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AI Models 84757

  • DeepSeek-V4.1-Flash-1017-Volta↗

    lxe

  • Qwen3.6-27B-Omnimerge-v4-GGUF↗

    ManniX-ITA

    GGUF quantizations of `ManniX-ITA/Qwen3.6-27B-Omnimerge-v4` — the **MLP-passthrough** variant that defends against the Qwen3.6 think-policy fragility we discovered. Source dtype is BF16; this repo provides the standard bartowski quant ladder (F16 → IQ2_XXS) for `llama.cpp`. > **Source model:** `ManniX-ITA/Qwen3.6-27B-Omnimerge-v4` (BF16 weights, model card with full benchmarks and methodology). > **NOT** a quant of clean Qwen/Qwen3.6-27B — these GGUFs contain the v4 merge. > > **MTP companion (2× decode speedup):** weight-identical GGUFs with the MTP head retained for `llama.cpp --spec-type draft-mtp` self-speculative decoding are at `ManniX-ITA/Qwen3.6-27B-Omnimerge-v4-MTP-GGUF`. Quality is statistically indistinguishable from this repo (HE 137/164 ↔ 137/164, GPQA 155/198 ↔ 154/198); aggregate decode is 2.0-2.3 × faster on a single 24 GB GPU. Use that repo for interactive / single-request workloads where latency matters. All quants made using imatrix with calibration data v5, the same calibration set bartowski uses for the Qwen3.6 base release — so quality fingerprints are directly comparable to bartowski's `Qwen_Qwen3.6-27B-GGUF` repo.

  • Qwen_Qwen3.5-4B-Q4_K_M.gguf↗

    Manojb

  • T3qm7Neuralsynthesis-7B-GGUF↗

    MaziyarPanahi

  • LongCat-Image-Dev↗

    meituan-longcat

  • armenian-text-embeddings-1↗

    Metric-AI

  • swin-large-patch4-window12-384-in22k↗

    Microsoft

  • Devstral 2↗

    Mistral AI

  • Phi-3-mini-4k-instruct-llamafile↗

    mozilla-ai

    This repository contains executable weights (which we call llamafiles) that run on Linux, MacOS, Windows, FreeBSD, OpenBSD, and NetBSD for AMD64 and ARM64. Assuming your system has at least 32GB of RAM, you can try running the following command which download, concatenate, and execute the model. Alternatively, you may download an official `llamafile` executable from Mozilla Ocho on GitHub, in which case you can use the Mixtral llamafiles as a simple weights data file.

  • AesCode-32B-i1-GGUF↗

    mradermacher

    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) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9