTSForConditionalGeneration",
"parler_tts"
] | [
"text-to-speech",
"text-generation"
] | [
"text",
"audio"
] | [
"text"
] | [
"text",
"audio"
] |
64b94e28ee257c3a4c00b45b | 6949732424a8d297 | 8bd7d71767538b82 | 32,000 | BPE | 61,249 | true | false | true | true | 3 | Sequence | null | Sequence | [
"Prepend",
"Replace"
] | null | [
"Replace",
"ByteFallback",
"Fuse",
"Strip"
] | 42 | stabilityai/StableBeluga2 | stabilityai | null | 1,113 | 888,762 | False | 2023-07-20T15:09:28Z | 2023-09-18T15:55:32Z | transformers | 881 | 0 | null | text-generation | null | [
".gitattributes",
"LICENSE.txt",
"LLAMA 2 LICENSE/LICENSE.txt",
"LLAMA 2 LICENSE/Responsible-Use-Guide.pdf",
"LLAMA 2 LICENSE/USE_POLICY.md",
"README.md",
"config.json",
"generation_config.json",
"pytorch_model-00001-of-00029.bin",
"pytorch_model-00002-of-00029.bin",
"pytorch_model-00003-of-0002... | cb47d3db70ea3ddc2cabdeb358c303b328f65900 | [
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"llama",
"text-generation",
"en",
"dataset:conceptofmind/cot_submix_original",
"dataset:conceptofmind/flan2021_submix_original",
"dataset:conceptofmind/t0_submix_original",
"dataset:conceptofmind/niv2_submix_original",
"arxiv:2307.09288",
"arxiv:2306.02707",
"text-ge... | null | {"architectures": ["LlamaForCausalLM"], "model_type": "llama", "tokenizer_config": {"bos_token": {"__type": "AddedToken", "content": "<s>", "lstrip": false, "normalized": true, "rstrip": false, "single_word": false}, "eos_token": {"__type": "AddedToken", "content": "</s>", "lstrip": false, "normalized": true, "rstrip":... | {
"auto_model": "AutoModelForCausalLM",
"custom_class": null,
"pipeline_tag": "text-generation",
"processor": "AutoTokenizer"
} | {"base_model": null, "datasets": ["conceptofmind/cot_submix_original", "conceptofmind/flan2021_submix_original", "conceptofmind/t0_submix_original", "conceptofmind/niv2_submix_original"], "eval_results": null, "language": ["en"], "library_name": null, "license": null, "license_name": null, "license_link": null, "metric... | # Stable Beluga 2
Use [Stable Chat (Research Preview)](https://chat.stability.ai/chat) to test Stability AI's best language models for free
## Model Description
`Stable Beluga 2` is a Llama2 70B model finetuned on an Orca style Dataset
## Usage
Start chatting with `Stable Beluga 2` using the following code snippet... | null | null | [
"conceptofmind/cot_submix_original",
"conceptofmind/flan2021_submix_original",
"conceptofmind/t0_submix_original",
"conceptofmind/niv2_submix_original"
] | [
"en"
] | null | null | null | [
"AutoModelForCausalLM",
"llama",
"LlamaForCausalLM"
] | [
"text-generation"
] | [
"text"
] | [
"text"
] | [
"text"
] |
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