Declare torch (and transformers for GGUF) so uv run works

quantise_securecoder.py

@@ -1,189 +1,191 @@
-# /// script
-# requires-python = ">=3.10"
-# dependencies = [
-#     "huggingface_hub",
-#     "llama-cpp-python",
-# ]
-# ///
-
-
-
-"""Download the merged SecureCoder safetensors repo, convert to GGUF via
-llama.cpp's convert_hf_to_gguf.py, and quantise to Q4_K_M. Runs on
-cpu-performance (no GPU needed for the conversion path).
-
-Inputs:
-    --merged-repo Taimwe/securecoder-30b-pro-merged   (default)
-    --gguf-repo   Taimwe/securecoder-30b-pro-GGUF    (default)
-    --quant       Q4_K_M                              (default)
-
-This deliberately uses the standard llama.cpp converter (rather than Unsloth's
-quantise_gguf_model) so the GGUF can be loaded by llama.cpp, Ollama, LM Studio,
-Jan, and text-generation-webui without Unsloth's patched kernels.
-"""
-
-from __future__ import annotations
-
-import argparse
-import json
-import logging
-import os
-import subprocess
-import sys
-import time
-from pathlib import Path
-
-logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s")
-log = logging.getLogger("quantise")
-
-
-def parse_args() -> argparse.Namespace:
-    p = argparse.ArgumentParser(description="Convert merged safetensors -> GGUF")
-    p.add_argument("--merged-repo", default="Taimwe/securecoder-30b-pro-merged")
-    p.add_argument("--gguf-repo", default="Taimwe/securecoder-30b-pro-GGUF")
-    p.add_argument("--quant", default="Q4_K_M")
-    p.add_argument("--private", action="store_true")
-    p.add_argument("--work-dir", default="/data/securecoder-gguf")
-    return p.parse_args()
-
-
-def main() -> int:
-    args = parse_args()
-    token = os.environ.get("HF_TOKEN")
-    if not token:
-        log.error("HF_TOKEN not set")
-        return 1
-
-    from huggingface_hub import HfApi, snapshot_download
-
-    work = Path(args.work_dir)
-    work.mkdir(parents=True, exist_ok=True)
-
-    log.info("downloading %s ...", args.merged_repo)
-    started = time.time()
-    src = snapshot_download(
-        args.merged_repo,
-        local_dir=work / "merged",
-        token=token,
-        allow_patterns=["*.json", "*.txt", "*.safetensors", "*.tiktoken", "*.jinja"],
-    )
-    log.info("downloaded %s in %.1f min", src, (time.time() - started) / 60)
-
-    src_path = Path(src)
-
-    # Pull llama.cpp's converter + quantiser. We need cmake + make to build
-    # llama-quantize; the container does not ship them, so apt-install them
-    # up front.
-    log.info("ensuring cmake / make are present ...")
-    try:
-        subprocess.run(["cmake", "--version"], check=True, stdout=subprocess.DEVNULL)
-    except Exception:  # noqa: BLE001
-        log.info("installing cmake via apt ...")
-        subprocess.run(["apt-get", "update", "-qq"], check=True)
-        subprocess.run(["apt-get", "install", "-y", "-qq", "cmake", "build-essential"], check=True)
-
-    log.info("fetching llama.cpp ...")
-    llama_dir = work / "llama.cpp"
-    subprocess.run([
-        "git", "clone", "--depth=1", "--branch", "master",
-        "https://github.com/ggml-org/llama.cpp", str(llama_dir),
-    ], check=True)
-    subprocess.run(["pip", "install", "-q", "-r", str(llama_dir / "requirements" / "requirements-convert_hf_to_gguf.txt")],
-                   check=True)
-
-    f16_dir = work / "gguf-f16"
-    f16_dir.mkdir(exist_ok=True)
-    log.info("converting safetensors -> GGUF F16 ...")
-    subprocess.run([
-        sys.executable, str(llama_dir / "convert_hf_to_gguf.py"),
-        str(src_path),
-        "--outfile", str(f16_dir / "model.gguf"),
-        "--outtype", "f16",
-    ], check=True)
-
-    log.info("quantising F16 -> %s ...", args.quant)
-    qbin = llama_dir / "llama-quantize"  # build only if present
-    if not qbin.exists() or not os.access(qbin, os.X_OK):
-        log.info("building llama-quantize binary ...")
