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AI モデル 84753

• LoopedLM-P2-huginn-m-orthoinj↗

  IFM

• Qwen3-30B-A3B-12L-W4A16-test↗

  INCModel

• Index-Translate-9B↗

  IndexTeam

• NVIDIA-Nemotron-3-Super-120B-A12B-MLX-Q4↗

  inferencerlabs

• Qwen3-Coder-30B-A3B-Instruct-gguf-q4km-AutoRound↗

  Intel

• Atria-Dawn-Preview↗

  internlm

• EuroLLM-22B-Instruct-2512-FP8-Dynamic↗

  IntraFind

• Qwen3.5-9B-Claude-4.6-Opus-Reasoning-Distilled-GGUF↗

  Jackrong

  🔥 **Update (April 5):** I’ve released the complete training notebook, codebase, and a comprehensive PDF guide to help beginners and enthusiasts understand and reproduce this model's fine-tuning process. > ❤️ Special thanks to the **Unsloth** open-source library and @KyleHessling1 for their support. 👉 **GitHub Repository: Jackrong-llm-finetuning-guide** Visit the repo to dive into the codebase and reproduce the results locally or on Colab.

• yolov10n↗

  jameslahm

• 10Eros-Max-beta6-W4A8-ConvRot↗

  JoaoZaokk

  *The showcase above was rendered entirely at home on two RTX 3090s with the files in this repo: OpenAI's Sora launch "Tokyo walk" prompt, then two made-up characters, picture and sound from the model, about 6 minutes per 5-second 1280×736 clip. All characters are fictional.* Quantizations of TenStrip/10Eros-Max `10Eros_Max_h3_8stepTURBO_beta6_pruned.safetensors` (MiniMax H3, 50 blocks + 2 token-refiner blocks), plus the MiniMax H3 text encoder (Qwen3-VL 32B) in a form that runs natively on Ampere. The diffusion files go in `models/diffusion_models`, the text encoder in `models/text_encoders`. The VAEs are the official ones from Comfy-Org/MiniMax-H3: `minimax_h3_video_vae_fp16` and `minimax_h3_audio_vae_fp32`. Every tensor outside the quantized Linears is the source's, byte for byte.