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AI 모델 2119

• Qwen3.8-Flash-Next-abliterated-GSQ-RCO-Coder-Strata-GGUF↗

  alesha-pro

  The model weights in this repo are not mine. They are the GSQ-RCO Coder release of Qwen3.8-Flash-Next made by the Deep Algorithms and Systems Lab at ISTA: 256 of the 512 experts in each layer are kept, the rest are removed, and the kept weights are stored at 3.5 bits. The files are copied byte for byte from ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-Coder-GGUF at revision `5348543e0147355ac9cbcb031184a3546350988e`. The SHA256 of both shards and of the vision projector match their files. See `SHA256SUMS`. If you use these weights, cite the two ISTA-DASLab papers. The BibTeX is at the bottom of this page. My part is the check that the refusal vector I published for the unpruned sizes also works on the pruned model, and the measurements below. The vector is the same 480 KB file as in the repo with the four unpruned sizes, with the same SHA256. For the original card and ISTA's own SWE-bench and LiveCodeBench numbers, go to the ISTA-DASLab repo.

• wav2vec2-large-xlsr-53-estonian↗

  anton-l

  Fine-tuned facebook/wav2vec2-large-xlsr-53 on Estonian using the Common Voice dataset. When using this model, make sure that your speech input is sampled at 16kHz.

• laya-typed-decisions↗

  convaiinnovations

  Non-autoregressive **System 1 decision model**, fine-tuned on the typed-decisions workflows: agent-trace observability, customer service, invoice processing and security incidents. **+3.9 points over Jev's published 0.727, above the 0.735 teacher ceiling**, with 2.4x better Brier and 1.6x better score MAE. Jev figures are third-party published, not measured here — there is no TypeSafe API access in this project, and sample sizes and prompts differ. Treat the comparison as indicative.

• regardv3↗

  sasha

  This model is the result of a project entitled Towards Controllable Biases in Language Generation. It consists of a BERT classifier (no ensemble) trained on 1.7K samples of biased language. *Regard* measures language polarity towards and social perceptions of a demographic (compared to sentiment, which only measures overall language polarity).

• qwen3.8-27b-abliterated-3.69bpw-12GB-MTP.gguf↗

  soyaakinohara

  A **Ridge-style mixed-quantized GGUF** of the BF16 AEON-7/Qwen3.8-27B-AEON-ULTIMATE-UNCENSORED-BF16 model. This model keeps the AEON-7 uncensored / refusal-removed weights, while using a Gated-DeltaNet-aware mixed quantization layout inspired by empero-ai/Qwen3.8-27B-Ridge-GGUF. The goal is to retain as much quality as possible in the sensitive Gated-DeltaNet path while obtaining a small, fast GGUF suitable for local llama.cpp inference. > **Important:** This is not an official Empero Ridge release and is not the > same set of weights. It is an independent quantization of AEON-7 using a > Ridge-inspired tensor-type map and an AEON-specific importance matrix.

• Qwen2.5-VL-7B-Instruct-GGUF↗

  DhruvalLabs

  > [!IMPORTANT] > **This VLM requires TWO files** — a text backbone GGUF and the `mmproj` vision encoder GGUF. > Download one text backbone (e.g. Q4_K_M) **and** the mmproj file. Both must be in the same folder. 1. Search for `Dhptl/Qwen2.5-VL-7B-Instruct` in LM Studio 2. Download the Q4_K_M text file **and** the mmproj file 3. Load the model — LM Studio automatically uses both files > **Why is mmproj always F16?** > The vision encoder maps image pixels to token embeddings. Quantizing it causes > visible visual artifacts and degraded image understanding. It stays at F16 (half precision) > which is already very efficient at ~1-2GB for most models.

• Nous-Hermes-Llama2-llamafile↗

  jartine

  WARNING: This README may contain inaccuracies. It was generated automatically by forking TheBloke/Nous-Hermes-Llama2-GGUF and piping the README through sed. Errors should be reported to jartine, and do not reflect TheBloke. You can support his work on Patreon. llamafile is a new format introduced by Mozilla Ocho on Nov 20th 2023. It uses Cosmopolitan Libc to turn LLM weights into runnable llama.cpp binaries that run on the stock installs of six OSes for both ARM64 and AMD64. llamafile offers numerous advantages over GGML, such as better tokenisation, and support for special tokens. It is also supports metadata, and is designed to be extensible. The creator of the source model has listed its license as `['mit']`, and this quantization has therefore used that same license.

• Qwen3.6-35B-A3B-GGUF↗

  jimbothigpen

  A complete set of importance-matrix (imatrix) GGUF quantizations of **Qwen/Qwen3.6-35B-A3B** — a 35B-parameter Mixture-of-Experts model (~3B active per token) — spanning the full ladder from `IQ1_*` up to `Q8_0`, plus the `BF16` baseline. This set exists as a **canonical reference** for cross-quant / cross-backend quality (perplexity) and throughput comparison. Every quant in this repository was produced from the same `BF16` source and the **same importance matrix** (`Qwen3.6-35B-A3B-imatrix.gguf`, included here for full reproducibility). > **Generated with the jimbothigpen/llama.cpp fork.** > These GGUFs and their imatrix are produced by that fork's quantization toolchain. It is a fork of > upstream ggml-org/llama.cpp that integrates features ported > from several llama.cpp forks — including the IQ-K / IQ-KS / IQ-KT (trellis) quant families from > ik_llama.cpp — plus ROCm and Vulkan backend support.

• Ornith-1.5-27B-A3B-Coder-MTP-GGUF↗

  ManniX-ITA

  GGUF quantizations of **`ManniX-ITA/Ornith-1.5-27B-A3B-Coder`** — the pure competence-map 184-expert cut of `Ornith-1.5-35B-A3B`. **→ Recipe, drop maps, the full 11-bench evaluation, the cross-family comparison and the length/deliberation analysis all live on the weights card.** This card covers the quantized files only. > Its sibling **CoderX** > uses the same map with a REAP stability floor and measures better on almost every code and > reasoning axis at the same expert budget and the same file size. Start there unless you > specifically want this arm.

• shap-e-img2img↗

  OpenAI

  Shap-E introduces a diffusion process that can generate a 3D image from a text prompt. It was introduced in Shap-E: Generating Conditional 3D Implicit Functions by Heewoo Jun and Alex Nichol from OpenAI. *We present Shap-E, a conditional generative model for 3D assets. Unlike recent work on 3D generative models which produce a single output representation, Shap-E directly generates the parameters of implicit functions that can be rendered as both textured meshes and neural radiance fields. We train Shap-E in two stages: first, we train an encoder that deterministically maps 3D assets into the parameters of an implicit function; second, we train a conditional diffusion model on outputs of the encoder. When trained on a large dataset of paired 3D and text data, our resulting models are capable of generating complex and diverse 3D assets in a matter of seconds. When compared to Point-E, an explicit generative model over point clouds, Shap-E converges faster and reaches comparable or better sample quality despite modeling a higher-dimensional, multi-representation output space. We release model weights, inference code, and samples at this https URL.*