Upload distilled Qwen2.5-Coder-3B model with knowledge distillation

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+model_f16.gguf filter=lfs diff=lfs merge=lfs -text
+model_q4_0.gguf filter=lfs diff=lfs merge=lfs -text
+model_q5_0.gguf filter=lfs diff=lfs merge=lfs -text
+model_q8_0.gguf filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+library_name: peft
+base_model: Qwen/Qwen2.5-Coder-3B-Instruct-AWQ
+tags:
+- knowledge-distillation
+- code-generation
+- qwen
+- lora
+- distilled
+license: apache-2.0
+---
+
+# Qwen2.5-Coder-3B Distilled Model
+
+This is a **knowledge-distilled** version of Qwen2.5-Coder-3B-Instruct-AWQ, trained using knowledge distillation from Qwen2.5-Coder-7B-Instruct-AWQ.
+
+## Model Details
+
+- **Base Model**: Qwen/Qwen2.5-Coder-3B-Instruct-AWQ
+- **Teacher Model**: Qwen/Qwen2.5-Coder-7B-Instruct-AWQ
+- **Training Method**: Knowledge Distillation with LoRA
+- **Best Validation Loss**: 1.9286
+- **Training Time**: ~5 minutes
+- **Parameters Trained**: 14.9M (4.59% of base model)
+
+## Training Configuration
+
+- **Temperature**: 2.0 (optimal)
+- **Alpha**: 0.95 (95% distillation weight)
+- **LoRA Rank**: 8
+- **Target Modules**: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
+
+## Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
+
+# Load base model and tokenizer
+base_model = AutoModelForCausalLM.from_pretrained(
+    "Qwen/Qwen2.5-Coder-3B-Instruct-AWQ",
+    torch_dtype=torch.float16,
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-Coder-3B-Instruct-AWQ")
+
+# Load distilled adapter
+model = PeftModel.from_pretrained(base_model, "Vinitha2004/qwen2.5-coder-3b-instruct-awq-gguf")
+
+# Generate code
+input_text = "Original Code:\ndef add(a, b):\n    return a + b\n\nUpdate Snippet:\n// ... existing code ...\ndef add(a: int, b: int) -> int:\n// ... existing code ...\n\nUpdated Code:\n"
+inputs = tokenizer(input_text, return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=100)
+result = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(result)
+```
+
+## Performance
+
+This distilled model retains the knowledge from the 7B teacher model while being significantly more efficient:
+- **Faster inference** (3B vs 7B parameters)
+- **Lower memory usage**
+- **Maintained code generation quality**
+
+## Training Dataset
+
+Trained on 5000 code editing examples from custom dataset.
+
+## Files
+
+- `adapter_config.json`: LoRA configuration
+- `adapter_model.safetensors`: Trained LoRA weights (59MB)
+- Other standard tokenizer files

