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AI モデル 2119

• superprompt-v1↗

  roborovski

  A T5 model fine-tuned on the SuperPrompt dataset for upsampling text prompts to more detailed descriptions. Meant to be used as a pre-generation step for text-to-image models that benefit from more detailed prompts. It's important that you use the exact task prefix: `Expand the following prompt to add more detail:` and limit the max tokens to 77 - which is the max prompt length for Stable Diffusion models.

• MOSS-Audio-4B-Instruct-GGUF↗

  cstr

  GGUF / ggml conversions of `OpenMOSS-Team/MOSS-Audio-4B-Instruct` for use with `crispasr --backend moss-audio` from **CrispStrobe/CrispASR**. **JFK sample (samples/jfk.wav, 11s):** > And so, my fellow Americans, ask not what your country can do for you, ask what you can do for your country. 1. **Inspect** the HF safetensors: 3 shards, 901 tensors total -- audio encoder (conv stem + 32 transformer layers + layer_norm), audio adapter (1 GatedMLP), deepstack mergers (3 GatedMLPs), language model (embedding + 36 Qwen3 layers + final norm + lm_head).

• stable-diffusion-xl-1.0-inpainting-0.1↗

  diffusers

  --- license: openrail++ base_model: stabilityai/stable-diffusion-xl-base-1.0 tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - inpainting inference: false --- SD-XL Inpainting 0.1 is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input, with the extra capability of inpainting the pictures by using a mask. The SD-XL Inpainting 0.1 was initialized with the `stable-diffusion-xl-base-1.0` weights. The model is trained for 40k steps at resolution 1024x1024 and 5% dropping of the text-conditioning to improve classifier-free classifier-free guidance sampling. For inpainting, the UNet has 5 additional input channels (4 for the encoded masked-image and 1 for the mask itself) whose weights were zero-initialized after restoring the non-inpainting checkpoint. During training, we generate synthetic masks and, in 25% mask everything.

• MOSS-TTS-Local-Transformer-v1.5-GGUF↗

  mudler

  GGUF conversion of `OpenMOSS-Team/MOSS-TTS-Local-Transformer-v1.5` for moss-tts.cpp: a C++17/ggml inference port of the OpenMOSS MOSS-TTS family that runs entirely on stock ggml with no Python, ONNX or torch at inference time. The non-matmul tensors (all `lc.*` heads and embeddings, every norm and bias) are kept in f32 in every quant; only the Qwen3 global and GPT-J local attention/FFN matmuls are quantized, so the CPU gather/head paths stay exact. This port is numerically verified against the reference PyTorch implementation: the language model matches to about 1e-5 with an exact greedy code sequence, and the MOSS-Audio-Tokenizer-v2 decode matches at about 115 dB SNR (bit-exact in f32). On CPU it runs roughly twice as fast per frame as the reference PyTorch at the same f32 precision.

• Llama-3.2-3B-small_Shiro_roleplay-gguf↗

  samunder12

• GLM-5.2-GGUF↗

  unsloth

  You can now run GLM-5.2 in Unsloth Studio with toggles for High and Max thinking. Read our GLM-5.2 guide for analysis and instructions. See below for example of 1-bit UD-IQ1_M GGUF running in Unsloth: We're introducing GLM-5.2, our latest flagship model for long-horizon tasks. It marks a substantial leap in long-horizon task capability over its predecessor GLM-5.1 and, for the first time, delivers that capability on a **solid 1M-token context**. GLM-5.2's new capabilities include: - **Solid 1M Context:** A solid 1M-token context that stably sustains long-horizon work - **Advanced Coding with Flexible Effort**: Stronger coding capabilities with multiple thinking effort levels to balance performance and latency - **Improved Architecture**: We propose IndexShare, which reuses the same indexer across every four sparse attention layers, reducing per-token FLOPs by 2.9× at a 1M context length. We also improve GLM-5.2’s MTP layer for speculative decoding, increasing the acceptance length by up to 20% - **Pure Open**: An MIT open-source license — no regional limits, technical access without borders

• Qwen3.6-27B-AWQ-4bit↗

  Alennnndy

  This repository contains an optimized, 4-bit quantized version of the original Qwen3.6-27B model by Alibaba Cloud. The quantization was performed using the **AWQ** algorithm via the `llmcompressor` library, drastically reducing the VRAM required for local inference without a perceptible degradation in the original model's accuracy. * **Base Model:** `Qwen/Qwen3.6-27B` * **Algorithm:** AWQ * **Scheme:** W4A16_ASYM (4-bit asymmetric weights, 16-bit activations) * **Ignored Modules:** `lm_head`, `visual`, `vision` (Vision encoder layers are kept in their original precision to maintain multimodal capabilities). * **Weights Format:** Safetensors * **Calibration Dataset:** `HuggingFaceH4/ultrachat_200k` (512 random samples from the `train_sft` split, formatted with the model's chat template. Max sequence length: 1024).

