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AI Models 84759

  • Qwen3-Coder-30B-A3B-Instruct-Heretic-GGUF↗

    mradermacher

  • Qwen3-MOE-4x4B-16B-Jan-Polaris-Instruct-Power-House-i1-GGUF↗

    mradermacher

  • Qwen3-VL-4B-Instruct-Uncensored-abliterated-GGUF↗

    mradermacher

    weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files. (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)

  • Reverb-7b-Uncensored-DeLMAT-i1-GGUF↗

    mradermacher

  • Tess-v2.5.2-Qwen2-72B-GGUF↗

    mradermacher

  • Thaki-ASR-KO-Dialects-v1-GGUF↗

    mradermacher

  • Trismal-HyperUnion-7B-Base-v1.5v3A-Ties-GGUF↗

    mradermacher

  • Qwen3.8-35B-A3B-Distill-APEX-GGUF↗

    MrFuzzihead

    APEX (**A**daptive **P**recision for **EX**pert models) quantizations of empero-ai/Qwen3.8-35B-A3B-Distill, covering every deployment scenario from maximum quality down to ~10 GB. APEX assigns quantization precision per tensor role (routed expert / shared expert / attention / SSM) and per layer position (edge layers high, middle layers compressed), exploiting the sparsity of Mixture-of-Experts models. On the APEX reference model (Qwen3.5-35B-A3B), APEX tiers match or beat Q8_0 quality at ~60% of the size — see the APEX technical report for details. > **Note on the hardware-compatibility widget:** Hugging Face detects quantization variants from standard quant names (`Q4_K_M` etc.) in the filename. APEX tiers are mixed-precision and use APEX names, so the widget cannot classify them (this also affects other APEX repos). Use the table below for sizes; mmproj and MTP files are never classified by the widget by design.

  • KURE-Sparse-nano↗

    nlpai-lab

  • DeepSeek-V3-0324-NVFP4↗

    NVIDIA

    The NVIDIA DeepSeek V3-0324 FP4 model is the quantized version of DeepSeek AI's DeepSeek V3-0324 model, which is an auto-regressive language model that uses an optimized transformer architecture. For more information, please check here. The NVIDIA DeepSeek V3-0324 FP4 model is quantized with TensorRT Model Optimizer. This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party’s requirements for this application and use case; see link to Non-NVIDIA (DeepSeek V3-0324) Model Card. Developers looking to take off the shelf pre-quantized models for deployment in AI Agent systems, chatbots, RAG systems, and other AI-powered applications.