Upload convert_to_gguf.py with huggingface_hub

convert_to_gguf.py

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+#!/usr/bin/env python3
+# /// script
+# dependencies = [
+#     "transformers>=4.36.0",
+#     "peft>=0.7.0",
+#     "torch>=2.0.0",
+#     "accelerate>=0.24.0",
+#     "huggingface_hub>=0.20.0",
+#     "sentencepiece>=0.1.99",
+#     "protobuf>=3.20.0",
+#     "numpy",
+#     "gguf",
+# ]
+# ///
+
+"""
+GGUF Conversion Script - Production Ready
+
+This script converts a LoRA fine-tuned model to GGUF format for use with:
+- llama.cpp
+- Ollama
+- LM Studio
+- Other GGUF-compatible tools
+
+Usage:
+    Set environment variables:
+    - ADAPTER_MODEL: Your fine-tuned model (e.g., "username/my-finetuned-model")
+    - BASE_MODEL: Base model used for fine-tuning (e.g., "Qwen/Qwen2.5-0.5B")
+    - OUTPUT_REPO: Where to upload GGUF files (e.g., "username/my-model-gguf")
+    - HF_USERNAME: Your Hugging Face username (optional, for README)
+
+Dependencies: All required packages are declared in PEP 723 header above.
+Build tools (gcc, cmake) are installed automatically by this script.
+"""
+
+import os
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+from huggingface_hub import HfApi
+import subprocess
+
+print("🔄 GGUF Conversion Script")
+print("=" * 60)
+
+# Configuration from environment variables
+ADAPTER_MODEL = os.environ.get("ADAPTER_MODEL", "evalstate/qwen-capybara-medium")
+BASE_MODEL = os.environ.get("BASE_MODEL", "Qwen/Qwen2.5-0.5B")
+OUTPUT_REPO = os.environ.get("OUTPUT_REPO", "evalstate/qwen-capybara-medium-gguf")
+username = os.environ.get("HF_USERNAME", ADAPTER_MODEL.split('/')[0])
+
+print(f"\n📦 Configuration:")
+print(f"   Base model: {BASE_MODEL}")
+print(f"   Adapter model: {ADAPTER_MODEL}")
+print(f"   Output repo: {OUTPUT_REPO}")
+
+# Step 1: Load base model and adapter
+print("\n🔧 Step 1: Loading base model and LoRA adapter...")
+print("   (This may take a few minutes)")
+
+base_model = AutoModelForCausalLM.from_pretrained(
+    BASE_MODEL,
+    dtype=torch.float16,
+    device_map="auto",
+    trust_remote_code=True,
+)
+print("   ✅ Base model loaded")
+
+# Load and merge adapter
+print("   Loading LoRA adapter...")
+model = PeftModel.from_pretrained(base_model, ADAPTER_MODEL)
+print("   ✅ Adapter loaded")
+
+print("   Merging adapter with base model...")
+merged_model = model.merge_and_unload()
+print("   ✅ Models merged!")
+
+# Load tokenizer
+tokenizer = AutoTokenizer.from_pretrained(ADAPTER_MODEL, trust_remote_code=True)
+print("   ✅ Tokenizer loaded")
+
+# Step 2: Save merged model temporarily
+print("\n💾 Step 2: Saving merged model...")
+merged_dir = "/tmp/merged_model"
+merged_model.save_pretrained(merged_dir, safe_serialization=True)
+tokenizer.save_pretrained(merged_dir)
+print(f"   ✅ Merged model saved to {merged_dir}")
+
+# Step 3: Install llama.cpp for conversion
+print("\n📥 Step 3: Setting up llama.cpp for GGUF conversion...")
+
+# CRITICAL: Install build tools FIRST (before cloning llama.cpp)
+print("   Installing build tools...")
+subprocess.run(
+    ["apt-get", "update", "-qq"],
+    check=True,
+    capture_output=True
+)
+subprocess.run(
+    ["apt-get", "install", "-y", "-qq", "build-essential", "cmake"],
+    check=True,
+    capture_output=True
+)
+print("   ✅ Build tools installed")
+
+print("   Cloning llama.cpp repository...")
+subprocess.run(
+    ["git", "clone", "https://github.com/ggerganov/llama.cpp.git", "/tmp/llama.cpp"],
+    check=True,
+    capture_output=True
+)
+print("   ✅ llama.cpp cloned")
+
+print("   Installing Python dependencies...")
