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AI モデル 2119

• Dr_Samantha-7B-GGUF↗

  TheBloke

• deepseek-vl-7b-chat↗

  DeepSeek

  Introducing DeepSeek-VL, an open-source Vision-Language (VL) Model designed for real-world vision and language understanding applications. DeepSeek-VL possesses general multimodal understanding capabilities, capable of processing logical diagrams, web pages, formula recognition, scientific literature, natural images, and embodied intelligence in complex scenarios. Haoyu Lu*, Wen Liu*, Bo Zhang**, Bingxuan Wang, Kai Dong, Bo Liu, Jingxiang Sun, Tongzheng Ren, Zhuoshu Li, Hao Yang, Yaofeng Sun, Chengqi Deng, Hanwei Xu, Zhenda Xie, Chong Ruan (*Equal Contribution, **Project Lead) DeepSeek-VL-7b-base uses the SigLIP-L and SAM-B as the hybrid vision encoder supporting 1024 x 1024 image input and is constructed based on the DeepSeek-LLM-7b-base which is trained on an approximate corpus of 2T text tokens. The whole DeepSeek-VL-7b-base model is finally trained around 400B vision-language tokens. DeekSeel-VL-7b-chat is an instructed version based on DeepSeek-VL-7b-base.

• Winnow-12B↗

  EldanRing

  Winnow-12B is an EldanRing fine-tune of Gemma 4 12B IT for local typed decisions, chat, and image input. Give it a shared state and questions with known answer options; the Winnow server scores those options through `/v1/systemone` without generating an explanation. Ordinary chat uses `/v1/chat/completions` from the same loaded model. The GGUF downloads contain the merged fine-tune. No separate adapter or base-model download is needed. Winnow Q8 and Jev each answered **198 of 231** JevBench questions correctly on these frozen inputs. JevBench reports public-subset accuracy rather than the composite leaderboard score. These panels were used during development. The BF16 results use an earlier GGUF export; see export provenance and the evaluation report.

• gemma-4-12B↗

  Google

  Hugging Face | GitHub | Launch Blog | Documentation | Technical Report > [!Note] > This model card is for the Gemma 4 12B Unified model, which is part of the Gemma 4 family of open models. Built with the same multimodal functionality as Gemma 4 E2B and E4B (text, audio, image, and video inputs), it brings native audio and vision understanding directly to local environments without the need for separate encoders. This unified approach to multimodality makes the model encoder-free, offering a deployment size that is perfect for consumer devices and streamlined local execution. Gemma is a family of open models built by Google DeepMind. Gemma 4 models are multimodal, handling text and image input (with audio supported on E2B, E4B, and 12B) and generating text output. This release includes open-weights models in both pre-trained and instruction-tuned variants. Gemma 4 features a context window of up to 256K tokens and maintains multilingual support in over 140 languages.

• Qwen3.8-27B-Coder390-EfficientThink-Opus5.5-GPT6Astra-Grok4.7-DSV4Pro-K3-SFT-RLOO-GGUF-NInfer↗

  nerkyor

  A model post-trained with several rounds of SFT and RLOO on top of Qwen3.8-27B-EfficientThink-SFT-SimPO-DFlash2, which is itself the official Qwen3.8-27B post-trained with SFT and SimPO. It is built to fix the original model's habit of failing to stop: it already has an answer, yet keeps re-deriving until it hits the 94K cap with no final answer. This repository is the standalone **GGUF** repository with eight GGUF packages under `GGUF/`: **Q2 / Q3 / Q4 LynnStyle** (built-in **Q4_0** MTP head), the same three as **-Q8MTP** versions (built-in **Q8_0** MTP head), **Q6_K**, and **Q8_0** (each with a built-in MTP head), plus the matching NInfer packages under `GGUF-NInfer/` (vision built in). The raw BF16 vision-tower file (`GGUF-NInfer/vision-bf16.safetensors`) is included for your own packaging/conversion; V3 `.ninfer` packages already have vision built in and do not need it at runtime. For llama.cpp multimodal, use `--mmproj GGUF/mmproj-Qwen3.8-27B-Q8_0.gguf` (Q8_0 mmproj in `GGUF/` only; not required inside NInfer packages). The BF16, static FP8, NVFP4, NInfer W4A4, INT8 W8A8, INT4 W4A16, and other tiers are in the main repository Qwen3.8-27B-Coder390-EfficientThink-Opus5.5-GPT6Astra-Grok4.7-DSV4Pro-K3-SFT-RLOO-MTP-DFlash2. - **Baseline**: the original Qwen3.8 27B (FP8) scores GPQA 177/198, MMLU 444/500, LCB 83/100; its GPQA reasoning length (P50 / P70 / P90 tokens) is 5,299 / 12,607 / 50,577. - **GGUF Q2 LynnStyle · scores**: GPQA 177/198, MMLU 433/500, LCB 90/100 (llama.cpp, C4, 100K full sets; scored before the built-in MTP head was attached (external Q8 MTP draft); same quantized weights). - **GGUF Q2 LynnStyle · reasoning length (P50 / P70 / P90 tokens)**: GPQA 2,989.5 / 8,923.8 / 27,156.1; MMLU 181 / 326.3 / 1,020.5; LCB 5,656.5 / 17,835.8 / 40,067.3. - **GGUF Q2 LynnStyle · speed**: NInfer with built-in Q4 MTP, C8 aggregate 521.0 tok/s (512-token cap, short run); llama.cpp with built-in Q4 MTP, C4 aggregate 174.8 tok/s (4096-token cap, long run). Protocols differ; do not compare directly. - **GGUF Q2 LynnStyle · size**: GGUF 13,276,009,792 bytes (BPW 3.89), NInfer 13,568,617,472 bytes with vision (text + MTP BPW 3.89). - **GGUF Q3 LynnStyle-16G · what it is**: our lowest Q3 tier, recommended for 16 GB VRAM (`GGUF/Qwen3.8-27B-Coder390-Q3LynnStyle16G-MTP.gguf`). It changes the earlier Q2 scheme: the lowest type is now IQ3_XXS (no Q6 / Q8 in the body), b

