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AI 模型 84759

• Ornith-1.5-35B-A3B-agentic-NVFP4-FP8↗

  primitive-ai

• Weather-Image-Classification↗

  prithivMLmods

• ppt-pythia-160m-uniform250-previous_mse_restore_mlp_layernorm_warmup2000-seed1024-stage2↗

  qing-yao

• ArliAI-Llama-3-8B-Dolfin-v0.5-GGUF↗

  QuantFactory

• trocr-base-ru↗

  raxtemur

• Qwen3-0.6B-quantized.w4a16↗

  RedHatAI

  This model was obtained by quantizing the weights of Qwen3-0.6B to INT4 data type. This optimization reduces the number of bits per parameter from 16 to 4, reducing the disk size and GPU memory requirements by approximately 75%. Only the weights of the linear operators within transformers blocks are quantized. Weights are quantized using a asymmetric per-group scheme, with group size 64. The GPTQ algorithm is applied for quantization, as implemented in the llm-compressor library. The model was evaluated on the OpenLLM leaderboard tasks (versions 1 and 2), using lm-evaluation-harness, and on reasoning tasks using lighteval. vLLM was used for all evaluations.

• mlfoundations-dev_-_stackoverflow_10000tasks_0p-gguf↗

  RichardErkhov

• OLMo-2-2.7B-Unlearning↗

  sbordt

• Impish_LLAMA_3B_GGUF↗

  SicariusSicariiStuff

• tinyfrank-1.4B↗

  SkunkworksAI