+subprocess.run(
+    ["pip", "install", "-r", "/tmp/llama.cpp/requirements.txt"],
+    check=True,
+    capture_output=True
+)
+# sentencepiece and protobuf are needed for tokenizer conversion
+subprocess.run(
+    ["pip", "install", "sentencepiece", "protobuf"],
+    check=True,
+    capture_output=True
+)
+print("   ✅ Dependencies installed")
+
+# Step 4: Convert to GGUF (FP16)
+print("\n🔄 Step 4: Converting to GGUF format (FP16)...")
+gguf_output_dir = "/tmp/gguf_output"
+os.makedirs(gguf_output_dir, exist_ok=True)
+
+convert_script = "/tmp/llama.cpp/convert_hf_to_gguf.py"
+model_name = ADAPTER_MODEL.split('/')[-1]
+gguf_file = f"{gguf_output_dir}/{model_name}-f16.gguf"
+
+print(f"   Running: python {convert_script} {merged_dir}")
+try:
+    result = subprocess.run(
+        [
+            "python", convert_script,
+            merged_dir,
+            "--outfile", gguf_file,
+            "--outtype", "f16"
+        ],
+        check=True,
+        capture_output=True,
+        text=True
+    )
+    print(result.stdout)
+    if result.stderr:
+        print("Warnings:", result.stderr)
+except subprocess.CalledProcessError as e:
+    print(f"❌ Conversion failed!")
+    print("STDOUT:", e.stdout)
+    print("STDERR:", e.stderr)
+    raise
+print(f"   ✅ FP16 GGUF created: {gguf_file}")
+
+# Step 5: Quantize to different formats
+print("\n⚙️  Step 5: Creating quantized versions...")
+
+# Build quantize tool using CMake (more reliable than make)
+print("   Building quantize tool with CMake...")
+try:
+    # Create build directory
+    os.makedirs("/tmp/llama.cpp/build", exist_ok=True)
+
+    # Configure with CMake
+    subprocess.run(
+        ["cmake", "-B", "/tmp/llama.cpp/build", "-S", "/tmp/llama.cpp",
+         "-DGGML_CUDA=OFF"],  # Disable CUDA for faster build
+        check=True,
+        capture_output=True,
+        text=True
+    )
+
+    # Build just the quantize tool
+    subprocess.run(
+        ["cmake", "--build", "/tmp/llama.cpp/build", "--target", "llama-quantize", "-j", "4"],
+        check=True,
+        capture_output=True,
+        text=True
+    )
+    print("   ✅ Quantize tool built")
+except subprocess.CalledProcessError as e:
+    print(f"   ❌ Build failed!")
+    print("STDOUT:", e.stdout)
+    print("STDERR:", e.stderr)
+    raise
+
+# Use the CMake build output path
+quantize_bin = "/tmp/llama.cpp/build/bin/llama-quantize"
+
+# Common quantization formats
+quant_formats = [
+    ("Q4_K_M", "4-bit, medium quality (recommended)"),
+    ("Q5_K_M", "5-bit, higher quality"),
+    ("Q8_0", "8-bit, very high quality"),
+]
+
+quantized_files = []
+for quant_type, description in quant_formats:
+    print(f"   Creating {quant_type} quantization ({description})...")
+    quant_file = f"{gguf_output_dir}/{model_name}-{quant_type.lower()}.gguf"
+
+    subprocess.run(
+        [quantize_bin, gguf_file, quant_file, quant_type],
+        check=True,
+        capture_output=True
+    )
+    quantized_files.append((quant_file, quant_type))
+
+    # Get file size
+    size_mb = os.path.getsize(quant_file) / (1024 * 1024)
+    print(f"   ✅ {quant_type}: {size_mb:.1f} MB")
+
+# Step 6: Upload to Hub
+print("\n☁️  Step 6: Uploading to Hugging Face Hub...")
+api = HfApi()
+
+# Create repo
+print(f"   Creating repository: {OUTPUT_REPO}")
+try:
+    api.create_repo(repo_id=OUTPUT_REPO, repo_type="model", exist_ok=True)
+    print("   ✅ Repository created")
+except Exception as e:
+    print(f"   ℹ️  Repository may already exist: {e}")
+
+# Upload FP16 version
+print("   Uploading FP16 GGUF...")
