DeepSeek R1 Distill Qwen 7B represents a significant achievement in model distillation, successfully transferring the reasoning capabilities of the larger DeepSeek R1 model into a more computationally efficient 7B parameter variant. Built upon the Qwen2.5-Math-7B architecture, this model demonstrates how effective distillation techniques can create smaller models that maintain impressive performance across various benchmarks.
Architecture and Training
The model leverages the Qwen2.5-Math-7B architecture as its foundation. The distillation process involved fine-tuning on approximately 800,000 carefully curated samples generated by the larger DeepSeek-R1 model, which itself is a 37B parameter model activated from a 671B parameter architecture. This approach to distillation, as detailed in the DeepSeek R1 paper, demonstrates how reasoning capabilities can be effectively transferred from larger to smaller models.
The parent model, DeepSeek R1, was developed using a sophisticated pipeline incorporating two reinforcement learning (RL) stages and two supervised fine-tuning (SFT) stages. Unlike its sibling model DeepSeek R1-Zero (which used only RL), the training process began with "cold-start" data to prevent issues like endless repetition and poor readability.
Performance and Capabilities
DeepSeek R1 Distill Qwen 7B has demonstrated remarkable performance across several key benchmarks:
- MATH-500
- GPQA Diamond
- LiveCodeBench
The model significantly outperforms the original 7B Qwen model and shows competitive results against larger models in the same class. For optimal performance, the model requires specific parameter settings:
- Temperature: Best results achieved between 0.5 and 0.7
- Careful attention to these settings is crucial, as incorrect values can lead to issues such as endless repetition or incoherent outputs
Within the DeepSeek-R1 family, this 7B model compares favorably to its larger siblings (DeepSeek-R1-Zero and DeepSeek-R1, both 37B parameters), especially considering its significantly smaller size. The research shows that adding an additional RL stage to the distilled model could yield even further improvements in performance.
Technical Implementation and Licensing
The model can be deployed locally using tools such as vLLM with appropriate temperature and maximum length settings. It's available under the MIT License, though it's important to note that its base model, Qwen2.5-Math-7B, is under the Apache 2.0 License.
Implementation best practices include:
- Avoiding system prompts
- Maintaining recommended temperature settings
- Careful consideration of maximum length parameters
For further technical details about the underlying architecture and implementation, developers can refer to the DeepSeek-V3 GitHub repository.
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