QwQ-32B

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 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

Note: For the best experience, please review the usage guidelines before deploying QwQ models.

For more details, please refer to our blog, GitHub, and Documentation.

Usage Guidelines

To achieve optimal performance, we recommend the following settings:

1. Enforce Thoughtful Output: Ensure the model starts with "<think>\n" to prevent generating empty thinking content, which can degrade output quality. If you use apply_chat_template and set add_generation_prompt=True, this is already automatically implemented, but it may cause the response to lack the <think> tag at the beginning. This is normal behavior.

2. Sampling Parameters:

   • Use Temperature=0.6 and TopP=0.95 instead of Greedy decoding to avoid endless repetitions.
   • Use TopK between 20 and 40 to filter out rare token occurrences while maintaining the diversity of the generated output.

3. No Thinking Content in History: In multi-turn conversations, the historical model output should only include the final output part and does not need to include the thinking content. This feature is already implemented in apply_chat_template.

4. Standardize Output Format: We recommend using prompts to standardize model outputs when benchmarking.

   • Math Problems: Include "Please reason step by step, and put your final answer within \boxed{}." in the prompt.
   • Multiple-Choice Questions: Add the following JSON structure to the prompt to standardize responses: "Please show your choice in the answer field with only the choice letter, e.g.,\"answer\": \"C\"." in the prompt.

Evaluation & Performance

Detailed evaluation results are reported in this 📑 blog.

For requirements on GPU memory and the respective throughput, see results here.