Upload convert_grpo_gguf.py with huggingface_hub

convert_grpo_gguf.py

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+# /// 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 for GRPO model (two-step adapter merge)"""
+
+import os
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+from huggingface_hub import HfApi
+import subprocess
+
+print("🔄 GGUF Conversion Script - GRPO Model")
+print("=" * 60)
+
+BASE_MODEL = os.environ.get("BASE_MODEL", "Qwen/Qwen3-0.6B")
+SFT_ADAPTER = os.environ.get("SFT_ADAPTER", "chaddy81/qwen3-0.6b-multicode-sft")
+GRPO_ADAPTER = os.environ.get("GRPO_ADAPTER", "chaddy81/qwen3-0.6b-multicode-grpo")
+OUTPUT_REPO = os.environ.get("OUTPUT_REPO", "chaddy81/qwen3-0.6b-multicode-grpo-gguf")
+QUANT_TYPE = os.environ.get("QUANT_TYPE", "Q8_0")
+
+print(f"\n📦 Configuration:")
+print(f"   Base model: {BASE_MODEL}")
+print(f"   SFT adapter: {SFT_ADAPTER}")
+print(f"   GRPO adapter: {GRPO_ADAPTER}")
+print(f"   Output repo: {OUTPUT_REPO}")
+print(f"   Quantization: {QUANT_TYPE}")
+
+# Step 1: Load base model
+print("\n🔧 Step 1: Loading base model...")
+base_model = AutoModelForCausalLM.from_pretrained(
+    BASE_MODEL,
+    dtype=torch.float16,
+    device_map="auto",
+    trust_remote_code=True,
+)
+print("   ✅ Base model loaded")
+
+# Step 2: Apply and merge SFT adapter
+print("\n🔧 Step 2: Applying SFT adapter...")
+sft_model = PeftModel.from_pretrained(base_model, SFT_ADAPTER)
+merged_sft = sft_model.merge_and_unload()
+print("   ✅ SFT adapter merged")
+
+# Step 3: Apply and merge GRPO adapter
+print("\n🔧 Step 3: Applying GRPO adapter...")
+grpo_model = PeftModel.from_pretrained(merged_sft, GRPO_ADAPTER)
+final_model = grpo_model.merge_and_unload()
+print("   ✅ GRPO adapter merged")
+
+# Load tokenizer from GRPO adapter (has latest config)
+tokenizer = AutoTokenizer.from_pretrained(GRPO_ADAPTER, trust_remote_code=True)
+print("   ✅ Tokenizer loaded")
+
+# Step 4: Save merged model
+print("\n💾 Step 4: Saving merged model...")
+merged_dir = "/tmp/merged_model"
+final_model.save_pretrained(merged_dir, safe_serialization=True)
+tokenizer.save_pretrained(merged_dir)
+print("   ✅ Merged model saved")
+
+# Step 5: Setup llama.cpp
+print("\n📥 Step 5: Setting up llama.cpp...")
+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")
+
+subprocess.run(["git", "clone", "--depth", "1", "https://github.com/ggerganov/llama.cpp.git", "/tmp/llama.cpp"], check=True, capture_output=True)
+print("   ✅ llama.cpp cloned")
+
+subprocess.run(["pip", "install", "-q", "-r", "/tmp/llama.cpp/requirements.txt"], check=True, capture_output=True)
+subprocess.run(["pip", "install", "-q", "sentencepiece", "protobuf"], check=True, capture_output=True)
+print("   ✅ Dependencies installed")
+
+# Step 6: Convert to GGUF
+print("\n🔄 Step 6: Converting to GGUF format...")
