Upload convert_to_gguf_simple.py with huggingface_hub

convert_to_gguf_simple.py

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+# /// script
+# dependencies = ["torch", "transformers", "peft", "huggingface_hub", "sentencepiece", "protobuf", "gguf"]
+# ///
+
+import subprocess
+from pathlib import Path
+from huggingface_hub import HfApi, create_repo
+from peft import AutoPeftModelForCausalLM
+from transformers import AutoTokenizer
+
+# Config
+ADAPTER_REPO = "kingjux/ffmpeg-command-generator"
+OUTPUT_REPO = "kingjux/ffmpeg-command-generator-gguf"
+
+print("=" * 50)
+print("GGUF Conversion for LM Studio")
+print("=" * 50)
+
+# Step 1: Load and merge LoRA with base model
+print("\n[1/3] Loading adapter and merging with base model...")
+model = AutoPeftModelForCausalLM.from_pretrained(
+    ADAPTER_REPO,
+    device_map="auto",
+    trust_remote_code=True,
+)
+tokenizer = AutoTokenizer.from_pretrained(ADAPTER_REPO, trust_remote_code=True)
+
+print("Merging LoRA weights...")
+merged_model = model.merge_and_unload()
+
+merged_path = Path("/tmp/merged_model")
+merged_path.mkdir(exist_ok=True)
+print(f"Saving merged model to {merged_path}...")
+merged_model.save_pretrained(merged_path)
+tokenizer.save_pretrained(merged_path)
+
+# Step 2: Convert to GGUF using llama.cpp Python converter
+print("\n[2/3] Converting to GGUF...")
+llama_cpp_path = Path("/tmp/llama.cpp")
+if not llama_cpp_path.exists():
+    subprocess.run([
+        "git", "clone", "--depth", "1",
+        "https://github.com/ggerganov/llama.cpp.git",
+        str(llama_cpp_path)
+    ], check=True)
+
+# Install requirements
+subprocess.run([
+    "pip", "install", "-q", "-r",
+    str(llama_cpp_path / "requirements" / "requirements-convert_hf_to_gguf.txt")
+], check=True)
+
+gguf_output_dir = Path("/tmp/gguf_output")
+gguf_output_dir.mkdir(exist_ok=True)
+
+# Convert to F16 GGUF (no quantization needed - LM Studio handles it)
+f16_path = gguf_output_dir / "ffmpeg-command-generator-f16.gguf"
+subprocess.run([
+    "python", str(llama_cpp_path / "convert_hf_to_gguf.py"),
+    str(merged_path),
+    "--outfile", str(f16_path),
+    "--outtype", "f16"
+], check=True)
+print(f"Created: {f16_path}")
+
+# Step 3: Upload to Hub
+print("\n[3/3] Uploading to Hugging Face Hub...")
+api = HfApi()
+create_repo(OUTPUT_REPO, repo_type="model", exist_ok=True)
+
+# Model card
+model_card = """---
+license: apache-2.0
+base_model: Qwen/Qwen2.5-0.5B-Instruct
+tags:
+  - gguf
+  - ffmpeg
+  - command-generation
+  - lm-studio
+  - ollama
+---
+
+# FFMPEG Command Generator (GGUF)
+
+Fine-tuned Qwen2.5-0.5B that generates FFMPEG commands from natural language with chain-of-thought reasoning.
+
+## Quick Start
+
+### LM Studio
+```bash
+lms import kingjux/ffmpeg-command-generator-gguf
+```
+
+### Ollama
+```bash
+ollama run hf.co/kingjux/ffmpeg-command-generator-gguf
+```
+
+## Example
+
+**Input:** "Convert video.mp4 to webm format"
+
+**Output:**
+```
+<think>
+Task: Convert MP4 to WebM
+- WebM uses VP9 video + Opus audio
+- Use -c:v libvpx-vp9 for video
+- Use -c:a libopus for audio
+</think>
+
+ffmpeg -i video.mp4 -c:v libvpx-vp9 -c:a libopus output.webm
+```
+
+## Training
+- Base: Qwen2.5-0.5B-Instruct
+- Method: LoRA fine-tuning (r=16, alpha=32)
+- Dataset: 30 FFMPEG command examples with CoT reasoning
+- Trained on HuggingFace Jobs (T4 GPU)
+"""
+
+card_path = gguf_output_dir / "README.md"
+card_path.write_text(model_card)
+
+# Upload
+for file in [card_path, f16_path]:
+    print(f"Uploading {file.name}...")
+    api.upload_file(
+        path_or_fileobj=str(file),
+        path_in_repo=file.name,
+        repo_id=OUTPUT_REPO,
+        repo_type="model"
+    )
+
+print("\n" + "=" * 50)
+print("DONE!")
+print(f"Model: https://huggingface.co/{OUTPUT_REPO}")
+print(f"\nLM Studio: lms import {OUTPUT_REPO}")
+print("=" * 50)