+api.upload_file(
+    path_or_fileobj=gguf_file,
+    path_in_repo=f"{model_name}-f16.gguf",
+    repo_id=OUTPUT_REPO,
+)
+print("   ✅ FP16 uploaded")
+
+# Upload quantized versions
+for quant_file, quant_type in quantized_files:
+    print(f"   Uploading {quant_type}...")
+    api.upload_file(
+        path_or_fileobj=quant_file,
+        path_in_repo=f"{model_name}-{quant_type.lower()}.gguf",
+        repo_id=OUTPUT_REPO,
+    )
+    print(f"   ✅ {quant_type} uploaded")
+
+# Create README
+print("\n📝 Creating README...")
+readme_content = f"""---
+base_model: {BASE_MODEL}
+tags:
+- gguf
+- llama.cpp
+- quantized
+- trl
+- sft
+---
+
+# {OUTPUT_REPO.split('/')[-1]}
+
+This is a GGUF conversion of [{ADAPTER_MODEL}](https://huggingface.co/{ADAPTER_MODEL}), which is a LoRA fine-tuned version of [{BASE_MODEL}](https://huggingface.co/{BASE_MODEL}).
+
+## Model Details
+
+- **Base Model:** {BASE_MODEL}
+- **Fine-tuned Model:** {ADAPTER_MODEL}
+- **Training:** Supervised Fine-Tuning (SFT) with TRL
+- **Format:** GGUF (for llama.cpp, Ollama, LM Studio, etc.)
+
+## Available Quantizations
+
+| File | Quant | Size | Description | Use Case |
+|------|-------|------|-------------|----------|
+| {model_name}-f16.gguf | F16 | ~1GB | Full precision | Best quality, slower |
+| {model_name}-q8_0.gguf | Q8_0 | ~500MB | 8-bit | High quality |
+| {model_name}-q5_k_m.gguf | Q5_K_M | ~350MB | 5-bit medium | Good quality, smaller |
+| {model_name}-q4_k_m.gguf | Q4_K_M | ~300MB | 4-bit medium | Recommended - good balance |
+
+## Usage
+
+### With llama.cpp
+
+```bash
+# Download model
+huggingface-cli download {OUTPUT_REPO} {model_name}-q4_k_m.gguf
+
+# Run with llama.cpp
+./llama-cli -m {model_name}-q4_k_m.gguf -p "Your prompt here"
+```
+
+### With Ollama
+
+1. Create a `Modelfile`:
+```
+FROM ./{model_name}-q4_k_m.gguf
+```
+
+2. Create the model:
+```bash
+ollama create my-model -f Modelfile
+ollama run my-model
+```
+
+### With LM Studio
+
+1. Download the `.gguf` file
+2. Import into LM Studio
+3. Start chatting!
+
+## License
+
+Inherits the license from the base model: {BASE_MODEL}
+
+## Citation
+
+```bibtex
+@misc{{{OUTPUT_REPO.split('/')[-1].replace('-', '_')},
+  author = {{{username}}},
+  title = {{{OUTPUT_REPO.split('/')[-1]}}},
+  year = {{2025}},
+  publisher = {{Hugging Face}},
+  url = {{https://huggingface.co/{OUTPUT_REPO}}}
+}}
+```
+
+---
+
+*Converted to GGUF format using llama.cpp*
+"""
+
+api.upload_file(
+    path_or_fileobj=readme_content.encode(),
+    path_in_repo="README.md",
+    repo_id=OUTPUT_REPO,
+)
+print("   ✅ README uploaded")
+
+print("\n" + "=" * 60)
+print("✅ GGUF Conversion Complete!")
+print(f"📦 Repository: https://huggingface.co/{OUTPUT_REPO}")
+print(f"\n📥 Download with:")
+print(f"   huggingface-cli download {OUTPUT_REPO} {model_name}-q4_k_m.gguf")
+print(f"\n🚀 Use with Ollama:")
+print("   1. Download the GGUF file")
+print(f"   2. Create Modelfile: FROM ./{model_name}-q4_k_m.gguf")
+print("   3. ollama create my-model -f Modelfile")
+print("   4. ollama run my-model")
+print("=" * 60)