+gguf_output_dir = "/tmp/gguf_output"
+os.makedirs(gguf_output_dir, exist_ok=True)
+model_name = GRPO_ADAPTER.split('/')[-1]
+gguf_fp16 = f"{gguf_output_dir}/{model_name}-f16.gguf"
+
+try:
+    result = subprocess.run(
+        ["python", "/tmp/llama.cpp/convert_hf_to_gguf.py", merged_dir, "--outfile", gguf_fp16, "--outtype", "f16"],
+        check=True, capture_output=True, text=True
+    )
+    print("   ✅ FP16 GGUF created")
+except subprocess.CalledProcessError as e:
+    print(f"❌ Conversion failed! STDERR: {e.stderr[-2000:]}")
+    raise
+
+# Step 7: Quantize
+print(f"\n⚙️  Step 7: Creating {QUANT_TYPE} quantization...")
+os.makedirs("/tmp/llama.cpp/build", exist_ok=True)
+subprocess.run(["cmake", "-B", "/tmp/llama.cpp/build", "-S", "/tmp/llama.cpp", "-DGGML_CUDA=OFF"], check=True, capture_output=True, text=True)
+subprocess.run(["cmake", "--build", "/tmp/llama.cpp/build", "--target", "llama-quantize", "-j", "4"], check=True, capture_output=True, text=True)
+print("   ✅ Quantize tool built")
+
+quantize_bin = "/tmp/llama.cpp/build/bin/llama-quantize"
+gguf_quant = f"{gguf_output_dir}/{model_name}-{QUANT_TYPE.lower()}.gguf"
+subprocess.run([quantize_bin, gguf_fp16, gguf_quant, QUANT_TYPE], check=True, capture_output=True)
+size_mb = os.path.getsize(gguf_quant) / (1024 * 1024)
+print(f"   ✅ {QUANT_TYPE}: {size_mb:.1f} MB")
+
+# Step 8: Upload to Hub
+print("\n☁️  Step 8: Uploading to Hub...")
+api = HfApi()
+api.create_repo(repo_id=OUTPUT_REPO, repo_type="model", exist_ok=True)
+print("   ✅ Repository ready")
+
+api.upload_file(path_or_fileobj=gguf_quant, path_in_repo=f"{model_name}-{QUANT_TYPE.lower()}.gguf", repo_id=OUTPUT_REPO)
+print(f"   ✅ {QUANT_TYPE} uploaded")
+
+# Create README
+readme = f"""---
+base_model: {BASE_MODEL}
+tags:
+- gguf
+- llama.cpp
+- quantized
+- trl
+- grpo
+---
+
+# {OUTPUT_REPO.split('/')[-1]}
+
+GGUF conversion of [{GRPO_ADAPTER}](https://huggingface.co/{GRPO_ADAPTER}).
+
+**Training Pipeline:**
+1. Base: [{BASE_MODEL}](https://huggingface.co/{BASE_MODEL})
+2. SFT: [{SFT_ADAPTER}](https://huggingface.co/{SFT_ADAPTER})
+3. GRPO: [{GRPO_ADAPTER}](https://huggingface.co/{GRPO_ADAPTER})
+
+## Available Files
+
+| File | Quant | Size |
+|------|-------|------|
+| {model_name}-{QUANT_TYPE.lower()}.gguf | {QUANT_TYPE} | {size_mb:.1f} MB |
+
+## Usage
+
+### With Ollama
+```bash
+huggingface-cli download {OUTPUT_REPO} {model_name}-{QUANT_TYPE.lower()}.gguf
+echo "FROM ./{model_name}-{QUANT_TYPE.lower()}.gguf" > Modelfile
+ollama create {model_name} -f Modelfile
+ollama run {model_name}
+```
+
+### With llama.cpp
+```bash
+./llama-cli -m {model_name}-{QUANT_TYPE.lower()}.gguf -p "Your prompt"
+```
+"""
+api.upload_file(path_or_fileobj=readme.encode(), path_in_repo="README.md", repo_id=OUTPUT_REPO)
+print("   ✅ README uploaded")
+
+print("\n" + "=" * 60)
+print("✅ GGUF Conversion Complete!")
+print(f"📦 https://huggingface.co/{OUTPUT_REPO}")
+print(f"📥 huggingface-cli download {OUTPUT_REPO} {model_name}-{QUANT_TYPE.lower()}.gguf")
+print("=" * 60)