bert_regard_v2_large
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avibe
AvitoTech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 ↗SigLIP2-giant
AvitoTech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 ↗qwen3.5-4b-gguf
avnishrai1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 ↗GIST-all-MiniLM-L6-v2
avsolatorio*GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning* The model is fine-tuned on top of the sentence-transformers/all-MiniLM-L6-v2 using the MEDI dataset augmented with mined triplets from the MTEB Classification training dataset (excluding data from the Amazon Polarity Classification task). The model does not require any instruction for generating embeddings. This means that queries for retrieval tasks can be directly encoded without crafting instructions.
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View details ↗GIST-Embedding-v0
avsolatorio*GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning* The model is fine-tuned on top of the BAAI/bge-base-en-v1.5 using the MEDI dataset augmented with mined triplets from the MTEB Classification training dataset (excluding data from the Amazon Polarity Classification task). The model does not require any instruction for generating embeddings. This means that queries for retrieval tasks can be directly encoded without crafting instructions.
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View details ↗GIST-large-Embedding-v0
avsolatorio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 ↗GIST-small-Embedding-v0
avsolatorio*GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning* The model is fine-tuned on top of the BAAI/bge-small-en-v1.5 using the MEDI dataset augmented with mined triplets from the MTEB Classification training dataset (excluding data from the Amazon Polarity Classification task). The model does not require any instruction for generating embeddings. This means that queries for retrieval tasks can be directly encoded without crafting instructions.
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View details ↗Qwen3.8-27B-AWQ-INT4
avyukth4-bit weight quantisation of Qwen/Qwen3.8-27B, built so the model fits a single **24 GB** consumer GPU. > Note on the `qwen3_5` tag: Qwen3.8 is implemented with the Qwen3.5 > architecture (`model_type: qwen3_5`, `Qwen3_5ForConditionalGeneration`), so > the Hub derives that family tag automatically — the upstream > `Qwen/Qwen3.8-27B` repo carries it too. This is Qwen **3.8**. Text-only serving reclaims the vision encoder cache (~5k extra KV tokens on a 24 GB card):
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View details ↗whisper-turbo-v3-urdu
awaaz-se-alfaaz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 ↗byt5-german-grammar
aware-ai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 ↗M2-2B
AwareLiquid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 ↗aci-yoruba-phaseX
Awesome-x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 ↗dse-bert-base
aws-ai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 ↗MegaBeam-Mistral-7B-300k
aws-prototypingMegaBeam-Mistral-7B-300k is a fine-tuned Mistral-7B-Instruct-v0.2 language model that supports input contexts up to 320k tokens. MegaBeam-Mistral-7B-300k can be deployed on a single AWS `g5.48xlarge` instance using serving frameworks such as vLLM, Sagemaker DJL endpoint, and others. Similarities and differences beween MegaBeam-Mistral-7B-300k and Mistral-7B-Instruct-v0.2 are summarized below: _InfiniteBench is a cutting-edge benchmark tailored for evaluating the capabilities of language models to process, understand, and reason over super long contexts (100k+ tokens)_. We therefore evaluated MegaBeam-Mistral-7B-300k, Mistral-7B-Instruct-v0.2, Llama-3-8B-Instruct-262k, and Llama3-70B-1M on InfiniteBench. The InfiniteBench authors also evaluated SOTA proprietary and open-source LLMs on InfiniteBench. We thus combined both results in the table below. The 12 evaluation tasks are summarized below (as per InfiniteBench)) | Task Name | Context | # Examples | Avg Input Tokens | Avg Output Tokens | Description | | -------------------- | ------------- | ---------- | ---------------- | ----------------- | ------------------------------------------------------------------------------------------- | | En.Sum | Fake Book | 103 | 171.5k | 1.1k | Summarization of a fake book created with core entity substitution. | | En.QA | Fake Book | 351 | 192.6k | 4.8 | Free-form question answering based on the fake book. | | En.MC | Fake Book | 229 | 184.4k | 5.3 | Multiple choice questions derived from the fake book. | | En.Dia | Script | 200 | 103.6k | 3.4 | Identification of talkers in partially anonymized scripts. | | Zh.QA | New Book | 175 | 2068.6k | 6.3 | Question answering on a set of newly collected books. | | Code.Debug | Code Document | 394 | 114.7k | 4.8 | Finding which function in a code repo contains an crashing error (in multiple choice form). | | Code.Run | Synthetic | 400 | 75.2k | 1.3 | Simulating execution of multiple simple, synthetic functions. | | Math.Calc | Synthetic | 50 | 43.9k | 43.9k | Calculations involving super-long arithmetic equations. | | Math.Find | Synthetic | 350 | 87.9k | 1.3 | Finding special integers in a lengthy list. | | Retrieve.PassKey | Syntheti
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