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AI 모델 85003

• Viper-Coder-v1.6-r999-i1-GGUF↗

  mradermacher

• detailer-tool-concept-lora-illustriousxl↗

  Muapi

• QwenEdit_2511_girls_nsfw_v2.safetensors↗

  nnndite

• gpt4all-full-decay-epoch_1↗

  nomic-ai

• GCViT↗

  NVIDIA

• GR00T-H↗

  NVIDIA

• Mistral-Medium-3.5-128B-NVFP4↗

  NVIDIA

  Mistral Medium 3.5 128B is Mistral AI's first flagship merged model. It is a dense 128B model with a 256k context window, handling instruction-following, reasoning, and coding in a single set of weights. Mistral Medium 3.5 replaces its predecessor Mistral Medium 3.1 and Magistral in Le Chat. It also replaces Devstral 2 in our coding agent Vibe. Concretely, expect better performance for instruct, reasoning and coding tasks in a new unified model in comparison with our previous released models. The NVIDIA Mistral-Medium-3.5-128B NVFP4 model is quantized with Model Optimizer. This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party's requirements for this application and use case; see link to Non-NVIDIA Mistral-Medium-3.5-128B Model Card **GOVERNING TERMS:** **GOVERNING TERMS:** Use of this model is governed by Mistral’s Modified MIT license. To deploy and customize the model in your environment, please contact Mistral[https://mistral.ai/contact/].

• parakeet-ctc-0.6b-Vietnamese↗

  NVIDIA

• gpt-oss-20b↗

  oki0ki

• MiniCPM4-8B↗

  openbmb

  MiniCPM4 series are highly efficient large language models (LLMs) designed explicitly for end-side devices, which achieves this efficiency through systematic innovation in four key dimensions: model architecture, training data, training algorithms, and inference systems. - MiniCPM4-8B: The flagship of MiniCPM4, with 8B parameters, trained on 8T tokens. (** **Note**: In vLLM's chat API, `add_special_tokens` is `False` by default. This means important special tokens—such as the beginning-of-sequence (BOS) token—will not be added automatically. To ensure the input prompt is correctly formatted for the model, you should explicitly set `extra_body={"add_special_tokens": True}`. On two typical end-side chips, Jetson AGX Orin and RTX 4090, MiniCPM4 demonstrates significantly faster processing speed compared to similar-size models in long text processing tasks. As text length increases, MiniCPM4's efficiency advantage becomes more pronounced. On the Jetson AGX Orin platform, compared to Qwen3-8B, MiniCPM4 achieves approximately 7x decoding speed improvement. MiniCPM4 launches end-side versions with 8B and 0.5B parameter scales, both achieving best-in-class performance in their respective categories.