from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("2796gauravc/qwen2.5-0.5b-math")
tokenizer = AutoTokenizer.from_pretrained("2796gauravc/qwen2.5-0.5b-math")

question = "Janet has 10 apples. She gives 3 to her friend. How many does she have left?"
prompt = f"<|im_start|>user\n{question}<|im_end|>\n<|im_start|>assistant\n"

inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0]))

Training Details

Method: QLoRA (4-bit quantization + LoRA adapters)
LoRA Rank: 16
LoRA Alpha: 32
Target Modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Novel Contribution: Structured Reasoning Templates that constrain output format

Citation

bibtex

@misc{qwen2.5_0.5b_math},
  author = {Gaurav Chaudhary},
  title = {qwen2.5-0.5b-math: Small Model Math Reasoning},
  year = {2026},
  publisher = {HuggingFace},
  url = {https://huggingface.co/2796gauravc/qwen2.5-0.5b-math}
}