• FLUX-3-Image-Klein-pup-Uncensored-GGUF↗

  AtomicChat

  BFL's prompt upsampler for FLUX 3 Image Klein (a 9B Qwen3.5 fine-tune) with its refusal direction projected out, as GGUF for llama.cpp, plus the same edit as a LoRA for the stock files. Klein's text encoder without refusals is in FLUX-3-Image-Klein-TE-Uncensored-GGUF . The measurements are public. Built by the team behind Atomic Chat , the app that runs open-weight models locally and offline. As a chat model, refusals fall from 63% to 0% on held-out English prompts. MMLU goes from 74.15% to 73.95%. As Klein's upsampler, under BFL's t2i system prompt, the stock model refuses none of our requests but softens 2 of 35 sensitive ones in its captions; this one keeps them. The image model draws neither of the two in any build , so on Klein's pictures we measured no uncensoring effect; the open pipeline already draws the sensitive categories ( the per-category table ). Against the other uncensored upsamplers on our stand, prithiv's measures the same as this one, and Sakura's still refuses 22% in English. The same rungs and layouts as our stock upsampler GGUFs, with the edit baked in. KLD and top-1 are against the original BF16 on a 30-language neutral text set (87 chunks of 4096 tokens), so they include the edit itself. Refusals are on held-out prompts (81 English harmful requests; 250 XSTest safe prompts), plain chat, thinking off.

• Qwen3.8-27B-Abliterated-GSQ-Orca-GGUF↗

  ayozhee

  > **Update — September 27, 2026: IQ3_S-MTP is now available!** > > Added Qwen3.8-27B-Abliterated-GSQ-Orca-IQ3_S-MTP.gguf — **12.12 GB / 11.29 GiB**, built with the same selective GSQ–Orca method. Feedback and issue reports are welcome! > > **The detailed results below refer to the original IQ2_S release.** A compact, experimental GGUF with MTP included, built for local inference on smaller GPUs. It combines GSQ's quantized weights with selected abliterated weights from OrcaRouter and was developed and tested on an **RTX 3060 12 GB**. This is my first open-source model release. My goal is to share something useful for local use, with a straightforward account of how it was built and tested. Abliteration—the weight modification used to reduce refusal behavior—comes from OrcaRouter.

• bge-m3↗

  BAAI

  本プロジェクトでは、多機能性・多言語対応・多粒度という汎用性で際立つBGE-M3を紹介します。 - 多機能性：エンベディングモデルの一般的な3つの検索機能である密度ベース検索、マルチベクトル検索、スパース検索を同時に実行できます。 - 多言語対応：100以上の言語に対応可能です。 - 多粒度：短い文から最大8192トークンの長文ドキュメントまで、さまざまな粒度の入力に対応できます。 以下のパイプラインの使用を推奨します：ハイブリッド検索＋再ランキング。 - ハイブリッド検索は各種手法の強みを活かし、より高い精度と強力な汎化能力を提供します。代表的な例として、エンベディング検索とBM25アルゴリズムを併用する方法があります。 現在、エンベディング検索とスパース検索の両方に対応したBGE-M3をお試しいただけます。これにより、密度ベースのエンベディングを生成する際に追加コストなしでトークン重み（BM25に類似）を取得できます。ハイブリッド検索を利用するには、VespaやMilvusをご参照ください。 以前のテスト結果が低かったのは、クエリと同じIDを持つパッセージを誤って検索結果から除外していたためです。この誤りを修正後、MIRACLにおけるBGE-M3の全体的な性能は従来の結果を上回りましたが、実験の結論自体は変わりません。その他の結果にはこの誤りの影響はありません。 以前の低い結果を再現するには、`pyserini.search.faiss`または`pyserini.search.lucene`を使用してパッセージを検索する際に`--remove-query`パラメータを追加する必要があります。