Add 1.7B GGUF conversion script

convert_1.7B_gguf.py

@@ -0,0 +1,282 @@
+#!/usr/bin/env python3
+# /// script
+# requires-python = ">=3.10"
+# 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 for QMD Query Expansion 1.7B Model
+
+Loads base model, applies SFT adapter, then GRPO adapter, merges all,
+and converts to GGUF format for use with Ollama/llama.cpp/LM Studio.
+"""
+
+import os
+import sys
+import subprocess
+
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+from huggingface_hub import HfApi, login
+
+# Configuration
+BASE_MODEL = "Qwen/Qwen3-1.7B"
+SFT_MODEL = "tobil/qmd-query-expansion-1.7B-sft"
+GRPO_MODEL = "tobil/qmd-query-expansion-1.7B-grpo"
+OUTPUT_REPO = "tobil/qmd-query-expansion-1.7B-gguf"
+
+def run_command(cmd, description):
+    """Run a command with error handling."""
+    print(f"   {description}...")
+    try:
+        result = subprocess.run(cmd, check=True, capture_output=True, text=True)
+        return True
+    except subprocess.CalledProcessError as e:
+        print(f"   ❌ Command failed: {' '.join(cmd)}")
+        if e.stderr:
+            print(f"   STDERR: {e.stderr[:500]}")
+        return False
+    except FileNotFoundError:
+        print(f"   ❌ Command not found: {cmd[0]}")
+        return False
+
+
+print("🔄 QMD Query Expansion 1.7B GGUF Conversion")
+print("=" * 60)
+
+# Install build tools
+print("\n📦 Installing build dependencies...")
+subprocess.run(["apt-get", "update", "-qq"], capture_output=True)
+subprocess.run(["apt-get", "install", "-y", "-qq", "build-essential", "cmake", "git"], capture_output=True)
+print("   ✅ Build tools ready")
+
+# Login to HuggingFace
+hf_token = os.environ.get("HF_TOKEN")
+if hf_token:
+    print("\n🔐 Logging in to HuggingFace...")
+    login(token=hf_token)
+    print("   ✅ Logged in")
+
+# Step 1: Load base model
+print(f"\n🔧 Step 1: Loading base model {BASE_MODEL}...")
+base_model = AutoModelForCausalLM.from_pretrained(
+    BASE_MODEL,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+    trust_remote_code=True,
+)
+print("   ✅ Base model loaded")
+
+# Step 2: Load and merge SFT adapter
+print(f"\n🔧 Step 2: Loading SFT adapter {SFT_MODEL}...")
+model = PeftModel.from_pretrained(base_model, SFT_MODEL)
+print("   Merging SFT adapter...")
+model = model.merge_and_unload()
+print("   ✅ SFT merged")
+
+# Step 3: Load and merge GRPO adapter
+print(f"\n🔧 Step 3: Loading GRPO adapter {GRPO_MODEL}...")
+model = PeftModel.from_pretrained(model, GRPO_MODEL)
+print("   Merging GRPO adapter...")
+merged_model = model.merge_and_unload()
+print("   ✅ GRPO merged - final model ready")
+
+# Load tokenizer
+print("\n📝 Loading tokenizer...")
+tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL, trust_remote_code=True)
+print("   ✅ Tokenizer loaded")
+
+# Step 4: Save merged model
+print("\n💾 Step 4: Saving merged model to disk...")
+merged_dir = "/tmp/merged_model"
+merged_model.save_pretrained(merged_dir, safe_serialization=True)
+tokenizer.save_pretrained(merged_dir)
+print(f"   ✅ Saved to {merged_dir}")
+
+# Step 5: Setup llama.cpp
+print("\n📥 Step 5: Setting up llama.cpp...")
+if not os.path.exists("/tmp/llama.cpp"):
+    run_command(
+        ["git", "clone", "--depth", "1", "https://github.com/ggerganov/llama.cpp.git", "/tmp/llama.cpp"],
+        "Cloning llama.cpp"
+    )
+
+# Install Python deps
+subprocess.run([sys.executable, "-m", "pip", "install", "-q", "-r", "/tmp/llama.cpp/requirements.txt"], capture_output=True)
+subprocess.run([sys.executable, "-m", "pip", "install", "-q", "sentencepiece", "protobuf"], capture_output=True)
+print("   ✅ llama.cpp ready")
+
+# Step 6: Convert to GGUF (FP16)
+print("\n🔄 Step 6: Converting to GGUF format (FP16)...")
