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View details ↗careerbert-jg
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View details ↗gemma-4-12B-it-GGUF
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View details ↗MiniCPM5-1B-GGUF
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View details ↗MiniCPM5-2B-GGUF
LWWZH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 ↗This is an uncensored version of Qwen/Qwen3.8-27B 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. The newly added **Huihui-Qwen3.8-27B-abliterated-Underdog-Saluki** series come from ConwayResearch/Underdog-Saluki-27B-1.0 and ukisai/Swift-1.5-Qwen3.8-27B-GSQ-RCO-GGUF. Only layers **22 to 52 (0-based indexing)** have been ablated, while the other layers remain unablated. It may come with a small disclaimer warning. Due to ConwayResearch/Underdog-Saluki-27B-1.0 not providing an imatrix file, we will attempt to perform ablation by mixing the GGUF weights of Swift-1.5 and ConwayResearch/Underdog-Saluki-27B-1.0.
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View details ↗DeepSeek-V4.1-Flash-1017 (552B MoE + 196B Engram) in 148 GB, built to run on two RTX 3090s and 128 GB of RAM with exact routing. Runs on **dsx**, a Rust + CUDA engine made for this model. For the best quality on the same machine, use dsx-ud (KL 0.269, one 305 GB download). All four run on dsx and were measured on the same eval (Lucebox 1017, 8 x 1,024 tokens against the FP8 model's top-256 log-probs). Unsloth's GGUFs were measured in llama.cpp with the same token ids and metric (tool). They also carry the Engram table, quantized to IQ4_NL (110.6 GB); dsx reads it unquantized from the official shards.
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View details ↗DeepSeek-V4.1-Flash-1017 (552B MoE + 196B Engram) in one 252 GB download for two RTX 3090s and 128 GB of RAM, with the routed experts in **T16**, dsx's own trellis format: exllamav3's bitshift trellis with a codebook made for desktop CPUs, quantized from DeepSeek's original weights. Runs on **dsx**, a Rust + CUDA engine for this model. The default package contains an IQ4_NL Engram table. The optional `q6k-engram` branch uses Q6_K instead; the quality measurements below identify the package used for each result. The quality and speed figures below belong to this model release and its recorded runtime. They are not a new measurement of every later kernel revision. See NVIDIA measurement status for the runtime settings and open correctness checks, and model release history for other packages.
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View details ↗LTX-2.5
lxxxy6LTX-2.5 — Video, Audio & World Simulation Full control and customization — self-host on your infrastructure. LTX-2.5 — Video, Audio & World Simulation Full control and customization — self-host on your infrastructure. Under $10M annual revenue Commercial and production use at no cost under the LTX-2.x Community License. Transfer of fine-tunes may require a paid license, in accordance with the LTX-2.x Community License. Read the Documentation
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View details ↗This model is distilled from the zero-shot classification pipeline on the Multilingual Sentiment dataset using this script. In reality the multilingual-sentiment dataset is annotated of course, but we'll pretend and ignore the annotations for the sake of example. Teacher model: MoritzLaurer/mDeBERTa-v3-base-mnli-xnli Teacher hypothesis template: "The sentiment of this text is {}." Student model: distilbert-base-multilingual-cased
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View details ↗URM-LLaMa-3.1-8B
LxzGordon- **Paper:** https://arxiv.org/pdf/2410.00847 - **Model:** URM-LLaMa-3.1-8B - Fine-tuned from Skywork-Reward-Llama-3.1-8B
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View details ↗d_m12
lyan0505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 ↗A **compressed-tensors NVFP4 W4A4** release of heretic-org/Qwen3.8-27B-heretic-ara with the original **BF16 vision/video tower** and all **15 native BF16 MTP tensors** retained and runtime-validated on one RTX 5090. > **Release artifact:** this repository contains the converted `nvfp4-pack-quantized` checkpoint. The raw ModelOpt export was an intermediate artifact. Conversion to compressed-tensors is required for the tested Qwen3.8 multimodal + MTP vLLM path. Primary runtime: **vLLM's Qwen3.8 CUDA 13 build**. The packed NVFP4 weights, multimodal path, and native MTP drafter were validated with the pinned image below. Hugging Face may display an automatic `8-bit` badge because two FP4 values are physically packed in each U8 storage element; the quantization is NVFP4 W4A4.
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