Setup repo with Phi 3

tools/generate_model_yaml.py

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+from huggingface_hub import HfApi, HfFileSystem
+from pathlib import Path
+import yaml
+import requests
+import os
+from datetime import datetime
+from collections import defaultdict
+import re
+import sys
+
+
+def generate_model_bundle(repo_id: str, output_dir: str):
+    api = HfApi()
+    fs = HfFileSystem()
+    model_info = api.model_info(repo_id)
+
+    # Create output path
+    out_path = Path(output_dir)
+    out_path.mkdir(parents=True, exist_ok=True)
+
+    # ----- 1. Fetch metadata -----
+    model_card = model_info.cardData or {}
+    tags = model_info.tags or []
+    files = api.list_repo_files(repo_id)
+
+    # ----- 2. Filter files -----
+    model_files = [f for f in files if f.endswith(".gguf") or f.endswith(".safetensors")]
+    tokenizer_files = [f for f in files if "tokenizer" in f.lower()]
+    license_file = next((f for f in files if "license" in f.lower()), None)
+
+    # ----- 3. Fetch README -----
+    readme_url = f"https://huggingface.co/{repo_id}/raw/main/README.md"
+    readme_path = out_path / "README.md"
+    try:
+        r = requests.get(readme_url)
+        r.raise_for_status()
+        readme_path.write_text(r.text)
+    except Exception:
+        readme_path.write_text(f"# README for {repo_id}\n(Not found on HuggingFace)")
+
+    # ----- 4. Fetch LICENSE -----
+    if license_file:
+        license_text = api.hf_hub_download(repo_id, license_file)
+        license_dst = out_path / Path(license_file).name
+        Path(license_dst).write_text(Path(license_text).read_text())
+
+    # ----- 5. Build variant groups -----
+    variants = []
+    shard_groups = defaultdict(list)
+    unsharded_files = []
+
+    for f in model_files:
+        match = re.match(r"(.+)-\d+-of-\d+\.safetensors$", f)
+        if match:
+            prefix = match.group(1)
+            shard_groups[prefix].append(f)
+        else:
+            unsharded_files.append(f)
+
+    for prefix, files_group in shard_groups.items():
+        total_size = sum(fs.info(f"hf://{repo_id}/{f}").get("size", 0) for f in files_group)
+        context_length = 128000 if "128k" in prefix.lower() else 4096
+        bits = 16  # Assume safetensors shards are FP16
+
+        variants.append({
+            "id": prefix,
+            "label": prefix,
+            "bits": bits,
+            "context_length": context_length,
+            "size_bytes": total_size,
+            "hf_repo": f"https://huggingface.co/{repo_id}",
+            "files": sorted(files_group)
+        })
+
+    for f in unsharded_files:
+        ext = Path(f).suffix
+        size_bytes = fs.info(f"hf://{repo_id}/{f}").get("size", 0)
+        bits = 16 if "fp16" in f.lower() or ext == ".safetensors" else 4 if "q4" in f.lower() else 8
+        context_length = 128000 if "128k" in f.lower() else 4096
+
+        variants.append({
+            "id": Path(f).stem,
+            "label": f,
+            "bits": bits,
+            "context_length": context_length,
+            "size_bytes": size_bytes,
+            "hf_repo": f"https://huggingface.co/{repo_id}",
+            "files": [f]
+        })
+
+    # ----- 6. Handle date -----
+    last_modified = model_info.lastModified
+    if isinstance(last_modified, str):
+        last_modified = datetime.fromisoformat(last_modified.replace("Z", "+00:00"))
+
+    # ----- 7. YAML data -----
+    yaml_data = {
+        "model": {
+            "name": repo_id.split("/")[-1],
+            "display_name": model_card.get("title", repo_id),
+            "description": model_card.get("summary", "No description available."),
+            "publisher_original": model_card.get("license", "other"),
+            "publisher_quantized": "Community",
+            "license": model_card.get("license", "other"),
+            "license_url": f"https://huggingface.co/{repo_id}/blob/main/{license_file}" if license_file else "N/A",
+            "publish_date": last_modified.date().isoformat(),
+            "modality": "text",
+            "thinking_model": True,
+            "tokenizer": {"files": tokenizer_files},
+            "architecture": model_card.get("model_architecture", "transformer"),
+            "formats": [{
+                "type": "gguf" if any(f.endswith(".gguf") for f in model_files) else "safetensors",
+                "variants": variants
+            }]
+        }
+    }
+
+    with open(out_path / "model.yaml", "w") as f:
+        yaml.dump(yaml_data, f, sort_keys=False)
+
+    return str(out_path)
+
+
+# -------- Entry point for CLI --------
+if __name__ == "__main__":
+    if len(sys.argv) != 3:
+        print("Usage: python generate_model_yaml.py <huggingface/repo-id> <output-folder>")
+        sys.exit(1)
+
+    repo_id = sys.argv[1]
+    output_dir = sys.argv[2]
+
+    output_path = generate_model_bundle(repo_id, output_dir)
+    print(f"✅ Model bundle generated at: {output_path}")