Upload app.py with huggingface_hub

app.py

@@ -0,0 +1,251 @@
+"""
+🔧 LoRA Merger Space
+FSDP 체크포인트를 다운받아 베이스 모델과 병합 후 Hub에 업로드합니다.
+"""
+
+import os
+import torch
+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+from huggingface_hub import snapshot_download, HfApi, login
+import logging
+
+# Logging
+logging.basicConfig(level=logging.INFO, format='%(asctime)s | %(message)s')
+logger = logging.getLogger(__name__)
+
+# Configuration
+SOURCE_REPO = "ongilLabs/IB-Math-Ontology-7B"  # LoRA adapter
+BASE_MODEL = "Qwen/Qwen2.5-Math-7B-Instruct"
+OUTPUT_REPO = "ongilLabs/IB-Math-Ontology-7B"  # Merged model output
+
+def merge_model(progress=gr.Progress()):
+    """메인 병합 함수"""
+    logs = []
+    
+    def log(msg):
+        logger.info(msg)
+        logs.append(msg)
+        return "\n".join(logs)
+    
+    try:
+        # Step 1: Download checkpoint
+        progress(0.1, desc="📥 Downloading checkpoint...")
+        log("📥 Downloading checkpoint from Hub...")
+        
+        local_dir = snapshot_download(
+            repo_id=SOURCE_REPO,
+            local_dir="/tmp/checkpoint",
+            token=os.getenv("HF_TOKEN")
+        )
+        log(f"   Downloaded to: {local_dir}")
+        
+        # Step 2: Find adapter
+        progress(0.2, desc="🔍 Finding adapter...")
+        adapter_path = None
+        
+        # Check locations
+        for path in [f"{local_dir}/last-checkpoint", local_dir]:
+            if os.path.exists(f"{path}/adapter_config.json"):
+                adapter_path = path
+                log(f"✅ Found adapter at: {path}")
+                break
+        
+        if not adapter_path:
+            # List files for debugging
+            log("❌ adapter_config.json not found!")
+            log("📂 Available files:")
+            for root, dirs, files in os.walk(local_dir):
+                for f in files:
+                    rel_path = os.path.relpath(os.path.join(root, f), local_dir)
+                    log(f"   - {rel_path}")
+            return "\n".join(logs) + "\n\n❌ FAILED: No adapter found"
+        
+        # Step 3: Load base model
+        progress(0.3, desc="📦 Loading base model...")
+        log(f"📦 Loading base model: {BASE_MODEL}")
+        log("   This may take 3-5 minutes...")
+        
+        base_model = AutoModelForCausalLM.from_pretrained(
+            BASE_MODEL,
+            torch_dtype=torch.bfloat16,
+            device_map="auto",
+            trust_remote_code=True,
+        )
+        log("   ✅ Base model loaded!")
+        
+        # Step 4: Load tokenizer
+        progress(0.4, desc="📝 Loading tokenizer...")
+        log("📝 Loading tokenizer...")
+        tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL, trust_remote_code=True)
+        log("   ✅ Tokenizer loaded!")
+        
+        # Step 5: Load LoRA adapter
+        progress(0.5, desc="🔗 Loading LoRA adapter...")
+        log(f"🔗 Loading LoRA adapter from: {adapter_path}")
+        
+        model = PeftModel.from_pretrained(
+            base_model,
+            adapter_path,
+            torch_dtype=torch.bfloat16,
+        )
+        log("   ✅ LoRA adapter loaded!")
+        
+        # Step 6: Merge
+        progress(0.6, desc="🔧 Merging LoRA with base model...")
+        log("🔧 Merging LoRA weights with base model...")
+        model = model.merge_and_unload()
+        log("   ✅ Merge complete!")
+        
+        # Step 7: Save
+        progress(0.7, desc="💾 Saving merged model...")
+        output_dir = "/tmp/merged_model"
+        log(f"💾 Saving merged model to: {output_dir}")
+        
+        os.makedirs(output_dir, exist_ok=True)
+        model.save_pretrained(output_dir, safe_serialization=True, max_shard_size="5GB")
+        tokenizer.save_pretrained(output_dir)
+        
+        # List saved files
+        log("   📂 Saved files:")
+        for f in os.listdir(output_dir):
+            size_mb = os.path.getsize(os.path.join(output_dir, f)) / (1024 * 1024)
+            log(f"      - {f}: {size_mb:.1f} MB")
+        
+        # Step 8: Create model card
+        progress(0.8, desc="📝 Creating model card...")
+        log("📝 Creating model card...")
