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AI 모델 84759

• Qwen3-1.7B-MLX-bf16↗

  阿里巴巴 · Qwen

• SAE-Res-Qwen3-30B-A3B-Base-W128K-L0_100↗

  阿里巴巴 · Qwen

• Phi-3-mini-4k-instruct-iMat-GGUF↗

  qwp4w3hyb

• Llama-3.2-1B-Amharic-Instruct↗

  rasyosef

• Meta-Llama-3.1-8B-FP8↗

  RedHatAI

  Quantized version of Meta-Llama-3.1-8B. It achieves an average score of 65.90 on the OpenLLM benchmark (version 1), whereas the unquantized model achieves 66.47. This model was obtained by quantizing the weights and activations of Meta-Llama-3.1-8B to FP8 data type, ready for inference with vLLM built from source. This optimization reduces the number of bits per parameter from 16 to 8, reducing the disk size and GPU memory requirements by approximately 50%. Only the weights and activations of the linear operators within transformers blocks are quantized. Symmetric per-tensor quantization is applied, in which a single linear scaling maps the FP8 representations of the quantized weights and activations. LLM Compressor is used for quantization with 512 sequences of UltraChat.

• Trinity-Large-Thinking-NVFP4↗

  RedHatAI

• BramVanroy_-_llama2-13b-ft-mc4_nl_cleaned_tiny-gguf↗

  RichardErkhov

• Isaak-Carter_-_J.O.S.I.E.3-Beta9-7B-slerp-gguf↗

  RichardErkhov

• m8than_-_mistral-7b-instruct-0.2-gguf↗

  RichardErkhov

• PoseAnything↗

  Ryan241005