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AI 모델 2119
- MiniMax-H3_Ref2VA-LoRA-CrossView-Warp_v1↗
Cseti
Give it a video and a camera offset — azimuth, elevation — and it generates the same scene from that new viewpoint. This is the CrossView-Warp idea ported to **MiniMax-H3**. The model reads two videos: a depth-warp of your clip, which carries the geometry, and the clip itself, which carries identity and appearance. The warp comes from the **CrossViewWarp ComfyUI node**. **Trigger word:** crossview (note: You can also influence the generation by prompting what you want to see in the unseen (magenta) areas.)
- Wan2.1-T2V-1.3B-GGUF↗
samuelchristlie
Wan2.1 is an open-source suite of video foundation models, compatible with consumer-grade GPUs, that excels in various video generation tasks like text-to-video, image-to-video, and video editing, even supporting visual text generation. This model is a derivative work of the original model licensed under the Apache 2.0 License, and is therefore distributed under the terms of the same license. Thanks to Patrick Gillespie for creating the ASCII text art tool used in this project https://patorjk.com/software/taag/
- Nanbeige4.3-33B-NVFP4↗
hsam007
- MiniCPM5-1B-MLX↗
openbmb
MiniCPM Tech Report | MiniCPM Wiki(Chinese) | GitHub Repo | UltraData | MiniCPM Desk Pet | Online Demo We are releasing **MiniCPM5-1B**, the first model in the **MiniCPM5** series. It is a dense 1B Transformer built for on-device, local deployment, and resource-constrained scenarios, reaching 1B-class open-source SOTA. 🏆 **1B-class open-source SOTA**: compared with strong open-source models in the same size class, MiniCPM5-1B reaches SOTA within this comparison set. Its advantage is most visible in agentic tool use, code generation, and difficult reasoning.
- Qwen-Image-2512-GGUF↗
byteshape
This is a GGUF-quantized version of the **Qwen-Image-2512** diffusion transformer, produced with **ByteShape's ShapeLearn**, which learns the optimal datatype per tensor to maintain high quality even at very low bitlengths. Six sizes are available, from **16.87 GB (6.61 bpw)** down to **7.77 GB (3.04 bpw)**. All variants generate high-quality images. > Looking for more speed? We also publish a **vLLM-Omni** build of the same model with optimized Humming kernels, roughly **2-3x faster** per step. See byteshape/Qwen-Image-2512-Humming.
- Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-APEX-MTP-GGUF↗
mudler
⚡ Each donation = another big MoE quantized I host 30+ free APEX MoE quantizations as independent research. My only local hardware is an NVIDIA DGX Spark (122 GB unified memory), enough for ~30-50B-class MoEs, but bigger ones (200B+) require rented compute on H100/H200/Blackwell, typically $20-100 per quant. If APEX quants are useful to you, your support directly funds those bigger runs. **APEX (Adaptive Precision for EXpert Models)** quantizations of hesamation/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled, with the **MTP (multi-token prediction) head bundled** for in-the-box self-speculative decoding. These GGUFs bundle the model's **MTP (multi-token prediction) head** alongside the trunk in a single file, courtesy of llama.cpp PR #22673. With a recent llama.cpp (>= commit 255582687) you can enable self-speculative decoding using just this one file — no separate draft model needed:
- 72B-Qwen2.5-Kunou-v1↗
Sao10K
I do not really have anything planned for this model other than it being a generalist, and Roleplay Model? It was just something made and planned in minutes. Same with the 14 and 32B version. Kunou's the name of an OC I worked on for a couple of years, for a... fanfic. mmm... A kind-of successor to L3-70B-Euryale-v2.2 in all but name? I'm keeping Stheno/Euryale lineage to Llama series for now. I had a version made on top of Nemotron, a supposed Euryale 2.4 but that flopped hard, it was not my cup of tea. This version is basically a better, more cleaned up Dataset used on Euryale and Stheno. Recommended Model Settings | *Look, I just use these, they work fine enough. I don't even know how DRY or other meme samplers work. Your system prompt matters more anyway.*
- Muse-Glimmer-30B-Abliterated-GGUF↗
The-freezer
> [!IMPORTANT] > ## Improvement update — August 15, 2026 > > This release now includes compact **abliterated Q4_K_M companions**: a **1.63 GB DFlash drafter** and a **1.40 GB multimodal projector**, matching Meta's consumer-hardware footprint while preserving this model's modified weights. The complete text quant ladder has also been refreshed with Meta's post-release Jinja correction, which normalizes `Reasoning effort` to `Reasoning strength` and prevents duplicate reasoning directives. Text generation, image input, and DFlash speculative decoding were validated together on current llama.cpp. > ## ✅ All quants live > > The full text quant ladder (`Q2_K` → `Q8_0`), compact Q4_K_M and full-precision vision projectors, and compact Q4_K_M and full-precision DFlash drafters are uploaded — see the **Files** tab. > ## ⚠️ EXPERIMENTAL > > Same-day arch, quantized. Expect sharp edges — decode, coherence, tool-parse, serve edge cases under load. Please open a Community discussion with loader/version, quant, prompt, sampling, and failure mode. Real repros get fixed faster.
- natural-sql-7b↗
chatdb
Big thanks to the defog team for open sourcing sql-eval 👏 Natural-SQL also can handle complex, compound questions that other models typically struggle with. There is a more detailed writeup Here is a write up, small test done here. The model weights are licensed under `CC BY-SA 4.0`, with extra guidelines for responsible use expanded from the original model's Deepseek license. You're free to use and adapt the model, even commercially. If you alter the weights, such as through fine-tuning, you must publicly share your changes under the same `CC BY-SA 4.0` license.
- Unlimited-OCR-GGUF↗
sabafallah
Unlimited-OCR is DeepSeek-OCR v1's DeepEncoder + a DeepSeek-V2 MoE decoder, with the decoder attention replaced by R-SWA (Reference Sliding Window Attention). llama.cpp support comes in two PRs (the same GGUF files work for both): The max_tiles fix #25614 is merged too. The mmproj files were re-uploaded on 2026-07-13 and carry `preproc_max_tiles = 32`, matching the reference implementation. Unlimited-OCR shares DeepSeek-OCR v1's projector type, so a build without #25614 ignores the key and falls back to v1's cap of 9 - tall or dense pages are then split into a coarser tile grid than the reference. Older mmproj files keep working either way. The DRY flags are a weak loop guard mirroring the reference implementation's `no_repeat_ngram_size=35` / `ngram_window=128`. Keep the allowed length high: aggressive DRY settings garble the model's HTML table output.