feat: Switch to Llama 3.1 8B (Bedrock-compatible)

- Default model now meta-llama/Llama-3.1-8B-Instruct
- Added multi-model chat format support:
  - Llama 3 format
  - Mistral/Mixtral format
  - Qwen format
  - Gemma format
- Trained model can be imported to AWS Bedrock

components/runpod_trainer/handler.py

@@ -51,20 +51,48 @@ def get_curated_fallback() -> List[Dict]:
     return patterns * 50  # 200 samples
 
 
-def format_for_gemma(item: Dict) -> str:
+def format_for_llm(item: Dict, model_name: str = "") -> str:
-    """Format DDI item for Gemma instruction tuning."""
+    """Format DDI item for LLM instruction tuning. Auto-detects format based on model."""
     severity_name = DDI_SEVERITY.get(item['severity'], 'unknown')
     
-    return f"""<start_of_turn>user
+    user_msg = f"Analyze the drug interaction between {item['drug1']} and {item['drug2']}."
-Analyze the drug interaction between {item['drug1']} and {item['drug2']}.<end_of_turn>
+    assistant_msg = f"""Interaction: {item['interaction_text']}
-<start_of_turn>model
-Interaction: {item['interaction_text']}
 Severity: {severity_name.upper()}
-Recommendation: {"Avoid this combination" if item['severity'] >= 3 else "Monitor patient" if item['severity'] == 2 else "Generally safe"}<end_of_turn>"""
+Recommendation: {"Avoid this combination" if item['severity'] >= 3 else "Monitor patient" if item['severity'] == 2 else "Generally safe"}"""
+    
+    # Llama 3 format
+    if 'llama' in model_name.lower():
+        return f"""<|begin_of_text|><|start_header_id|>user<|end_header_id|>
+
+{user_msg}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
+
+{assistant_msg}<|eot_id|>"""
+    
+    # Mistral/Mixtral format
+    elif 'mistral' in model_name.lower() or 'mixtral' in model_name.lower():
+        return f"""<s>[INST] {user_msg} [/INST] {assistant_msg}</s>"""
+    
+    # Qwen format
+    elif 'qwen' in model_name.lower():
+        return f"""<|im_start|>user
+{user_msg}<|im_end|>
+<|im_start|>assistant
+{assistant_msg}<|im_end|>"""
+    
+    # Gemma format
+    elif 'gemma' in model_name.lower():
+        return f"""<start_of_turn>user
+{user_msg}<end_of_turn>
+<start_of_turn>model
+{assistant_msg}<end_of_turn>"""
+    
+    # Generic fallback
+    else:
+        return f"""### User: {user_msg}\n### Assistant: {assistant_msg}"""
 
 
-def train_gemma_lora(job_input: Dict[str, Any]) -> Dict[str, Any]:
+def train_llm_lora(job_input: Dict[str, Any]) -> Dict[str, Any]:
-    """Train Gemma 3 with QLoRA for DDI classification."""
+    """Train LLM with QLoRA for DDI classification. Supports Llama, Mistral, Qwen, Gemma."""
     import torch
     from transformers import (
         AutoTokenizer,
@@ -78,8 +106,8 @@ def train_gemma_lora(job_input: Dict[str, Any]) -> Dict[str, Any]:
     import tempfile
     import shutil
     
-    # Parameters
+    # Parameters - Default to Llama 3.1 8B (Bedrock-compatible)
-    model_name = job_input.get('model_name', 'google/gemma-3-12b-it')
+    model_name = job_input.get('model_name', 'meta-llama/Llama-3.1-8B-Instruct')
     max_samples = job_input.get('max_samples', 10000)
     epochs = job_input.get('epochs', 1)
     learning_rate = job_input.get('learning_rate', 2e-4)
@@ -101,8 +129,8 @@ def train_gemma_lora(job_input: Dict[str, Any]) -> Dict[str, Any]:
         print(f"Loading DrugBank DDI data (max {max_samples})...")
         raw_data = load_drugbank_data(max_samples=max_samples, severity_filter=severity_filter)
         
-        # Format for Gemma
+        # Format for the target LLM
-        formatted_data = [{"text": format_for_gemma(item)} for item in raw_data]
+        formatted_data = [{"text": format_for_llm(item, model_name)} for item in raw_data]
         dataset = Dataset.from_list(formatted_data)
         
         print(f"Dataset size: {len(dataset)}")
@@ -342,7 +370,7 @@ def handler(job):
     """RunPod serverless handler."""
     job_input = job.get('input', {})
     
-    model_name = job_input.get('model_name', 'google/gemma-3-12b-it')
+    model_name = job_input.get('model_name', 'meta-llama/Llama-3.1-8B-Instruct')
     use_lora = job_input.get('use_lora', True)
     
     # Auto-detect: use LoRA for large models
@@ -352,7 +380,7 @@ def handler(job):
         use_lora = False
     
     if use_lora:
-        return train_gemma_lora(job_input)
+        return train_llm_lora(job_input)
     else:
         return train_bert_classifier(job_input)