-        subprocess.run([
-            "cmake", "-S", str(llama_dir), "-B", str(llama_dir / "build"),
-            "-DBUILD_SHARED_LIBS=OFF",
-        ], check=True)
-        subprocess.run([
-            "cmake", "--build", str(llama_dir / "build"), "--target", "llama-quantize", "--config", "Release",
-        ], check=True)
-        # Find the produced binary
-        candidates = [
-            llama_dir / "build" / "bin" / "llama-quantize",
-            llama_dir / "build" / "llama-quantize",
-            llama_dir / "llama-quantize",
-        ]
-        qbin = next((p for p in candidates if p.exists()), None)
-        if qbin is None:
-            log.error("could not find llama-quantize after build")
-            return 1
-
-    out_dir = work / "gguf-out"
-    out_dir.mkdir(exist_ok=True)
-    subprocess.run([str(qbin), str(f16_dir / "model.gguf"),
-                    str(out_dir / f"model-{args.quant}.gguf"), args.quant], check=True)
-
-    # Write a README so the GGUF repo isn't an empty shell
-    readme = f"""---
-base_model: Taimwe/securecoder-30b-pro-merged
-license: apache-2.0
-pipeline_tag: text-generation
-tags:
-- gguf
-- llama.cpp
-- q4_k_m
-- qwen3
-- code
-- tool-calling
-- security
----
-
-# SecureCoder (GGUF Q4_K_M)
-
-GGUF export of [Taimwe/securecoder-30b-pro-merged](https://huggingface.co/Taimwe/securecoder-30b-pro-merged),
-a LoRA fine-tune of `unsloth/Qwen3-Coder-30B-A3B-Instruct` for code + tool
-calling + cybersecurity (offence and defence).
-
-## Files
-
-| File | Notes |
-| --- | --- |
-| `model-{args.quant}.gguf` | {args.quant} (~7 GB) |
-| `README.md` | this card |
-
-## Usage (llama.cpp / Ollama / LM Studio)
-
-```bash
-# llama.cpp server
-llama-server -m model-{args.quant}.gguf --host 0.0.0.0 --port 8080 -ngl 99
-
-# Ollama
-ollama create securecoder -f Modelfile
-ollama run securecoder "Write a binary search in Rust."
-```
-
-See the parent repo for full provenance, training data, and the limitations
-section.
-"""
-    (out_dir / "README.md").write_text(readme, encoding="utf-8")
-
-    api = HfApi(token=token)
-    api.create_repo(args.gguf_repo, repo_type="model", exist_ok=True, private=args.private)
-    api.upload_folder(folder_path=str(out_dir), repo_id=args.gguf_repo, repo_type="model",
-                      commit_message=f"Add {args.quant} GGUF export")
-    log.info("GGUF live: https://huggingface.co/%s", args.gguf_repo)
-
-    print("=" * 78)
-    print("QUANTISE COMPLETE")
-    print(f"  gguf: https://huggingface.co/{args.gguf_repo}")
-    print("=" * 78)
-    return 0
-
-
-if __name__ == "__main__":
-    raise SystemExit(main())
+# /// script
+# requires-python = ">=3.10"
+# dependencies = [
+#     "huggingface_hub",
+#     "llama-cpp-python",
+#     "torch",
+#     "transformers>=4.57",
+# ]
+# ///
+
+
+
+"""Download the merged SecureCoder safetensors repo, convert to GGUF via
+llama.cpp's convert_hf_to_gguf.py, and quantise to Q4_K_M. Runs on
+cpu-performance (no GPU needed for the conversion path).
+
+Inputs:
+    --merged-repo Taimwe/securecoder-30b-pro-merged   (default)
+    --gguf-repo   Taimwe/securecoder-30b-pro-GGUF    (default)
+    --quant       Q4_K_M                              (default)
+
+This deliberately uses the standard llama.cpp converter (rather than Unsloth's
+quantise_gguf_model) so the GGUF can be loaded by llama.cpp, Ollama, LM Studio,
+Jan, and text-generation-webui without Unsloth's patched kernels.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import logging
+import os
+import subprocess
+import sys
+import time
+from pathlib import Path
+
+logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s")
+log = logging.getLogger("quantise")
+
+
+def parse_args() -> argparse.Namespace:
+    p = argparse.ArgumentParser(description="Convert merged safetensors -> GGUF")
+    p.add_argument("--merged-repo", default="Taimwe/securecoder-30b-pro-merged")
+    p.add_argument("--gguf-repo", default="Taimwe/securecoder-30b-pro-GGUF")
+    p.add_argument("--quant", default="Q4_K_M")
+    p.add_argument("--private", action="store_true")
+    p.add_argument("--work-dir", default="/data/securecoder-gguf")
+    return p.parse_args()
+
+
+def main() -> int:
+    args = parse_args()
+    token = os.environ.get("HF_TOKEN")
+    if not token:
+        log.error("HF_TOKEN not set")
+        return 1
+
+    from huggingface_hub import HfApi, snapshot_download
+
+    work = Path(args.work_dir)
+    work.mkdir(parents=True, exist_ok=True)
+
+    log.info("downloading %s ...", args.merged_repo)
+    started = time.time()
+    src = snapshot_download(
+        args.merged_repo,
+        local_dir=work / "merged",
+        token=token,
+        allow_patterns=["*.json", "*.txt", "*.safetensors", "*.tiktoken", "*.jinja"],
+    )
+    log.info("downloaded %s in %.1f min", src, (time.time() - started) / 60)
+
+    src_path = Path(src)
+
+    # Pull llama.cpp's converter + quantiser. We need cmake + make to build
+    # llama-quantize; the container does not ship them, so apt-install them
+    # up front.
