Create convert_phi35_complete.sh

convert_phi35_complete.sh

@@ -0,0 +1,211 @@
+#!/bin/bash
+set -e
+
+echo "🚀 COMPREHENSIVE PHI-3.5-MINI CONVERSION & QUANTIZATION SYSTEM"
+echo "================================================================"
+
+# Configuration
+MODEL_NAME="microsoft/Phi-3.5-mini-instruct"
+OUTPUT_DIR="./phi3.5-mini-gguf-complete"
+QUANT_TYPES=("Q2_K" "Q3_K_S" "Q3_K_M" "Q3_K_L" "Q4_0" "Q4_1" "Q4_K_S" "Q4_K_M" "Q5_0" "Q5_1" "Q5_K_S" "Q5_K_M" "Q6_K" "Q8_0" "F16" "F32")
+
+# Create directories
+mkdir -p $OUTPUT_DIR
+mkdir -p $OUTPUT_DIR/artifacts
+mkdir -p $OUTPUT_DIR/logs
+
+# Install system dependencies
+echo "📦 INSTALLING SYSTEM DEPENDENCIES"
+sudo apt-get update
+sudo apt-get install -y \
+    build-essential \
+    cmake \
+    git \
+    python3 \
+    python3-pip \
+    python3-venv \
+    libcurl4-openssl-dev \
+    git-lfs \
+    wget \
+    curl \
+    pkg-config \
+    libopenblas-dev \
+    libatomic-dev
+
+# Initialize Git LFS
+git lfs install
+
+# Create Python virtual environment
+python3 -m venv phi3-convert
+source phi3-convert/bin/activate
+
+# Install Python dependencies
+pip install --upgrade pip
+pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
+pip install transformers>=4.43.0
+pip install safetensors
+pip install huggingface-hub
+pip install accelerate
+pip install protobuf
+pip install sentencepiece
+pip install ninja
+pip install requests
+pip install tqdm
+pip install psutil
+pip install GPUtil
+
+# Clone and build llama.cpp with all optimizations
+echo "🔨 BUILDING LLAMA.CPP WITH FULL OPTIMIZATIONS"
+if [ ! -d "llama.cpp" ]; then
+    git clone https://github.com/ggerganov/llama.cpp
+fi
+
+cd llama.cpp
+
+# Clean build directory
+rm -rf build
+mkdir -p build
+cd build
+
+# Configure with maximum optimizations
+cmake .. \
+    -DCMAKE_BUILD_TYPE=Release \
+    -DLLAMA_NATIVE=ON \
+    -DLLAMA_CURL=ON \
+    -DLLAMA_OPENBLAS=ON \
+    -DLLAMA_BLAS=ON \
+    -DBLAS_LIBRARIES=/usr/lib/openblas-base/libopenblas.a \
+    -DLLAMA_CUDA=OFF \
+    -DLLAMA_METAL=OFF \
+    -DLLAMA_MPI=OFF \
+    -DLLAMA_CCACHE=ON \
+    -DLLAMA_BUILD_TESTS=ON \
+    -DLLAMA_BUILD_EXAMPLES=ON
+
+# Build with maximum parallelism
+make -j$(nproc)
+
+# Return to main directory
+cd ../..
+
+# Download the model
+echo "📥 DOWNLOADING PHI-3.5-MINI-INSTRUCT MODEL"
+if [ ! -d "Phi-3.5-mini-instruct" ]; then
+    git clone https://huggingface.co/microsoft/Phi-3.5-mini-instruct
+fi
+
+# Verify model files
+echo "🔍 VERIFYING MODEL FILES"
+cd Phi-3.5-mini-instruct
+ls -la
+cd ..
