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AI Models 535

• whisper-large-v2-mlx↗

  mlx-community

• MiniCPM4-MCP↗

  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. - MiniCPM4-0.5B: The small version of MiniCPM4, with 0.5B parameters, trained on 1T tokens. - MiniCPM4-8B-Eagle-FRSpec: Eagle head for FRSpec, accelerating speculative inference for MiniCPM4-8B. - MiniCPM4-8B-Eagle-FRSpec-QAT-cpmcu: Eagle head trained with QAT for FRSpec, efficiently integrate speculation and quantization to achieve ultra acceleration for MiniCPM4-8B. - MiniCPM4-8B-Eagle-vLLM: Eagle head in vLLM format, accelerating speculative inference for MiniCPM4-8B. - MiniCPM4-8B-marlin-Eagle-vLLM: Quantized Eagle head for vLLM format, accelerating speculative inference for MiniCPM4-8B. - BitCPM4-0.5B: Extreme ternary quantization applied to MiniCPM4-0.5B compresses model parameters into ternary values, achieving a 90% reduction in bit width. - BitCPM4-1B: Extreme ternary quantization applied to MiniCPM3-1B compresses model parameters into ternary values, achieving a 90% reduction in bit width. - MiniCPM4-Survey: Based on MiniCPM4-8B, accepts users' quiries as input and autonomously generate trustworthy, long-form survey papers. - MiniCPM4-MCP: Based on MiniCPM4-8B, accepts users' queries and available MCP tools as input and autonomously calls relevant MCP tools to satisfy users' requirements. (**<-- you are here**) **MiniCPM4-MCP** is an open-source on-device LLM agent model jointly developed by THUNLP, Renmin University of China and ModelBest, built on MiniCPM-4 with 8 billion parameters. It is capable of solving a wide range of real-world tasks by interacting with various tool and data resources through MCP. The MCP Servers supported by MiniCPM4-MCP include Airbnb, Amap-Maps, Arxiv-MCP-Server, Calculator, Computer-Control-MCP, Desktop-commander, Filesystem, Github, Gaode, MCP-Code-Executor, MCP-DOCx, PPT, PPTx, Simple-Time-Server, Slack, and Whisper. Follow the instructions provided in each server's reposi

• CrisperWhisper↗

  unsloth

• tiny-random-WhisperForConditionalGeneration↗

  Xenova

• whisper-large-v2-swiss-german-spc↗

  PhilippeFuhrioso

• whisper_base_persian↗

  C1Tech

• whisperkit-litert↗

  argmaxinc

• whisper-tiny-GGUF↗

  oxide-lab

  **Q5_0** is the "last good" option. Lower quantizations (Q4, Q3, Q2) lead to a sharp loss of quality and meaningless output. For most use cases, we recommend **q4_k** format as it provides the best balance of: - Size reduction (~65% smaller) - Quality (minimal degradation) - Speed (faster inference than higher quantizations)

• whisper-small-khmer-v2↗

  seanghay

• whisper-large-v3-turbo-tj↗

  re-skill