wav2vec2-mic
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whisper-mic
huggingface-mic基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗whisper-tiny-mic
huggingface-mic基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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time-series-transformer-tourism-monthly
huggingface基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Carbon-3B-GGUF
HuggingFaceBioK-quants are produced with `--token-embedding-type Q8_0 --output-tensor-type Q8_0` so the sensitive embedding/output tensors stay at Q8_0 precision (important for this model's tight DNA-vs-text logit margins). Click **Use this model > llama.cpp** on this page for a ready-made snippet. To install llama.cpp, follow llama.app or download a prebuilt binary from the releases page. Carbon-3B is a **base** DNA model, not a chat model, so use text completion rather than the chat UI. Start the prompt with ` `:
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View details ↗Carbon-500M
HuggingFaceBio基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Carbon-500M-GGUF
HuggingFaceBio基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Carbon-8B
HuggingFaceBioA larger, higher-capacity member of the **Carbon** family of generative DNA foundation models. Carbon-8B is the 8B-parameter sibling of Carbon-3B. It is intended for users who can afford additional inference cost in exchange for stronger downstream performance. For the full design rationale, tokenizer specification, evaluation protocol, and usage details, please refer to the **Carbon-3B model card** and the Carbon technical report — this card focuses only on what is specific to Carbon-8B. - Technical report: https://www.biorxiv.org/content/10.64898/2026.05.22.727119v1 - Demo: https://huggingface.co/spaces/HuggingFaceBio/carbon-demo
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View details ↗Carbon-8B-GGUF
HuggingFaceBioK-quants are produced with `--token-embedding-type Q8_0 --output-tensor-type Q8_0` so the sensitive embedding/output tensors stay at Q8_0 precision (important for this model's tight DNA-vs-text logit margins). Click **Use this model > llama.cpp** on this page for a ready-made snippet. To install llama.cpp, follow llama.app or download a prebuilt binary from the releases page. Carbon-8B is a **base** DNA model, not a chat model, so use text completion rather than the chat UI. Start the prompt with ` `:
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View details ↗Carbon-A-1.2B
HuggingFaceBio基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Carbon-X
HuggingFaceBio基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Carbon-X-GGUF
HuggingFaceBioGGUF quantizations of Carbon-X, an open 14B language model for DNA understanding and generation from the **Carbon** family, for local inference with llama.cpp. Carbon-X reads raw DNA inside an ordinary chat conversation and answers in English: it predicts whether mutations are benign or pathogenic, identifies regions that control gene activity, classifies sequences by organism group, predicts the next bases of a sequence and explains its reasoning. A hybrid tokenizer keeps a BPE vocabulary for text and encodes DNA as non-overlapping 6-mers. See the Carbon-X model card for details on the model and its limitations. > [!IMPORTANT] > These files need llama.cpp b11742 or newer, the first > build with support for the Carbon `HybridDNATokenizer` (#30825). > Older builds, including the v0.7.0 release, load them but tokenize DNA wrongly.
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View details ↗ablation-model-fineweb-edu
HuggingFaceFW基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗fineweb-edu-classifier
HuggingFaceFWThis is a classifier for judging the educational value of web pages. It was developed to filter and curate educational content from web datasets and was trained on 450k annotations generated by LLama3-70B-instruct for web samples from FineWeb dataset. We used this classifier to build FineWeb-Edu dataset. To load the FineWeb-Edu classifier, use the following code: The classifier was trained on 450,000 pairs of web samples and their scores from 0 to 5, generated by Llama3. The samples were annotated based on their educational quality with 0 being not educational and 5 being highly educational.
