feat: add evaluation scripts for base and LoRA models

- Introduced `eval_base.py` for evaluating base model performance.
- Introduced `eval_lora.py` for evaluating LoRA model performance with additional LoRA weight handling.

scripts/eval_base.py

@@ -0,0 +1,99 @@
+"""Simple script to evaluate base model performance."""
+
+import argparse
+import sys
+from pathlib import Path
+
+# Add project root to Python path
+project_root = str(Path(__file__).parent.parent)
+sys.path.append(project_root)
+
+from unsloth import FastLanguageModel
+from vllm import SamplingParams
+
+from src import (
+    apply_chat_template,
+    build_reward_correctness_fn,
+    build_user_prompt,
+    get_qa_dataset,
+    get_system_prompt,
+)
+
+
+def main():
+    """Run base model evaluation."""
+    parser = argparse.ArgumentParser(description="Evaluate base model")
+    parser.add_argument("--model_name", type=str, required=True, help="Name/path of the model to evaluate")
+    args = parser.parse_args()
+
+    print(f"🚀 Setting up model {args.model_name}...")
+
+    # Setup model
+    model, tokenizer = FastLanguageModel.from_pretrained(
+        model_name=args.model_name,
+        max_seq_length=4096 * 2,
+        load_in_4bit=True,
+        fast_inference=True,
+    )
+
+    # Setup sampling params
+    sampling_params = SamplingParams(
+        temperature=0.5,
+        top_p=0.95,
+        max_tokens=4096,
+    )
+
+    def generate_fn(inputs):
+        """Generate responses for inputs."""
+        messages = [
+            {
+                "messages": [
+                    {"role": "system", "content": get_system_prompt()},
+                    {"role": "user", "content": build_user_prompt(input_text)},
+                ]
+            }
+            for input_text in inputs
+        ]
+
+        outputs = model.fast_generate(
+            [apply_chat_template(msg, tokenizer=tokenizer)["text"] for msg in messages],
+            sampling_params=sampling_params,
+        )
+
+        # Format outputs as chat messages
+        formatted_outputs = []
+        for output in outputs:
+            formatted_outputs.append(
+                {
+                    "messages": [
+                        {"role": "system", "content": get_system_prompt()},
+                        {"role": "assistant", "content": output.outputs[0].text},
+                    ]
+                }
+            )
+        return formatted_outputs
+
+    # Get dataset
+    _, test_dataset = get_qa_dataset()
+    questions = test_dataset["prompt"]
+    answers = test_dataset["answer"]
+
+    print(f"📝 Evaluating {len(questions)} questions...")
+
+    # Build verifier
+    verify_fn = build_reward_correctness_fn(generate_fn, tokenizer)
+
+    # Run evaluation
+    completions = generate_fn(questions)
+    rewards = verify_fn(questions, completions, answer=answers)
+    accuracy = sum(rewards) / len(rewards)
+
+    print(f"\n{'=' * 50}")
+    print("🎯 BASE MODEL EVALUATION RESULTS:")
+    print(f"{'=' * 50}")
+    print(f"✨ Model: {args.model_name}")
+    print(f"📊 Accuracy: {accuracy:.4f} ({sum(rewards)}/{len(rewards)} correct)")
+
+
+if __name__ == "__main__":
+    main()

scripts/eval_lora.py

@@ -0,0 +1,122 @@
+"""Simple script to evaluate LoRA model performance."""
+
+import argparse
+import sys
+from pathlib import Path
+
+# Add project root to Python path
+project_root = str(Path(__file__).parent.parent)
+sys.path.append(project_root)
+
+from unsloth import FastLanguageModel
+from vllm import SamplingParams
+
+from src import (
+    apply_chat_template,
+    build_reward_correctness_fn,
+    build_user_prompt,
+    get_qa_dataset,
+    get_system_prompt,
+)
+
+
+def main():
+    """Run LoRA model evaluation."""
+    parser = argparse.ArgumentParser(description="Evaluate LoRA model")
+    parser.add_argument("--model_name", type=str, required=True, help="Name/path of the base model")
+    parser.add_argument("--lora_path", type=str, required=True, help="Path to LoRA weights")
+    args = parser.parse_args()
+
+    print(f"🚀 Setting up model {args.model_name} with LoRA from {args.lora_path}...")
+
+    # Setup model with LoRA support
+    model, tokenizer = FastLanguageModel.from_pretrained(
+        model_name=args.model_name,
+        max_seq_length=4096 * 2,
+        load_in_4bit=True,
+        fast_inference=True,
+        max_lora_rank=64,
+    )
+
+    # Setup LoRA
+    model = FastLanguageModel.get_peft_model(
+        model,
+        r=64,
+        target_modules=[
+            "q_proj",
+            "k_proj",
+            "v_proj",
+            "o_proj",
+            "gate_proj",
+            "up_proj",
+            "down_proj",
+        ],
+        lora_alpha=64,
+        use_gradient_checkpointing=True,
+        random_state=3407,
+    )
+
+    # Setup sampling params
+    sampling_params = SamplingParams(
+        temperature=0.5,
+        top_p=0.95,
+        max_tokens=4096,
+    )
+
+    def generate_fn(inputs):
+        """Generate responses for inputs."""
+        messages = [
+            {
+                "messages": [
+                    {"role": "system", "content": get_system_prompt()},
+                    {"role": "user", "content": build_user_prompt(input_text)},
+                ]
+            }
+            for input_text in inputs
+        ]
+
+        lora_request = model.load_lora(args.lora_path)
+        outputs = model.fast_generate(
+            [apply_chat_template(msg, tokenizer=tokenizer)["text"] for msg in messages],
+            sampling_params=sampling_params,
+            lora_request=lora_request,
+        )
+
+        # Format outputs as chat messages
+        formatted_outputs = []
+        for output in outputs:
+            formatted_outputs.append(
+                {
+                    "messages": [
+                        {"role": "system", "content": get_system_prompt()},
+                        {"role": "assistant", "content": output.outputs[0].text},
+                    ]
+                }
+            )
+        return formatted_outputs
+
+    # Get dataset
+    _, test_dataset = get_qa_dataset()
+    questions = test_dataset["prompt"]
+    answers = test_dataset["answer"]
+
+    print(f"📝 Evaluating {len(questions)} questions...")
+
+    # Build verifier
+    verify_fn = build_reward_correctness_fn(generate_fn, tokenizer)
+
+    # Run evaluation
+    completions = generate_fn(questions)
+    rewards = verify_fn(questions, completions, answer=answers)
+    accuracy = sum(rewards) / len(rewards)
+
+    print(f"\n{'=' * 50}")
+    print("🎯 LORA MODEL EVALUATION RESULTS:")
+    print(f"{'=' * 50}")
+    print(f"✨ Base Model: {args.model_name}")
+    print(f"🔧 LoRA Path: {args.lora_path}")
+    print(f"📊 Accuracy: {accuracy:.4f} ({sum(rewards)}/{len(rewards)} correct)")
+
+
+if __name__ == "__main__":
+    main()