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123 lines
3.4 KiB
123 lines
3.4 KiB
"""Simple script to evaluate LoRA model performance."""
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import argparse
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import sys
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from pathlib import Path
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# Add project root to Python path
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project_root = str(Path(__file__).parent.parent)
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sys.path.append(project_root)
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from unsloth import FastLanguageModel
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from vllm import SamplingParams
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from src import (
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apply_chat_template,
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build_reward_correctness_fn,
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build_user_prompt,
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get_qa_dataset,
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get_system_prompt,
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)
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def main():
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"""Run LoRA model evaluation."""
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parser = argparse.ArgumentParser(description="Evaluate LoRA model")
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parser.add_argument("--model_name", type=str, required=True, help="Name/path of the base model")
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parser.add_argument("--lora_path", type=str, required=True, help="Path to LoRA weights")
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args = parser.parse_args()
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print(f"🚀 Setting up model {args.model_name} with LoRA from {args.lora_path}...")
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# Setup model with LoRA support
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model, tokenizer = FastLanguageModel.from_pretrained(
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model_name=args.model_name,
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max_seq_length=4096 * 2,
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load_in_4bit=True,
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fast_inference=True,
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max_lora_rank=64,
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)
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# Setup LoRA
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model = FastLanguageModel.get_peft_model(
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model,
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r=64,
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target_modules=[
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"q_proj",
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"k_proj",
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"v_proj",
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"o_proj",
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"gate_proj",
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"up_proj",
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"down_proj",
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],
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lora_alpha=64,
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use_gradient_checkpointing=True,
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random_state=3407,
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)
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# Setup sampling params
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sampling_params = SamplingParams(
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temperature=0.5,
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top_p=0.95,
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max_tokens=4096,
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)
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def generate_fn(inputs):
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"""Generate responses for inputs."""
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messages = [
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{
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"messages": [
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{"role": "system", "content": get_system_prompt()},
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{"role": "user", "content": build_user_prompt(input_text)},
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]
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}
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for input_text in inputs
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]
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lora_request = model.load_lora(args.lora_path)
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outputs = model.fast_generate(
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[apply_chat_template(msg, tokenizer=tokenizer)["text"] for msg in messages],
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sampling_params=sampling_params,
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lora_request=lora_request,
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)
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# Format outputs as chat messages
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formatted_outputs = []
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for output in outputs:
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formatted_outputs.append(
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{
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"messages": [
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{"role": "system", "content": get_system_prompt()},
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{"role": "assistant", "content": output.outputs[0].text},
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]
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}
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)
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return formatted_outputs
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# Get dataset
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_, test_dataset = get_qa_dataset()
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questions = test_dataset["prompt"]
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answers = test_dataset["answer"]
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print(f"📝 Evaluating {len(questions)} questions...")
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# Build verifier
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verify_fn = build_reward_correctness_fn(generate_fn, tokenizer)
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# Run evaluation
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completions = generate_fn(questions)
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rewards = verify_fn(questions, completions, answer=answers)
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accuracy = sum(rewards) / len(rewards)
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print(f"\n{'=' * 50}")
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print("🎯 LORA MODEL EVALUATION RESULTS:")
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print(f"{'=' * 50}")
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print(f"✨ Base Model: {args.model_name}")
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print(f"🔧 LoRA Path: {args.lora_path}")
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print(f"📊 Accuracy: {accuracy:.4f} ({sum(rewards)}/{len(rewards)} correct)")
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if __name__ == "__main__":
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main()
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