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GradientResearch/Qwen3-32B-LoRA-ECHO-KK-GRPO

sourceHugging Faceapache-2.0updated 1y agoView on Hugging Face

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Model Card

Model Card for Qwen3-32B-LoRA-ECHO-KK-GRPO

Based on Qwen3-32B, we applied the ECHO framework to perform LoRA fine-tuning on the KK dataset. Ultimately, it achieved near-perfect scores on the 2–8 PPL test set, surpassing o4-mini, DeepSeek-R1, and o3-mini-high.

Tabel 3: Model performance on K&K logic puzzle task across different degrees of difficulty | model | 2 | 3 | 4 | 5 | 6 | 7 | 8 | |----------------|---------------------------:|--------------------------:|--------------------------:|--------------------:|------------:|-------------:|-------------:| | Qwen3-32B | 0.98 | 0.99 | 0.98 | 0.99 | 0.98 | 0.96 |0.95 | | Deepseek-R1 | 1.00 | 0.97 | 0.95 | 0.93 | 0.91 | 0.93 |0.91 | | o3-mini-high | 1.00 | 1.00 | 1.00 | 1.00 | 0.99 | 0.98 |0.98 | | o4-mini | 1.00 | 1.00 | 0.96 | 0.94 | 0.97 | 0.93 |0.87 | | Qwen3-32B-Echo(GRPO w/Lora) | 0.99 | 1.00 | 1.00 | 1.00 | 0.99 | 1.00 |0.99 |

Quick start

python


from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "GradientResearch/Qwen3-32B-LoRA-ECHO-KK-GRPO"# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

# prepare the model input
prompt = "K & K"
messages = [
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=True # Switches between thinking and non-thinking modes. Default is True.
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

# conduct text completion
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() 

# parsing thinking contenttry:
    # rindex finding 151668 (</think>)
    index = len(output_ids) - output_ids[::-1].index(151668)
except ValueError:
    index = 0

thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n")
content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n")

print("thinking content:", thinking_content)
print("content:", content)

Citation

If you find our work helpful, feel free to give us a cite.

@misc{xiao2025echodecouplinginferencetraining,
      title={Echo: Decoupling Inference and Training for Large-Scale RL Alignment on Heterogeneous Swarms}, 
      author={Jie Xiao and Changyuan Fan and Qingnan Ren and Alfred Long and Yuchen Zhang and Rymon Yu and Eric Yang and Lynn Ai and Shaoduo Gan},
      year={2025},
      eprint={2508.05387},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2508.05387}, 
}