• Nous-Hermes-13B-Code-GGUF↗

  TheBloke

  GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. GGUF 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 `cc-by-nc-4.0`, and this quantization has therefore used that same license. As this model is based on Llama 2, it is also subject to the Meta Llama 2 license terms, and the license files for that are additionally included. It should therefore be considered as being claimed to be licensed under both licenses. I contacted Hugging Face for clarification on dual licensing but they do not yet have an official position. Should this change, or should Meta provide any feedback on this situation, I will update this section accordingly.

• sam2_hiera_large.fb_r1024_2pt1↗

  timm

• Kromoss↗

  lodestones

  Kromoss is a full fine-tune of MOSS-Image-Base at 1024 px. It uses the same architecture, inference pipeline, text encoder (Qwen3.5-9B) and VAE (FLUX.2 small decoder) as MOSS-Image-SFT, and this repo has the same layout, so it is a drop-in replacement. The repo also ships two 8-step turbo LoRAs distilled from MOSS-Image-Base: **8 steps, CFG 1**, no negative branch. - `8-steps-dmd2-lora.safetensors` (recommended): the DMD LoRA below, further trained with DMD2's GAN stage against real images. Slightly more contrast and detail, and the best PartiPrompts score. - `8-steps-dmd-lora.safetensors`: data-space DMD distillation. The transformer and text encoder use bfloat16; the VAE and initial noise use float32.

• Qwen3.8-27B-Uncensored-Aggressive↗

  0xKitkat

  **If you use this model, follow @procrastiness on X/Twitter.** That’s where drop notes, quant updates, and the next bake land first. One follow is the whole ask. Same idea as HauhauCS Aggressive on Qwen3.6: **keep the original model’s skills, strip the refusal / preamble habit.** No extra fine-tune. No dataset swap. This is a weight edit plus a baked chat template, not a jailbreak prompt you have to remember. > Safety alignment has been removed. The model will comply with requests the base checkpoint would refuse. Use it only in ways that are legal where you are. The publisher does not condone criminal use.

• pythia-1.4b-deduped↗

  EleutherAI

  The *Pythia Scaling Suite* is a collection of models developed to facilitate interpretability research (see paper). It contains two sets of eight models of sizes 70M, 160M, 410M, 1B, 1.4B, 2.8B, 6.9B, and 12B. For each size, there are two models: one trained on the Pile, and one trained on the Pile after the dataset has been globally deduplicated. All 8 model sizes are trained on the exact same data, in the exact same order. We also provide 154 intermediate checkpoints per model, hosted on Hugging Face as branches. The Pythia model suite was designed to promote scientific research on large language models, especially interpretability research. Despite not centering downstream performance as a design goal, we find the models match or exceed the performance of similar and same-sized models, such as those in the OPT and GPT-Neo suites. Previously, we released an early version of the Pythia suite to the public. However, we decided to retrain the model suite to address a few hyperparameter discrepancies. This model card lists the changes ; see appendix B in the Pythia paper for further discussion. We found no difference in benchmark performance between the two Pythia versions. The old models are still available, but we suggest the retrained suite if you are just starting to use Pythia. **This is the current release.**