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AI 모델 84760

• Qwen3 VL 4B (Reasoning)↗

  阿里巴巴 · Qwen

• Qwen2-72B-Instruct-iMat-GGUF↗

  qwp4w3hyb

• Qwen3-4B-quantized.w4a16↗

  RedHatAI

  This model was obtained by quantizing the weights of Qwen3-4B 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.

• Qwen3.5-4B-Clarity-1.0-GGUF↗

  Regic1d3

• anthracite-org_-_magnum-v2-32b-gguf↗

  RichardErkhov

• semcoder_-_semcoder_1030-gguf↗

  RichardErkhov

• tlphams_-_solar-10.7b-merged-v0.1-gguf↗

  RichardErkhov

• CodeSearch-ModernBERT-Crow-Plus↗

  Shuu12121

• DarijaBERT↗

  SI2M-Lab

• Assistant_Pepe_32B↗

  SicariusSicariiStuff