limo-qwen3-8b-math-merged

LIMO-Qwen3-8B-Math-Merged

This is the merged version of the LIMO fine-tuned Qwen3-8B model for mathematical reasoning.

Model Details

• Base Model: Qwen3-8B
• Training Method: LoRA fine-tuning with Unsloth (now merged)
• Dataset: GAIR/LIMO (817 high-quality samples)
• Model Type: Full merged model (not adapter)

Key Features

• ✅ Full merged model - no need to load base model + adapter
• ✅ Ready for inference - works with standard transformers
• ✅ Compatible with benchmarking tools - works with lighteval
• ✅ Mathematical reasoning optimized - trained on LIMO dataset

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "Cbgcbg/limo-qwen3-8b-math-merged",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True
)

tokenizer = AutoTokenizer.from_pretrained(
    "Cbgcbg/limo-qwen3-8b-math-merged",
    trust_remote_code=True
)

# Format input
messages = [
    {"role": "system", "content": "Please reason step by step, and put your final answer within \\boxed{}."},
    {"role": "user", "content": "What is the sum of the first 10 positive integers?"}
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)

inputs = tokenizer(text, return_tensors="pt").to(model.device)

# Generate
with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=512,
        temperature=0.7,
        do_sample=True,
        pad_token_id=tokenizer.eos_token_id
    )

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

Training Configuration

• Batch Size: 8
• Learning Rate: 2e-5
• Epochs: 3
• LoRA Rank: 16
• LoRA Alpha: 32
• LoRA Dropout: 0.1

Performance

This merged model maintains the same mathematical reasoning capabilities as the LoRA adapter but is easier to use and compatible with more tools.

Comparison with LoRA Version

Citation

@misc{ye2025limoreasoning,
    title={LIMO: Less is More for Reasoning},
    author={Yixin Ye and Zhen Huang and Yang Xiao and Ethan Chern and Shijie Xia and Pengfei Liu},
    year={2025},
    eprint={2502.03387},
    archivePrefix={arXiv},
    primaryClass={cs.CL},
    url={https://arxiv.org/abs/2502.03387},
}

Related Models

• LoRA Adapter: Cbgcbg/limo-qwen3-8b-math
• Base Model: unsloth/Qwen3-8B-bnb-4bit