LFM2.5-24B-A2B-MLX-DWQ-3bit-v2
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Nemotron-3.5-Bolt-74B-A9B-MLX-mixed46
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View details ↗Ornith-1.5-122B-MLX-Mixed2-3-8bit-MTP
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View details ↗Spark-X2.6-4B-MLX-SLQ-4.50bpw
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View details ↗heron1i-flash-lite-v2
Heronetic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 ↗r16-v1
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View details ↗r16-v2
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View details ↗r17
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View details ↗GGUF quantizations of `hesamation/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled`, a reasoning SFT fine-tune of `Qwen/Qwen3.6-35B-A3B` on Claude Opus 4.6-style chain-of-thought distillation data. The source fine-tune is text-only. The Qwen3.6 base architecture includes a vision encoder, but this fine-tuning run did not train on image or video examples. Treat these GGUF files as text-generation/runtime quantizations of the merged fine-tuned checkpoint. - **Developed by:** @hesamation - **Source model:** `hesamation/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled` - **Base model:** `Qwen/Qwen3.6-35B-A3B` - **License:** apache-2.0
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View details ↗qwen-3.8-27b-abliterated
heterodoxin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 ↗GmshNet-8B-v0.1
hexera-org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 ↗GmshNet-8B-v0.1-GGUF
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View details ↗Kokoro-82M
hexgrad**Kokoro** is an open-weight TTS model with 82 million parameters. Despite its lightweight architecture, it delivers comparable quality to larger models while being significantly faster and more cost-efficient. With Apache-licensed weights, Kokoro can be deployed anywhere from production environments to personal projects. > [!NOTE] > As of April 2025, the market rate of Kokoro served over API is **under $1 per million characters of text input**, or under $0.06 per hour of audio output. (On average, 1000 characters of input is about 1 minute of output.) Sources: ArtificialAnalysis/Replicate at 65 cents per M chars and DeepInfra at 80 cents per M chars. > > This is an Apache-licensed model, and Kokoro has been deployed in numerous projects and commercial APIs. We welcome the deployment of the model in real use cases. > [!CAUTION] > Fake websites like kokorottsai_com (snapshot: https://archive.ph/nRRnk) and kokorotts_net (snapshot: https://archive.ph/60opa) are likely scams masquerading under the banner of a popular model. > > Any website containing "kokoro" in its root domain (e.g. kokorottsai_com, kokorotts_net) is **NOT owned by and NOT affiliated with this model page or its author**, and attempts to imply otherwise are red flags.
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View details ↗Kokoro-82M-v1.1-zh
hexgradThis model is the result of a short training run that added 100 Chinese speakers from a professional dataset. The Chinese data was freely and permissively granted to us by LongMaoData, a professional dataset company. Thank you for making this model possible. Separately, some crowdsourced synthetic English data also entered the training mix: [1] - 1 hour of Maple, an American female. - 1 hour of Sol, another American female. - And 1 hour of Vale, an older British female. This model is not a strict upgrade over its predecessor since it drops many voices, but it is released early to gather feedback on new voices and tokenization. Aside from the Chinese dataset and the 3 hours of English, the rest of the data was left behind for this training run. The goal is to push the model series forward and ultimately restore some of the voices that were left behind.
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View details ↗DeepWind1.0-33M
Hexian-2001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 ↗vyra-yolo-ppe-detection
Hexmon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 ↗persian-embeddings
heydariAIThis model is a fine-tuned version of xlm-roberta-base, specifically trained on a massive corpus of Persian data to create high-quality contextual embeddings for Persian sentences and paragraphs. It is designed to perform exceptionally well on tasks such as semantic search, clustering, and contextual similarity for Persian text, while also supporting multilingual tasks in English and Persian. The fine-tuning process focused on adapting the pre-trained multilingual XLM-RoBERTa model to better capture Persian linguistic nuances, making it highly effective for tasks requiring embeddings tailored to the Persian language. Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
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View details ↗Qwen3.5-27B-abliterated-GGUF
HeYujiehuihui-ai/Huihui-Qwen3.5-27B-abliterated is an uncensored version of Qwen/Qwen3.5-27B created with abliteration (see remove-refusals-with-transformers to know more about it). This is a crude, proof-of-concept implementation to remove refusals from an LLM model without using TransformerLens.
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View details ↗This repository contains the GGUF quantized versions of huihui-ai/Huihui-Qwen3.5-35B-A3B-abliterated huihui-ai/Huihui-Qwen3.5-35B-A3B-abliterated is an uncensored version of Qwen/Qwen3.5-35B-A3B created with abliteration (see remove-refusals-with-transformers to know more about it). This is a crude, proof-of-concept implementation to remove refusals from an LLM model without using TransformerLens.
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View details ↗vedu-scribe-0.8b
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View details ↗CRAFT
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