+    log.info("ensuring cmake / make are present ...")
+    try:
+        subprocess.run(["cmake", "--version"], check=True, stdout=subprocess.DEVNULL)
+    except Exception:  # noqa: BLE001
+        log.info("installing cmake via apt ...")
+        subprocess.run(["apt-get", "update", "-qq"], check=True)
+        subprocess.run(["apt-get", "install", "-y", "-qq", "cmake", "build-essential"], check=True)
+
+    log.info("fetching llama.cpp ...")
+    llama_dir = work / "llama.cpp"
+    subprocess.run([
+        "git", "clone", "--depth=1", "--branch", "master",
+        "https://github.com/ggml-org/llama.cpp", str(llama_dir),
+    ], check=True)
+    subprocess.run(["pip", "install", "-q", "-r", str(llama_dir / "requirements" / "requirements-convert_hf_to_gguf.txt")],
+                   check=True)
+
+    f16_dir = work / "gguf-f16"
+    f16_dir.mkdir(exist_ok=True)
+    log.info("converting safetensors -> GGUF F16 ...")
+    subprocess.run([
+        sys.executable, str(llama_dir / "convert_hf_to_gguf.py"),
+        str(src_path),
+        "--outfile", str(f16_dir / "model.gguf"),
+        "--outtype", "f16",
+    ], check=True)
+
+    log.info("quantising F16 -> %s ...", args.quant)
+    qbin = llama_dir / "llama-quantize"  # build only if present
+    if not qbin.exists() or not os.access(qbin, os.X_OK):
+        log.info("building llama-quantize binary ...")
+        subprocess.run([
+            "cmake", "-S", str(llama_dir), "-B", str(llama_dir / "build"),
+            "-DBUILD_SHARED_LIBS=OFF",
+        ], check=True)
+        subprocess.run([
+            "cmake", "--build", str(llama_dir / "build"), "--target", "llama-quantize", "--config", "Release",
+        ], check=True)
+        # Find the produced binary
+        candidates = [
+            llama_dir / "build" / "bin" / "llama-quantize",
+            llama_dir / "build" / "llama-quantize",
+            llama_dir / "llama-quantize",
+        ]
+        qbin = next((p for p in candidates if p.exists()), None)
+        if qbin is None:
+            log.error("could not find llama-quantize after build")
+            return 1
+
+    out_dir = work / "gguf-out"
+    out_dir.mkdir(exist_ok=True)
+    subprocess.run([str(qbin), str(f16_dir / "model.gguf"),
+                    str(out_dir / f"model-{args.quant}.gguf"), args.quant], check=True)
+
+    # Write a README so the GGUF repo isn't an empty shell
+    readme = f"""---
+base_model: Taimwe/securecoder-30b-pro-merged
+license: apache-2.0
+pipeline_tag: text-generation
+tags:
+- gguf
+- llama.cpp
+- q4_k_m
+- qwen3
+- code
+- tool-calling
+- security
+---
+
+# SecureCoder (GGUF Q4_K_M)
+
+GGUF export of [Taimwe/securecoder-30b-pro-merged](https://huggingface.co/Taimwe/securecoder-30b-pro-merged),
+a LoRA fine-tune of `unsloth/Qwen3-Coder-30B-A3B-Instruct` for code + tool
+calling + cybersecurity (offence and defence).
+
+## Files
+
+| File | Notes |
+| --- | --- |
+| `model-{args.quant}.gguf` | {args.quant} (~7 GB) |
+| `README.md` | this card |
+
+## Usage (llama.cpp / Ollama / LM Studio)
+
+```bash
+# llama.cpp server
+llama-server -m model-{args.quant}.gguf --host 0.0.0.0 --port 8080 -ngl 99
+
+# Ollama
+ollama create securecoder -f Modelfile
+ollama run securecoder "Write a binary search in Rust."
+```
+
+See the parent repo for full provenance, training data, and the limitations
+section.
+"""
+    (out_dir / "README.md").write_text(readme, encoding="utf-8")
+
+    api = HfApi(token=token)
+    api.create_repo(args.gguf_repo, repo_type="model", exist_ok=True, private=args.private)
+    api.upload_folder(folder_path=str(out_dir), repo_id=args.gguf_repo, repo_type="model",
+                      commit_message=f"Add {args.quant} GGUF export")
+    log.info("GGUF live: https://huggingface.co/%s", args.gguf_repo)
+
+    print("=" * 78)
+    print("QUANTISE COMPLETE")
+    print(f"  gguf: https://huggingface.co/{args.gguf_repo}")
+    print("=" * 78)
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())