fast_inference.py

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+#!/usr/bin/env python3
+"""
+Optimized inference script for GGUF models
+Supports llama-cpp-python for maximum speed
+"""
+
+import argparse
+import time
+from pathlib import Path
+import multiprocessing
+
+try:
+    from llama_cpp import Llama
+    LLAMA_CPP_AVAILABLE = True
+except ImportError:
+    LLAMA_CPP_AVAILABLE = False
+    print("llama-cpp-python not available.")
+    print("Install with: pip install llama-cpp-python")
+
+class FastInference:
+    """Optimized inference class for GGUF models"""
+    
+    def __init__(self, model_path: str, n_ctx: int = 4096, n_threads: int = -1):
+        self.model_path = model_path
+        
+        if not LLAMA_CPP_AVAILABLE:
+            raise ImportError("llama-cpp-python required for GGUF inference")
+        
+        # Use all CPU threads if not specified
+        if n_threads == -1:
+            n_threads = multiprocessing.cpu_count()
+        
+        # Initialize model with optimized settings
+        self.model = Llama(
+            model_path=model_path,
+            n_ctx=n_ctx,
+            n_threads=n_threads,
+            n_batch=512,  # Batch size for prompt processing
+            n_gpu_layers=-1 if self._has_gpu() else 0,  # Use GPU if available
+            use_mmap=True,  # Memory-mapped files
+            use_mlock=True,  # Lock memory
+            verbose=False
+        )
+        
+        print(f"Model loaded: {model_path}")
+        print(f"Context length: {n_ctx}")
+        print(f"Threads: {n_threads}")
+        print(f"GPU layers: {-1 if self._has_gpu() else 0}")
+    
+    def _has_gpu(self) -> bool:
+        """Check if GPU is available"""
+        try:
+            import torch
+            return torch.cuda.is_available()
+        except ImportError:
+            return False
+    
+    def generate(self, prompt: str, max_tokens: int = 512, temperature: float = 0.7) -> str:
+        """Generate text with optimized settings"""
+        
+        start_time = time.time()
+        
+        # Optimized generation parameters
+        response = self.model(
+            prompt,
+            max_tokens=max_tokens,
+            temperature=temperature,
+            top_p=0.9,
+            repeat_penalty=1.1,
+            stop=["</code>", "\n\n\n"],  # Stop sequences
+            stream=False
+        )
+        
+        generation_time = time.time() - start_time
+        generated_text = response['choices'][0]['text']
+        
+        # Calculate tokens per second
+        estimated_tokens = len(generated_text.split())
+        tokens_per_sec = estimated_tokens / generation_time if generation_time > 0 else 0
+        
+        print(f"\n📊 Performance:")
+        print(f"  Time: {generation_time:.2f}s")
+        print(f"  Speed: {tokens_per_sec:.1f} tokens/sec")
+        print(f"  Tokens: {estimated_tokens}")
+        
+        return generated_text
+    
+    def generate_stream(self, prompt: str, max_tokens: int = 512, temperature: float = 0.7):
+        """Generate text with streaming"""
+        
+        print("\n🚀 Streaming response:")
+        start_time = time.time()
+        total_tokens = 0
+        
+        stream = self.model(
+            prompt,
+            max_tokens=max_tokens,
+            temperature=temperature,
+            top_p=0.9,
+            repeat_penalty=1.1,
+            stop=["</code>", "\n\n\n"],
+            stream=True
+        )
+        
+        for chunk in stream:
+            text = chunk['choices'][0]['text']
+            print(text, end='', flush=True)
+            total_tokens += 1
+        
+        generation_time = time.time() - start_time
+        tokens_per_sec = total_tokens / generation_time if generation_time > 0 else 0
+        
+        print(f"\n\n📊 Streaming Performance:")
+        print(f"  Time: {generation_time:.2f}s")
+        print(f"  Speed: {tokens_per_sec:.1f} tokens/sec")
+    
+    def chat_mode(self):
+        """Interactive chat mode"""
+        print("\n🤖 Interactive Chat Mode")
+        print("Commands: 'exit' to quit, 'stream' to toggle streaming")
+        print("-" * 50)
+        
+        use_streaming = False
+        
+        while True:
+            try:
+                prompt = input("\n👤 You: ")
+                
+                if prompt.lower() == 'exit':
+                    print("👋 Goodbye!")
+                    break
+                elif prompt.lower() == 'stream':
+                    use_streaming = not use_streaming
+                    print(f"🔄 Streaming {'enabled' if use_streaming else 'disabled'}")
+                    continue
+                
+                print("🤖 Assistant:", end=" ")
+                
+                if use_streaming:
+                    self.generate_stream(prompt)
+                else:
+                    response = self.generate(prompt)
+                    print(response)
+                    
+            except KeyboardInterrupt:
+                print("\n\n👋 Goodbye!")
+                break
+
+def main():
+    parser = argparse.ArgumentParser(description="Fast GGUF Model Inference")
+    parser.add_argument("--model", required=True, help="Path to GGUF model file")
+    parser.add_argument("--prompt", help="Text prompt for generation")
+    parser.add_argument("--max-tokens", type=int, default=512, help="Maximum tokens to generate")
+    parser.add_argument("--temperature", type=float, default=0.7, help="Generation temperature")
+    parser.add_argument("--ctx-size", type=int, default=4096, help="Context size")
+    parser.add_argument("--threads", type=int, default=-1, help="Number of threads (-1 for auto)")
+    parser.add_argument("--interactive", action="store_true", help="Start interactive chat mode")
+    parser.add_argument("--stream", action="store_true", help="Use streaming generation")
+    
+    args = parser.parse_args()
+    
+    # Initialize inference
+    print(f"🚀 Loading model: {args.model}")
+    inferencer = FastInference(
+        args.model, 
+        n_ctx=args.ctx_size, 
+        n_threads=args.threads
+    )
+    
+    if args.interactive:
+        inferencer.chat_mode()
+    elif args.prompt:
+        if args.stream:
+            inferencer.generate_stream(args.prompt, args.max_tokens, args.temperature)
+        else:
+            response = inferencer.generate(args.prompt, args.max_tokens, args.temperature)
+            print("\n🤖 Generated text:")
+            print(response)
+    else:
+        print("Please provide --prompt or use --interactive mode")
+        print("Example: python fast_inference.py --model model.gguf --prompt 'def hello():' --interactive")
+
+if __name__ == "__main__":
+    main()

model_f16.gguf

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model_q4_0.gguf

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model_q8_0.gguf

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training_metadata.json

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+{
+  "training_completed": true,
+  "distillation_method": "knowledge_distillation",
+  "teacher_model": "Qwen/Qwen2.5-Coder-7B-Instruct-AWQ",
+  "student_model": "Qwen/Qwen2.5-Coder-3B-Instruct-AWQ",
+  "best_validation_loss": 1.9286,
+  "optimal_temperature": 2.0,
+  "optimal_alpha": 0.95,
+  "training_samples": 118,
+  "validation_samples": 23,
+  "test_samples": 100
+}