LahiruWije/Qwen2.5-0.5B-Instruct-GPRO-GSM8K

Model Card for Qwen2.5-0.5B-Instruct-GSM8K-Reasoning

This model is a fine-tuned version of the Qwen2.5-0.5B-Instruct model, specifically adapted for mathematical reasoning tasks using the GSM8K dataset. It leverages GPRO (Generalized Policy Optimization for Reasoning) methods, as described in the DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models paper, to enhance its reasoning capabilities. The fine-tuning process was performed using Unsloth for efficiency and TRL (Transformer Reinforcement Learning) for reinforcement learning-based training.

Model Details

How to Get Started with the Model

Use the code below to load and use the model with vLLM & Unsloth:

from unsloth import FastLanguageModel
from vllm import SamplingParams
import torch

# Load the Model & Tokenizer
model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "AdamLucek/Qwen2.5-3B-Instruct-GRPO-2K-GSM8K",
    max_seq_length = 2048,
    load_in_4bit = True,
    fast_inference = True,
    gpu_memory_utilization = 0.7,
)

# Prep the Message
PROMPT = "How many r's are in the word strawberry?"

SYSTEM_PROMPT = """
A conversation between User and Assistant. The user asks a question,
and the Assistant solves it. The assistant first thinks about the
reasoning process in the mind and then provides the user with the answer.
Respond in the following format:
<reasoning>
...
</reasoning>
<answer>
...
</answer>
"""

text = tokenizer.apply_chat_template([
    {"role" : "system", "content" : SYSTEM_PROMPT},
    {"role" : "user", "content" : PROMPT},
], tokenize = False, add_generation_prompt = True)

# Generate a response
sampling_params = SamplingParams(
    temperature = 0.8,
    top_p = 0.95,
    max_tokens = 1024,
)
output = model.fast_generate(
    text,
    sampling_params = sampling_params,
)[0].outputs[0].text

Model Description

• Model type: Transformer-based language model fine-tuned for mathematical reasoning.
• Language(s) (NLP): English
• License: Apache 2.0
• Finetuned from model: Qwen/Qwen2.5-0.5B-Instruct

Uses

Direct Use

This model is intended for mathematical reasoning tasks, particularly for solving grade-school-level math problems as found in the GSM8K dataset. It can be used directly for question-answering tasks involving arithmetic and reasoning.

Downstream Use [optional]

The model can be fine-tuned further for specific applications, such as tutoring systems, automated problem-solving tools, or other educational technologies.

Out-of-Scope Use

This model is not designed for:

• High-level mathematical research or advanced problem-solving.
• Non-mathematical reasoning tasks without additional fine-tuning.
• Applications requiring high precision in domains outside its training data.

Bias, Risks, and Limitations

• Bias: The model may inherit biases present in the GSM8K dataset or the base model.
• Risks: Incorrect reasoning or answers in critical applications (e.g., education or finance) could lead to misinformation.
• Limitations: The model's performance is constrained by the quality and scope of the GSM8K dataset and the base model's capabilities.

Recommendations

Users should:

• Validate the model's outputs for critical applications.
• Fine-tune the model further for domain-specific tasks.
• Be aware of potential biases and limitations in reasoning capabilities.

Citations

Cite GRPO as:

@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},
}

Cite TRL as:

@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 Gallouédec},
    year         = 2020,
    journal      = {GitHub repository},
    publisher    = {GitHub},
    howpublished = {\url{https://github.com/huggingface/trl}}
}