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View details ↗mistral-7b-sft-beta
HuggingFaceH4基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗starchat-alpha
HuggingFaceH4基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗starchat-beta
HuggingFaceH4基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗tiny-random-LlamaForCausalLM
HuggingFaceH4基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗tiny-random-LlamaForSequenceClassification
HuggingFaceH4基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗zephyr-7b-alpha
HuggingFaceH4Zephyr is a series of language models that are trained to act as helpful assistants. Zephyr-7B-α is the first model in the series, and is a fine-tuned version of mistralai/Mistral-7B-v0.1 that was trained on on a mix of publicly available, synthetic datasets using Direct Preference Optimization (DPO). We found that removing the in-built alignment of these datasets boosted performance on MT Bench and made the model more helpful. However, this means that model is likely to generate problematic text when prompted to do so. The model was initially fine-tuned on a variant of the `UltraChat` dataset, which contains a diverse range of synthetic dialogues generated by ChatGPT. We then further aligned the model with 🤗 TRL's `DPOTrainer` on the openbmb/UltraFeedback dataset, which contain 64k prompts and model completions that are ranked by GPT-4. As a result, the model can be used for chat and you can check out our demo to test its capabilities. Zephyr-7B-α has not been aligned to human preferences with techniques like RLHF or deployed with in-the-loop filtering of responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so). It is also unknown what the size and composition of the corpus was used to train the base model (`mistralai/Mistral-7B-v0.1`), however it is likely to have included a mix of Web data and technical sources like books and code. See the Falcon 180B model card for an example of this.
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View details ↗zephyr-7b-beta
HuggingFaceH4Zephyr is a series of language models that are trained to act as helpful assistants. Zephyr-7B-β is the second model in the series, and is a fine-tuned version of mistralai/Mistral-7B-v0.1 that was trained on on a mix of publicly available, synthetic datasets using Direct Preference Optimization (DPO). We found that removing the in-built alignment of these datasets boosted performance on MT Bench and made the model more helpful. However, this means that model is likely to generate problematic text when prompted to do so. You can find more details in the technical report. - **Model type:** A 7B parameter GPT-like model fine-tuned on a mix of publicly available, synthetic datasets. - **Language(s) (NLP):** Primarily English - **License:** MIT - **Finetuned from model:** mistralai/Mistral-7B-v0.1 - **Repository:** https://github.com/huggingface/alignment-handbook - **Demo:** https://huggingface.co/spaces/HuggingFaceH4/zephyr-chat - **Chatbot Arena:** Evaluate Zephyr 7B against 10+ LLMs in the LMSYS arena: http://arena.lmsys.org
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View details ↗Florence-2-DocVQA
HuggingFaceM4基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗idefics-80b-instruct
HuggingFaceM4IDEFICS (**I**mage-aware **D**ecoder **E**nhanced à la **F**lamingo with **I**nterleaved **C**ross-attention**S**) is an open-access reproduction of Flamingo, a closed-source visual language model developed by Deepmind. Like GPT-4, the multimodal model accepts arbitrary sequences of image and text inputs and produces text outputs. IDEFICS is built solely on publicly available data and models. The model can answer questions about images, describe visual contents, create stories grounded on multiple images, or simply behave as a pure language model without visual inputs. IDEFICS is on par with the original closed-source model on various image-text benchmarks, including visual question answering (open-ended and multiple choice), image captioning, and image classification when evaluated with in-context few-shot learning. It comes into two variants: a large 80 billion parameters version and a 9 billion parameters version.
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View details ↗idefics-9b
HuggingFaceM4IDEFICS (**I**mage-aware **D**ecoder **E**nhanced à la **F**lamingo with **I**nterleaved **C**ross-attention**S**) is an open-access reproduction of Flamingo, a closed-source visual language model developed by Deepmind. Like GPT-4, the multimodal model accepts arbitrary sequences of image and text inputs and produces text outputs. IDEFICS is built solely on publicly available data and models. The model can answer questions about images, describe visual contents, create stories grounded on multiple images, or simply behave as a pure language model without visual inputs. IDEFICS is on par with the original closed-source model on various image-text benchmarks, including visual question answering (open-ended and multiple choice), image captioning, and image classification when evaluated with in-context few-shot learning. It comes into two variants: a large 80 billion parameters version and a 9 billion parameters version.
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