pngwn/qwen2.5-0.5b-gsm8k-sft

Qwen2.5-0.5B GSM8K SFT

Supervised fine-tuned model for grade-school math reasoning on GSM8K.

Results

Model	GSM8K test exact-match accuracy	N eval
Base (Qwen/Qwen2.5-0.5B)	0.0008 (1/1319)	1319
Tuned (pngwn/qwen2.5-0.5b-gsm8k-sft)	0.3472 (458/1319)	1319

Training details

• Dataset: openai/gsm8k (main config)
• Train split: 7473 samples
• Test split: 1319 samples
• Epochs: 3
• Learning rate: 2e-5
• Batch size: 4 per device
• Gradient accumulation: 4
• Max sequence length: 1024
• Decoding: greedy (do_sample=False, max_new_tokens=256)
• Answer extraction: regex ####\s*(-?\d+(?:,\d+)*(?:\.\d+)?)

Eval script

The exact eval script used for both baseline and tuned evaluation is included in this repository as eval_gsm8k.py.

Generated by ML Intern

This model repository was generated by ML Intern, an agent for machine learning research and development on the Hugging Face Hub.

• Try ML Intern: https://smolagents-ml-intern.hf.space
• Source code: https://github.com/huggingface/ml-intern

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = 'pngwn/qwen2.5-0.5b-gsm8k-sft'
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

For non-causal architectures, replace AutoModelForCausalLM with the appropriate AutoModel class.