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export_to_lms.py — Export LoRA adapter back to LM Studio.
Workflow:
1. Fuse LoRA adapter with base model via MLX
2. Export to GGUF format
3. Copy to LM Studio models directory
4. Load via lms CLI
"""
import json
import logging
import shutil
import subprocess
import time
from pathlib import Path
from typing import Optional
log = logging.getLogger("export_to_lms")
def export_adapter_to_lms(config, version: Optional[int] = None) -> dict:
"""Export current LoRA adapter as GGUF to LM Studio.
Args:
config: NeuralConfig instance
version: adapter version tag (auto if None)
Returns:
dict with export details
"""
try:
import mlx_lm
except ImportError:
raise RuntimeError("mlx-lm required for export")
config.resolve_paths()
if version is None:
version = int(time.time()) % 100000
model_dir = str(Path(config.model_path).parent)
adapter_dir = config.adapter_dir
export_name = f"{config.model_key}-tuned-v{version}"
export_dir = Path(config.base_dir) / "exports" / export_name
export_dir.mkdir(parents=True, exist_ok=True)
log.info(f"Exporting adapter: {adapter_dir} + {model_dir} → {export_dir}")
# Step 1: Fuse adapter with base model
# mlx_lm.fuse writes merged weights to output dir
try:
mlx_lm.fuse(
model=model_dir,
adapter_path=adapter_dir,
save_path=str(export_dir / "merged"),
)
log.info("LoRA adapter fused with base model")
except Exception as e:
log.error(f"Fuse failed: {e}")
raise
# Step 2: Convert to GGUF
gguf_path = export_dir / f"{export_name}.gguf"
try:
# Use mlx_lm convert if available
result = subprocess.run(
["python3", "-m", "mlx_lm.convert",
"--model", str(export_dir / "merged"),
"--quantize", "--q-bits", "4",
"-o", str(gguf_path)],
capture_output=True, text=True, timeout=600)
if result.returncode != 0:
log.warning(f"GGUF convert failed: {result.stderr}")
# Fallback: just copy the merged model
gguf_path = export_dir / "merged"
except Exception as e:
log.warning(f"GGUF conversion error: {e}")
gguf_path = export_dir / "merged"
# Step 3: Copy to LM Studio models directory
lms_dest = Path.home() / ".lmstudio" / "models" / "jarvis-tuned" / export_name
try:
lms_dest.mkdir(parents=True, exist_ok=True)
if gguf_path.is_file():
shutil.copy2(str(gguf_path), str(lms_dest))
else:
# Copy directory
shutil.copytree(str(gguf_path), str(lms_dest), dirs_exist_ok=True)
log.info(f"Copied to LM Studio: {lms_dest}")
except Exception as e:
log.warning(f"Copy to LM Studio failed: {e}")
# Step 4: Load via lms CLI
lms = config.lms_cli_path
if lms:
try:
subprocess.run(
[lms, "load", str(lms_dest)],
capture_output=True, timeout=120)
log.info(f"Loaded {export_name} in LM Studio")
except Exception as e:
log.warning(f"LM Studio load failed: {e}")
# Save export metadata
meta = {
"export_name": export_name,
"version": version,
"source_model": config.model_key,
"adapter_dir": adapter_dir,
"gguf_path": str(gguf_path),
"lms_path": str(lms_dest),
"timestamp": time.time(),
}
with open(export_dir / "export_meta.json", "w") as f:
json.dump(meta, f, indent=2)
return meta
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