QwQ 32B AWQ Introduction QwQ is the reasoning model of the Qwen series. Compared with conventional instruction tuned models, QwQ, which is capable of thinking and reasoning, can achieve significantly enhanced performance in downstream tasks, especially hard problems. QwQ 32B is the medium sized reasoning model, which is capable of achieving competitive performance against state of the art reasoning models, e.g., DeepSeek R1, o1 mini. This repo contains the AWQ quantized 4 bit QwQ 32B model , which has the following features: Type: Causal Language Models Training Stage: Pretraining & Post training (Supervised Finetuning and Reinforcement Learning) Architecture: transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias Number of Parameters: 32.5B Number of Paramaters (Non Embedding): 31.0B Number of Layers: 64 Number of Attention Heads (GQA): 40 for Q and 8 for KV Context Length: Full 131,072 tokens For prompts exceeding 8,192 tokens in length, you must enable YaRN as outlined in this section. Quantization: AWQ 4 bit Note: For the best experience, please review the usage guidelines before deploying QwQ models. You can try our demo or access QwQ models via QwenChat. For more det…
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