from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "Justin6657/PoPilot",
    torch_dtype="auto",
    device_map="auto",
    trust_remote_code=True
)

tokenizer = AutoTokenizer.from_pretrained(
    "Justin6657/PoPilot",
    trust_remote_code=True
)

# Example usage
prompt = "Write a Python function to calculate fibonacci numbers:"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200, temperature=0.7)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

Training Details

Base Model: Qwen/Qwen2.5-Coder-14B
Fine-tuning Method: LoRA (Low-Rank Adaptation)
Training: Supervised Fine-Tuning (SFT)
Merged: Full model weights (LoRA merged with base)

This model was fine-tuned using LoRA adapters and then merged back into the full model weights. Original LoRA checkpoint path:

/net/projects/CLS/DSI_clinic/justin/checkpoint/augmented_train_Qwen2.5-Coder-14B_full-model_repair-synth_repair-simple-phase4