llama-3.1-8b-instruct-limo-lora

This model is a fine-tuned version of meta-llama/Llama-3.1-8B-Instruct model. The fine-tuning was performed using Low-Rank Adaptation (LoRA) on the LIMO dataset to enhance the model's reasoning capabilities, based on the work in the paper: LIMO: Less is More for Reasoning.

This repo contains the merged model weights. The LoRA adapter version can be found from here.

Model description

Usage

To utilize this model for text generation tasks, follow the steps below:

Installation

Ensure you have the necessary libraries installed:

pip install torch transformers

Generating Text

from transformers import AutoModelForCausalLM, AutoTokenizer
  
model_name = "t83714/llama-3.1-8b-instruct-limo"
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = "How much is (2+5)x5/7"

# Tokenize the input
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

# Generate the output
output = model.generate(**inputs, max_length=8000)
print(tokenizer.decode(output[0], skip_special_tokens=True))

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 5e-06
  • train_batch_size: 1
  • eval_batch_size: 8
  • seed: 42
  • distributed_type: multi-GPU
  • optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: cosine
  • num_epochs: 15

Framework versions

  • PEFT 0.12.0
  • Transformers 4.49.0
  • Pytorch 2.6.0+cu124
  • Datasets 3.3.2
  • Tokenizers 0.21.0

Acknowledgment

This model is trained based on the work of Ye et al. (2025). If you use this model, please also consider citing their paper:

@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}, 
}
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