+gguf_output_dir = "/tmp/gguf_output"
+os.makedirs(gguf_output_dir, exist_ok=True)
+
+model_name = "qmd-query-expansion-1.7B"
+gguf_file = f"{gguf_output_dir}/{model_name}-f16.gguf"
+
+convert_script = "/tmp/llama.cpp/convert_hf_to_gguf.py"
+if not run_command(
+    [sys.executable, convert_script, merged_dir, "--outfile", gguf_file, "--outtype", "f16"],
+    "Converting to FP16 GGUF"
+):
+    print("   ❌ Conversion failed!")
+    sys.exit(1)
+
+size_mb = os.path.getsize(gguf_file) / (1024 * 1024)
+print(f"   ✅ FP16 GGUF created: {size_mb:.1f} MB")
+
+# Step 7: Build quantize tool
+print("\n⚙️  Step 7: Building quantize tool...")
+os.makedirs("/tmp/llama.cpp/build", exist_ok=True)
+
+run_command(
+    ["cmake", "-B", "/tmp/llama.cpp/build", "-S", "/tmp/llama.cpp", "-DGGML_CUDA=OFF"],
+    "Configuring with CMake"
+)
+run_command(
+    ["cmake", "--build", "/tmp/llama.cpp/build", "--target", "llama-quantize", "-j", "4"],
+    "Building llama-quantize"
+)
+
+quantize_bin = "/tmp/llama.cpp/build/bin/llama-quantize"
+print("   ✅ Quantize tool built")
+
+# Step 8: Create quantized versions
+print("\n⚙️  Step 8: Creating quantized versions...")
+quant_formats = [
+    ("Q4_K_M", "4-bit medium (recommended)"),
+    ("Q5_K_M", "5-bit medium"),
+    ("Q8_0", "8-bit"),
+]
+
+quantized_files = []
+for quant_type, description in quant_formats:
+    print(f"   Creating {quant_type} ({description})...")
+    quant_file = f"{gguf_output_dir}/{model_name}-{quant_type.lower()}.gguf"
+
+    if run_command([quantize_bin, gguf_file, quant_file, quant_type], f"Quantizing to {quant_type}"):
+        size_mb = os.path.getsize(quant_file) / (1024 * 1024)
+        print(f"   ✅ {quant_type}: {size_mb:.1f} MB")
+        quantized_files.append((quant_file, quant_type))
+    else:
+        print(f"   ⚠️  Skipping {quant_type}")
+
+# Step 9: Upload to Hub
+print("\n☁️  Step 9: Uploading to Hugging Face Hub...")
+api = HfApi()
+
+print(f"   Creating repository: {OUTPUT_REPO}")
+api.create_repo(repo_id=OUTPUT_REPO, repo_type="model", exist_ok=True)
+
+# Upload F16
+print("   Uploading FP16...")
+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
+- query-expansion
+- qmd
+---
+
+# QMD Query Expansion 1.7B (GGUF)
+
+GGUF conversion of the QMD Query Expansion model for use with Ollama, llama.cpp, and LM Studio.
+
+## Model Details
+
+- **Base Model:** {BASE_MODEL}
+- **SFT Adapter:** {SFT_MODEL}
+- **GRPO Adapter:** {GRPO_MODEL}
+- **Task:** Query expansion for hybrid search (lex/vec/hyde format)
+
+## Available Quantizations
+
+| File | Quant | Description |
+|------|-------|-------------|
+| {model_name}-f16.gguf | F16 | Full precision |
+| {model_name}-q8_0.gguf | Q8_0 | 8-bit |
+| {model_name}-q5_k_m.gguf | Q5_K_M | 5-bit medium |
+| {model_name}-q4_k_m.gguf | Q4_K_M | 4-bit medium (recommended) |
+
+## Usage
+
+### With Ollama
+
+```bash
+# Download
+huggingface-cli download {OUTPUT_REPO} {model_name}-q4_k_m.gguf --local-dir .
+
+# Create Modelfile
+echo 'FROM ./{model_name}-q4_k_m.gguf' > Modelfile
+
+# Create and run
+ollama create qmd-expand -f Modelfile
+ollama run qmd-expand
+```
+
+### Prompt Format
+
+Use Qwen3 chat format with `/no_think`:
+
+```
+<|im_start|>user
+/no_think Expand this search query: your query here<|im_end|>
+<|im_start|>assistant
+```
+
+### Expected Output
+
+```
+lex: keyword variation 1
+lex: keyword variation 2
+vec: natural language reformulation
+hyde: Hypothetical document passage answering the query.
+```
+
+## License
+
+Apache 2.0 (inherited from Qwen3)
+"""
+
+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("=" * 60)