+
+# Create comprehensive conversion script
+cat > complete_conversion.py << 'EOF'
+import os
+import sys
+import subprocess
+import json
+import time
+from datetime import datetime
+
+class Phi3Converter:
+    def __init__(self, model_path, output_dir):
+        self.model_path = model_path
+        self.output_dir = output_dir
+        self.quant_types = ["Q2_K", "Q3_K_S", "Q3_K_M", "Q3_K_L", "Q4_0", "Q4_1", 
+                           "Q4_K_S", "Q4_K_M", "Q5_0", "Q5_1", "Q5_K_S", "Q5_K_M", 
+                           "Q6_K", "Q8_0", "F16", "F32"]
+        
+    def run_command(self, cmd, description):
+        print(f"🔄 {description}")
+        print(f"   Command: {cmd}")
+        
+        start_time = time.time()
+        result = subprocess.run(cmd, shell=True, capture_output=True, text=True)
+        end_time = time.time()
+        
+        if result.returncode != 0:
+            print(f"❌ Error: {result.stderr}")
+            return False
+        
+        print(f"✅ Completed in {end_time - start_time:.2f}s")
+        return True
+    
+    def convert_to_gguf(self, outtype="f16"):
+        output_file = f"{self.output_dir}/phi3.5-mini.{outtype}.gguf"
+        cmd = f"python3 llama.cpp/convert-hf-to-gguf.py {self.model_path} --outfile {output_file} --outtype {outtype}"
+        
+        return self.run_command(cmd, f"Converting to GGUF ({outtype})")
+    
+    def quantize_model(self, input_file, quant_type):
+        output_file = f"{self.output_dir}/phi3.5-mini.{quant_type}.gguf"
+        cmd = f"./llama.cpp/build/bin/quantize {input_file} {output_file} {quant_type}"
+        
+        return self.run_command(cmd, f"Quantizing to {quant_type}")
+    
+    def generate_model_card(self):
+        model_card = {
+            "model_name": "Phi-3.5-mini-instruct-GGUF-Comprehensive",
+            "base_model": "microsoft/Phi-3.5-mini-instruct",
+            "description": "Comprehensive GGUF quantization of Phi-3.5-mini-instruct for Saem's Tunes",
+            "quantizations": self.quant_types,
+            "conversion_date": datetime.now().isoformat(),
+            "file_sizes": {},
+            "recommended_use_cases": {
+                "Q2_K": "Mobile devices, extremely low memory",
+                "Q3_K_S": "Mobile devices, low memory",
+                "Q4_0": "Good balance for most use cases",
+                "Q4_K_M": "Recommended balance (quality/speed)",
+                "Q5_K_M": "High quality with reasonable size",
+                "Q8_0": "Maximum quality, larger size",
+                "F16": "Original quality, large size"
+            }
+        }
+        
+        with open(f"{self.output_dir}/model_card.json", "w") as f:
+            json.dump(model_card, f, indent=2)
+    
+    def run_comprehensive_conversion(self):
+        print("🚀 STARTING COMPREHENSIVE CONVERSION PIPELINE")
+        
+        # Convert to F16 first
+        if not self.convert_to_gguf("f16"):
+            return False
+        
+        base_model = f"{self.output_dir}/phi3.5-mini.f16.gguf"
+        
+        # Create all quantization variants
+        for quant_type in self.quant_types:
+            if quant_type not in ["F16", "F32"]:
+                self.quantize_model(base_model, quant_type)
+        
+        # Convert to F32 separately
+        self.convert_to_gguf("f32")
+        
+        # Generate model card
+        self.generate_model_card()
+        
+        print("🎉 COMPREHENSIVE CONVERSION COMPLETED!")
+        return True
+
+if __name__ == "__main__":
+    converter = Phi3Converter("Phi-3.5-mini-instruct", "phi3.5-mini-gguf-complete")
+    converter.run_comprehensive_conversion()
+EOF
+
+# Run the comprehensive conversion
+echo "🏃 STARTING COMPREHENSIVE CONVERSION PROCESS"
+python3 complete_conversion.py
+
+# Generate file manifest
+echo "📊 GENERATING FILE MANIFEST"
+ls -la phi3.5-mini-gguf-complete/ > phi3.5-mini-gguf-complete/file_manifest.txt
+
+# Create deployment package
+echo "📦 CREATING DEPLOYMENT PACKAGE"
+tar -czvf phi3.5-mini-complete-gguf-$(date +%Y%m%d).tar.gz phi3.5-mini-gguf-complete/
+
+echo "🎉 COMPLETE CONVERSION SYSTEM FINISHED!"
+echo "📍 Output directory: phi3.5-mini-gguf-complete/"
+echo "📁 Total files generated: $(ls -1 phi3.5-mini-gguf-complete/*.gguf | wc -l)"