GSPO-DeepSeek-R1-Distill-Qwen-1.5B

GSPO-Trained Reasoning Model

This model is fine-tuned using Group Sequence Policy Optimization (GSPO), implementing the algorithm from the paper by Zheng et al. (Qwen Team, Alibaba Inc.). This is an open-source implementation optimized for H100 GPUs.

Model Description

• Base Model: deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
• Training Method: Group Sequence Policy Optimization (GSPO)
• Algorithm Source: Zheng et al. (2024) - Qwen Team, Alibaba Inc.
• Focus: Mathematical reasoning, logical problem-solving
• Parameters: 1.5B

Performance

About GSPO Algorithm

Group Sequence Policy Optimization was developed by Zheng et al. (2024) and offers improvements over traditional PPO/GRPO:

• Sequence-level importance ratios instead of token-level
• Length normalization for fair sequence comparison
• High clipping tolerance (50-75% vs 2-3% for PPO)
• Superior training stability for reasoning tasks

Implementation Details:

• Computes importance ratios at the full sequence level
• Applies length normalization: log_ratio = (current_log_prob - old_log_prob) / response_length
• Handles high clipping rates without training instability
• Optimized for mathematical and logical reasoning

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

# Load the model
model_name = "vivekvar/GSPO-DeepSeek-R1-Distill-Qwen-1.5B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16)

# Example usage
prompt = "Solve step by step: If 3x + 7 = 22, what is x?"
inputs = tokenizer(prompt, return_tensors="pt")

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=256,
        temperature=0.3,
        do_sample=True,
        pad_token_id=tokenizer.eos_token_id
    )

response = tokenizer.decode(outputs[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True)
print(response)

Training Details

Hyperparameters

• Learning Rate: 1e-7
• Clipping Range: ±0.002
• Batch Size: 2
• Group Size: 4
• Training Steps: 8 epochs
• Optimizer: AdamW with 8-bit optimization

Dataset

• Custom reasoning dataset: 500 math/logic problems
• Difficulty distribution: 60% easy, 30% medium, 10% hard
• Problem types: Arithmetic, algebra, logic puzzles, word problems

Training Infrastructure

• GPU: NVIDIA H100
• Framework: PyTorch + Transformers
• Memory optimization: Gradient checkpointing, bfloat16
• Training time: ~4 hours

Implementation Results

Results confirm the stability advantages of GSPO as described in the original paper.

🛠️ Implementation Contribution

This repository provides:

• ✅ Open-source GSPO implementation from the Qwen Team paper
• ✅ H100-optimized training for efficient large-scale training
• ✅ Comprehensive baseline comparisons with PPO and GRPO
• ✅ Reproducible setup with full training scripts
• ✅ Performance validation on reasoning benchmarks

📝 Citation

Original GSPO Paper:

@article{zheng2024gspo,
  title={Group Sequence Policy Optimization},
  author={Chujie Zheng and Shixuan Liu and Mingze Li and Xiong-Hui Chen and Bowen Yu and Chang Gao and Kai Dang and Yuqiong Liu and Rui Men and An Yang and Jingren Zhou and Junyang Lin},
  journal={Qwen Team, Alibaba Inc.},
  year={2024}
}

If using this implementation:

@misc{gspo_implementation2024,
  title={Open-Source Implementation of GSPO with H100 Optimization},
  author={[Varikuti Sara vivek]},
  year={2025},
  note={Implementation of Zheng et al. GSPO algorithm},
  url={https://github.com/vivekvar-dl/gpso}
}

Resources

• Original GSPO Paper: Link to paper by Qwen Team, Alibaba Inc.
• Training Scripts: Fully reproducible setup
• Baseline Comparisons: PPO and GRPO implementations included

Acknowledgments

Algorithm Credit: This model uses the GSPO algorithm developed by the Qwen Team at Alibaba Inc.: Chujie Zheng, Shixuan Liu, Mingze Li, Xiong-Hui Chen, Bowen Yu, Chang Gao, Kai Dang, Yuqiong Liu, Rui Men, An Yang, Jingren Zhou, Junyang Lin

Implementation: Open-source implementation with H100 optimizations for community use.

Limitations

• Algorithm: Implements GSPO as described in the original paper
• Model size: 1.5B parameters - may need scaling for complex tasks
• Training data: Custom dataset - performance may vary on other domains
• Hardware: Optimized for H100, may need adjustments for other GPUs

License

Apache 2.0 - See repository for full details.

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This model implements the GSPO algorithm described by Zheng et al. (Qwen Team, Alibaba Inc.) with practical optimizations for open-source community use.