📊 Benchmark Results

Evaluated on 100 competitive programming problems.

🚀 Usage

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

base_model_id = "Qwen/Qwen2.5-Coder-1.5B-Instruct"
adapter_id    = "Ashura7/cp-gpt-qwen2.5-coder-1.5b-lora"

tokenizer = AutoTokenizer.from_pretrained(base_model_id)
base      = AutoModelForCausalLM.from_pretrained(base_model_id, torch_dtype=torch.float16, device_map="auto")
model     = PeftModel.from_pretrained(base, adapter_id)

problem = "Given an array of integers, find the subarray with maximum sum."

messages = [
    {"role": "system", "content": "You are CP-GPT, an expert competitive programming assistant."},
    {"role": "user",   "content": f"Solve: {problem}"},
]

text   = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)

with torch.no_grad():
    output = model.generate(**inputs, max_new_tokens=512, temperature=0.1)

print(tokenizer.decode(output[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True))

⚙️ Training Config

📁 Project

Part of CP-GPT — Phase 2 adds RAG (FAISS), FastAPI backend on Render, and Gradio UI on HF Spaces.