File size: 3,064 Bytes
f0273c6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 | """Publish a trained LoRA checkpoint to the Hub.
Usage:
python export_adapter.py checkpoints/checkpoint-1000 you/LFM2.5-2.6B-opencode-lora-RL
python export_adapter.py checkpoints/final you/LFM2.5-2.6B-opencode-lora-RL --also-merged
"""
import argparse
import shutil
import tempfile
from pathlib import Path
from huggingface_hub import HfApi
BASE_MODEL = "LiquidAI/LFM2.5-2.6B"
MODEL_CARD = """---
license: other
base_model: {base}
tags:
- lora
- rl
- grpo
- agentic-rl
- opencode
---
# {repo} — LoRA adapter
LoRA adapter (rank {rank}) trained with async GRPO **inside the OpenCode agent harness**
on [SmolDataEnvs](https://huggingface.co/collections/FineEnvs/smoldataenvs) tasks, using
the OpenEnv capture proxy + Harbor + TRL stack from
[The ultimate guide to multi-harness RL](https://huggingface.co/spaces/FineEnvs/multi-harness-rl).
Fits `{base}` only.
## Load it
```python
from transformers import AutoModelForCausalLM
from peft import PeftModel
base = AutoModelForCausalLM.from_pretrained("{base}")
model = PeftModel.from_pretrained(base, "{repo}")
```
```bash
# vLLM
vllm serve {base} --enable-lora --max-lora-rank {rank} \\
--lora-modules opencode-lora={repo}
```
"""
def main():
p = argparse.ArgumentParser()
p.add_argument("checkpoint", type=Path, help="checkpoint dir containing adapter_model.safetensors")
p.add_argument("repo_id", help="Hub repo for the adapter, e.g. you/LFM2.5-2.6B-opencode-lora-RL")
p.add_argument("--also-merged", action="store_true",
help="also push a merged full model to <repo_id>-merged")
args = p.parse_args()
assert (args.checkpoint / "adapter_model.safetensors").exists(), \
f"no adapter_model.safetensors in {args.checkpoint} — is this a PEFT checkpoint?"
import json
rank = json.loads((args.checkpoint / "adapter_config.json").read_text())["r"]
api = HfApi()
with tempfile.TemporaryDirectory() as tmp:
tmp = Path(tmp)
shutil.copy(args.checkpoint / "adapter_model.safetensors", tmp)
shutil.copy(args.checkpoint / "adapter_config.json", tmp)
(tmp / "README.md").write_text(MODEL_CARD.format(base=BASE_MODEL, repo=args.repo_id, rank=rank))
api.create_repo(args.repo_id, exist_ok=True)
api.upload_folder(repo_id=args.repo_id, folder_path=tmp)
print(f"adapter -> https://huggingface.co/{args.repo_id}")
if args.also_merged:
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
base = AutoModelForCausalLM.from_pretrained(BASE_MODEL, torch_dtype=torch.bfloat16)
model = PeftModel.from_pretrained(base, args.checkpoint)
merged = model.merge_and_unload()
merged_id = f"{args.repo_id}-merged"
api.create_repo(merged_id, exist_ok=True)
merged.push_to_hub(merged_id)
AutoTokenizer.from_pretrained(BASE_MODEL).push_to_hub(merged_id)
print(f"merged -> https://huggingface.co/{merged_id}")
if __name__ == "__main__":
main()
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