+        
+        model_card = """---
+license: apache-2.0
+base_model: Qwen/Qwen2.5-Math-7B-Instruct
+tags:
+  - math
+  - ib-mathematics
+  - qwen2
+  - fine-tuned
+  - education
+  - ontology
+  - chain-of-thought
+language:
+  - en
+pipeline_tag: text-generation
+---
+
+# IB-Math-Ontology-7B
+
+Fine-tuned Qwen2.5-Math-7B-Instruct for IB Mathematics AA with ontology-based Chain-of-Thought reasoning.
+
+## Features
+- 🎯 **IB Math AA Specialized**: Trained on 1,332 ontology-based examples
+- 💭 **Chain-of-Thought**: Uses `<think>` tags for step-by-step reasoning
+- 📚 **Curriculum-Aligned**: Covers all 5 IB Math AA topics
+- ⚠️ **Pitfall Awareness**: Warns about common student mistakes
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained("ongilLabs/IB-Math-Ontology-7B", torch_dtype="auto", device_map="auto")
+tokenizer = AutoTokenizer.from_pretrained("ongilLabs/IB-Math-Ontology-7B")
+
+prompt = "Find the derivative of f(x) = x³ - 2x² + 5x [6 marks]"
+messages = [
+    {"role": "system", "content": "You are an expert IB Mathematics AA tutor. Think step-by-step and explain concepts clearly."},
+    {"role": "user", "content": prompt}
+]
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer(text, return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=512)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+## Training Details
+- **Base Model**: Qwen2.5-Math-7B-Instruct
+- **Method**: LoRA (r=64, alpha=128)
+- **Dataset**: 1,332 IB Math Ontology examples with CoT
+- **Hardware**: NVIDIA A100 (80GB)
+- **Epochs**: 3
+- **Precision**: BF16
+"""
+        
+        with open(os.path.join(output_dir, "README.md"), "w") as f:
+            f.write(model_card)
+        log("   ✅ Model card created!")
+        
+        # Step 9: Upload to Hub
+        progress(0.9, desc="🚀 Uploading to Hub...")
+        log(f"🚀 Uploading to Hub: {OUTPUT_REPO}")
+        
+        api = HfApi(token=os.getenv("HF_TOKEN"))
+        api.upload_folder(
+            folder_path=output_dir,
+            repo_id=OUTPUT_REPO,
+            commit_message="✨ Merged LoRA with base model - Production ready",
+        )
+        
+        log(f"   ✅ Uploaded to: https://huggingface.co/{OUTPUT_REPO}")
+        
+        # Done!
+        progress(1.0, desc="🎉 Complete!")
+        log("")
+        log("=" * 50)
+        log("🎉 SUCCESS! Model merged and uploaded!")
+        log("=" * 50)
+        log(f"📍 Model URL: https://huggingface.co/{OUTPUT_REPO}")
+        
+        return "\n".join(logs)
+        
+    except Exception as e:
+        log(f"\n❌ ERROR: {str(e)}")
+        import traceback
+        log(traceback.format_exc())
+        return "\n".join(logs)
+
+
+def create_ui():
+    """Gradio UI 생성"""
+    with gr.Blocks(title="LoRA Merger", theme=gr.themes.Soft()) as app:
+        gr.Markdown("""
+        # 🔧 IB-Math-Ontology LoRA Merger
+        
+        This Space merges the LoRA adapter with the base model.
+        
+        **Source**: `ongilLabs/IB-Math-Ontology-7B` (LoRA adapter)  
+        **Base**: `Qwen/Qwen2.5-Math-7B-Instruct`  
+        **Output**: `ongilLabs/IB-Math-Ontology-7B` (merged model)
+        
+        **Steps:**
+        1. Download LoRA checkpoint from Hub
+        2. Load base model (Qwen2.5-Math-7B-Instruct)
+        3. Load LoRA adapter
+        4. Merge LoRA weights into base model
+        5. Upload merged model to Hub
+        """)
+        
+        with gr.Row():
+            merge_btn = gr.Button("🚀 Start Merge", variant="primary", scale=2)
+        
+        output = gr.Textbox(
+            label="Logs",
+            lines=30,
+            max_lines=50,
+            show_copy_button=True
+        )
+        
+        merge_btn.click(fn=merge_model, outputs=output)
+        
+        gr.Markdown("""
+        ---
+        **Note**: 이 작업은 약 10-15분 소요됩니다. GPU 메모리가 충분한지 확인하세요.
+        """)
+    
+    return app
+
+
+if __name__ == "__main__":
+    app = create_ui()
+    app.launch()
+