LTX-2.3-DISTILLED
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View details ↗DISCOVER & COMPARE
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View details ↗LTX-2.5-Distilled-GGUF
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View details ↗MiniMax-H3_GGUFs
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View details ↗Minimax-H3-WAN2GP
FenomAIYou will find here all the MiniMax H3 image models used with WanGP (https://github.com/deepbeepmeep/Wan2GP) : WanGP supports the Wan (and derived models), Hunyuan Video, Flux 1 & 2, Qwen, Z-Image and LTX-2, LTX Video models with: Low VRAM requirements (as low as 6 GB of VRAM is sufficient for certain models) Support for old GPUs (RTX 10XX, 20xx, ...) Very Fast on the latest GPUs Easy to use Full Web based interface Auto download of the required model adapted to your specific architecture Tools integrated to facilitate Video Generation : Mask Editor, Prompt Enhancer, Temporal and Spatial Generation Loras Support to customize each model Queuing system : make your shopping list of videos to generate and come back later Discord Server to get Help from Other Users and show your Best Videos: https://discord.gg/g7efUW9jGV
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View details ↗Ornith-1.5-35B-A3B-NVFP4
FenomAIChirp Chirp! 🐦 We are introducing Ornith-1.5, a major step toward building foundation models through end-to-end self-improvement. Ornith-1.5 extends Ornith-1.0 (which was developed on top of Qwen3.5 and Gemma4 with additional continued pretraining, mid-training, and post-training) by expanding the self-improvement loop from scaffold and rollout optimization to jointly optimizing task generation, scaffold construction, and solution rollouts. Rather than relying on a fixed set of human-curated tasks and manually designed harnesses, Ornith-1.5 continuously generates new training tasks, discovers effective strategies for solving them, and improves the policy through reinforcement learning. For more details on the task, harness, and rollout reward design, please refer to our blog. This model card documents **Ornith-1.5-35B-A3B**, the mid-size mixture-of-experts member of the Ornith-1.5 family. It activates only ~3B parameters per token, yet significantly outperforms its similar-sized peer Qwen 3.6-35B across all coding and agentic benchmarks, and outperforms dense models such as Gemma 4-31B and Muse Glimmer-30B by wide margins on agentic coding.
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View details ↗Qwen3.6-27B-AWQ-INT4
FenomAI> [!Note] > This repository contains model weights and configuration files for the post-trained model in the Hugging Face Transformers format. > > These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, KTransformers, etc. Following the February release of the Qwen3.5 series, we're pleased to share the first open-weight variant of Qwen3.6. Built on direct feedback from the community, Qwen3.6 prioritizes stability and real-world utility, offering developers a more intuitive, responsive, and genuinely productive coding experience. - **Agentic Coding:** the model now handles frontend workflows and repository-level reasoning with greater fluency and precision. - **Thinking Preservation:** we've introduced a new option to retain reasoning context from historical messages, streamlining iterative development and reducing overhead.
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View details ↗Qwen3.6-35B-A3B-AWQ-4bit
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View details ↗Qwen3.8-27B-GGUF
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