from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

tokenizer = AutoTokenizer.from_pretrained("grpo_gsm8k/checkpoint-500")
model = AutoModelForCausalLM.from_pretrained(
    "grpo_gsm8k/checkpoint-500",
    torch_dtype=torch.float16,
    device_map="auto"
)

prompt = "What is the sqrt of 101?"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(
    **inputs,
    max_new_tokens=1024,
    temperature=0.4,
    top_p=0.95,
    repetition_penalty=1.15
)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

Training procedure

Visualize in Weights & Biases

This model was trained with GRPO, a method introduced in DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

Citations

bibtex

@article{zhihong2024deepseekmath,
    title        = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
    author       = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
    year         = 2024,
    eprint       = {arXiv:2402.03300},
}

bibtex

@misc{vonwerra2022trl,
	title        = {{TRL: Transformer Reinforcement Learning}},
	author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
	year         = 2020,
	journal      = {GitHub repository},
	publisher    = {GitHub},
	howpublished = {\url{https://github.com/huggingface/trl}}
}

axondendriteplus/llama-3.2-3B-GGK

llama-3.2-3B-GGK(Grpo-Gsm8k): GRPO-Finetuned Llama-3.2-3B on GSM8K

Checkpoints

Model Card for outputs

Quick Start

Training procedure

Citations

axondendriteplus/
llama-3.2-3B-GGK