Naholav/diverse-instruction

Diverse Instruction - LoRA Fine-tuned Qwen2.5-Coder-1.5B

This is the best performing checkpoint from the diverse_instruction training configuration.

Model Details

Property	Value
Base Model	Qwen/Qwen2.5-Coder-1.5B-Instruct
Training Dataset	Naholav/CodeGen-Diverse-5K
Training Method	LoRA (Low-Rank Adaptation)
Checkpoint	step-600, epoch-3
Pass@1 (AtCoder Easy)	31.71% (13/41 problems)

Training Configuration

• Prompt Style: Instruction (direct code generation without reasoning)
• System Prompt: "You are an expert programmer. Write clean, efficient code."
• LoRA Rank: 32
• LoRA Alpha: 64
• LoRA Dropout: 0.05
• Learning Rate: 5e-5

Note: All 4 models were trained with identical hyperparameters for fair comparison. Better configurations may be discovered through hyperparameter search methods (e.g., grid search, random search).

All Models Performance Comparison

Evaluated on LiveCodeBench AtCoder Easy problems (41 questions):

Model	Pass@1	Improvement
Base Model (Qwen2.5-Coder-1.5B)	24.39%	-
deep-instruction	26.83%	+10%
diverse-think	29.27%	+20%
deep-think	31.71%	+30%
diverse-instruction (this model)	31.71%	+30%

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch

# Load base model
base_model = AutoModelForCausalLM.from_pretrained(
    "Qwen/Qwen2.5-Coder-1.5B-Instruct",
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Load LoRA adapter
model = PeftModel.from_pretrained(base_model, "Naholav/diverse-instruction")

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-Coder-1.5B-Instruct")

# Generate with instruction prompt
messages = [
    {"role": "system", "content": "You are an expert programmer. Write clean, efficient code."},
    {"role": "user", "content": "Your problem here..."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=2048)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Resources

• GitHub Repository: https://github.com/naholav/CodeGen
• Training Dataset: Naholav/CodeGen-Diverse-5K

Citation

If you use this model, please cite:

@misc{naholav2024codegen,
  author = {naholav},
  title = {CodeGen: LoRA Fine-tuning for Competitive Programming},
  year = {2025},
  publisher = {HuggingFace},
  url = {https://huggingface.co/Naholav/diverse-instruction}
}