ct2fast-opus-mt-en-fr
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ct2fast-opus-mt-es-en
michaelfeil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-Embedding-0.6B-auto
michaelfeilThe Qwen3 Embedding model series is the latest proprietary model of the Qwen family, specifically designed for text embedding and ranking tasks. Building upon the dense foundational models of the Qwen3 series, it provides a comprehensive range of text embeddings and reranking models in various sizes (0.6B, 4B, and 8B). This series inherits the exceptional multilingual capabilities, long-text understanding, and reasoning skills of its foundational model. The Qwen3 Embedding series represents significant advancements in multiple text embedding and ranking tasks, including text retrieval, code retrieval, text classification, text clustering, and bitext mining. **Exceptional Versatility**: The embedding model has achieved state-of-the-art performance across a wide range of downstream application evaluations. The 8B size embedding model ranks **No.1** in the MTEB multilingual leaderboard (as of June 5, 2025, score **70.58**), while the reranking model excels in various text retrieval scenarios. **Comprehensive Flexibility**: The Qwen3 Embedding series offers a full spectrum of sizes (from 0.6B to 8B) for both embedding and reranking models, catering to diverse use cases that prioritize efficiency and effectiveness. Developers can seamlessly combine these two modules. Additionally, the embedding model allows for flexible vector definitions across all dimensions, and both embedding and reranking models support user-defined instructions to enhance performance for specific tasks, languages, or scenarios.
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View details ↗Qwen3-Reranker-0.6B-seq
michaelfeil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 ↗rune-26b-a4b
michaelfeil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 ↗albert-base-rci-wikisql-row
michaelrglass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 ↗Qwen3.5-0.8B-NVFP4-MTP-GGUF
michaelw9999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 ↗Qwen3.6-27B-NVFP4-MTP-GGUF
michaelw9999Recently re-quantized with the latest version of advanced-gguf-quantizer tool. MTP included. This latest (6-June-2026) NVFP4 quant of Qwen3.6-27B keeps quality very high, while improving KLD, tail KLD metrics, top-token stability, and probability error compared to the earlier releases. It is faster and smaller in size. It applies my RSF scale fitting technique to the Q_K quants and uses a different tensor mix layout compared to previously. 6-June-2026: changed the MTP tensors to be NVFP4, ~130tk/s tg is now possible in some configurations on 5090. I am experimenting with a smaller verison of this model designed to be used on machines with 16GB of VRAM: Qwen3.6-27B-NVFP4-SMALL-MTP-GGUF . The SMALL version will be slower than this model but should maintain similar quality, although I am still evaluating it.
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View details ↗Qwen3.6-35B-A3B-NVFP4-MTP-GGUF
michaelw9999This repo contains two experimental NVFP4 GGUF quantizations of **Qwen3.6-35B-A3B** for `llama.cpp`. This was quantized using my experimental advanced-gguf-quantizer tool. Both models were imatrix calibrated for the first time using a new custom dataset that I am evaluating. All PPL/KLD results were measured against the same BF16 wikitest KLD base, and then compared to the official NVFP4 release by NVIDIA. Further evaluation tests are underway to identify real world performance differences between **TURBO** and **HQ**.
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View details ↗Qwopus3.6-27B-Coder-MTP-NVFP4-GGUF
michaelw9999This repo contains two experimental NVFP4 GGUF quantizations of **Jackrong**'s excellent Qwopus3.6-27-Coder for llama.cpp. This was quantized using my experimental advanced-gguf-quantizer tool. This model did **not** have any imatrix used with it, to better keep with the original model's finetuning done by Jackrong. Further evaluation tests are underway to identify real world performance differences between **TURBO** and **HQ**.
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View details ↗Qwopus3.6-27B-v2-MTP-NVFP4-GGUF
michaelw9999This is the second release of the NVFP4 version of Jackrong's Qwopus3.6-27B-v2-MTP-GGUF. 9-June-2026: Fixed MTP heads to NVFP4; performance with MTP is much improved (108tk/s tg) Please note, I am not affiliated; this is my own quantization effort made with my experimental work-in-progress advanced-gguf-quantizer. More evaluatlions will be underway, this page will be updated when those are complete. Feedback on how to improve the quantizer/this quantization is appreciated. For improved performance and quality, try my llama.cpp NVFP4-Repack with MXFP6 from: https://github.com/michaelw9999/llama.cpp/tree/nvfp4repack_mxfp6_cuda These branches are updated regularly. NVFP4 repack preloads all tensors into a CUDA tile to boost speed. It is a tiny bit slower on first load, then provides ~10% prefill boost with a small reduction in token gen seen on larger models, and an increase on smaller models. However, it also enables NVFP4 input scale , which boosts model correctness.
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View details ↗roboengine-sam
michaelyuanqwq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 ↗OvisOCR3-GGUF
michalland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 ↗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 ↗Ollama-3.2-GGUF
michelkao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 ↗emotion_text_classifier
michelleli99DistilRoBERTa-base is a transformer model that performs sentiment analysis. I fine-tuned the model on transcripts from the Friends show with the goal of classifying emotions from text data, specifically dialogue from Netflix shows or movies. The model predicts 6 Ekman emotions and a neutral class. These emotions include anger, disgust, fear, joy, neutrality, sadness, and surprise. The model is a fine-tuned version of Emotion English DistilRoBERTa-base and DistilRoBERTa-base. This model was initially trained on the following table from Emotion English DistilRoBERTa-base: It was fine-tuned on: |Name|anger|disgust|fear|joy|neutral|sadness|surprise| |---|---|---|---|---|---|---|---| |Emotion Lines (Friends)|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
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View details ↗inappropriate_text_classifier
michelleli99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 ↗NSFW_text_classifier
michelleli99DistilBERT is a transformer model that performs sentiment analysis. I fine-tuned the model on Reddit posts with the purpose of classifying not safe for work (NSFW) content, specifically text that is considered inappropriate and unprofessional. The model predicts 2 classes, which are NSFW or safe for work (SFW). It was fine-tuned on 14317 Reddit posts pulled from the (Reddit API) [https://praw.readthedocs.io/en/stable/].
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View details ↗BioLinkBERT-base
michiyasunagaBioLinkBERT-base model pretrained on PubMed abstracts along with citation link information. It is introduced in the paper LinkBERT: Pretraining Language Models with Document Links (ACL 2022). The code and data are available in this repository. This model achieves state-of-the-art performance on several biomedical NLP benchmarks such as BLURB and MedQA-USMLE. LinkBERT is a transformer encoder (BERT-like) model pretrained on a large corpus of documents. It is an improvement of BERT that newly captures **document links** such as hyperlinks and citation links to include knowledge that spans across multiple documents. Specifically, it was pretrained by feeding linked documents into the same language model context, besides a single document.
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View details ↗BioLinkBERT-large
michiyasunagaBioLinkBERT-large model pretrained on PubMed abstracts along with citation link information. It is introduced in the paper LinkBERT: Pretraining Language Models with Document Links (ACL 2022). The code and data are available in this repository. This model achieves state-of-the-art performance on several biomedical NLP benchmarks such as BLURB and MedQA-USMLE. LinkBERT is a transformer encoder (BERT-like) model pretrained on a large corpus of documents. It is an improvement of BERT that newly captures **document links** such as hyperlinks and citation links to include knowledge that spans across multiple documents. Specifically, it was pretrained by feeding linked documents into the same language model context, besides a single document.
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View details ↗LinkBERT-base
michiyasunaga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 ↗LinkBERT-large
michiyasunaga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 ↗kb-whisper-coreml
mickekringai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 ↗QWEN-EDIT_360
mickmumpitz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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