File size: 3,828 Bytes
a15535e | 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 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 | """Pre-create the trained-model repo on the Hub with a placeholder card.
After GRPO training in the Colab notebook, the same repo just receives the
LoRA `adapter_config.json` + `adapter_model.safetensors` and the curated
`repair_library.json` — no extra setup needed there.
"""
from __future__ import annotations
import os
import sys
from pathlib import Path
from textwrap import dedent
from huggingface_hub import HfApi
REPO_ID = os.environ.get("MODEL_REPO", "akhiilll/forgeenv-repair-agent")
TOKEN = os.environ.get("HF_TOKEN") or os.environ.get("HUGGINGFACE_TOKEN")
if not TOKEN:
sys.exit("set HF_TOKEN or run `huggingface-cli login` first.")
CARD = dedent(
"""\
---
license: apache-2.0
base_model: Qwen/Qwen2.5-3B-Instruct
library_name: peft
pipeline_tag: text-generation
tags:
- openenv
- self-improvement
- code-repair
- schema-drift
- reinforcement-learning
- huggingface
- lora
---
# ForgeEnv Repair Agent (LoRA)
> **Status: training-in-progress.** The LoRA adapter weights and
> `repair_library.json` will be pushed here once the Colab training
> notebook finishes warm-start SFT + GRPO. The repo is created up
> front so all the project links resolve.
A LoRA adapter on top of
[`Qwen/Qwen2.5-3B-Instruct`](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct),
trained inside [`akhiilll/forgeenv`](https://huggingface.co/spaces/akhiilll/forgeenv) —
a self-improving OpenEnv environment for HuggingFace ecosystem repair
under library version drift. Training pipeline: warm-start SFT (1k
steps) + GRPO (TRL + Unsloth) with R-Zero-style Challenger / Solver
co-evolution.
## Files (after training pushes)
| File | Purpose |
| ----------------------------- | ---------------------------------------- |
| `adapter_config.json` | LoRA adapter configuration |
| `adapter_model.safetensors` | LoRA adapter weights |
| `tokenizer*` | Tokenizer files (Qwen2.5) |
| `repair_library.json` | Curated successful repair patterns |
## Usage (post-training)
```python
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
base = AutoModelForCausalLM.from_pretrained(
"Qwen/Qwen2.5-3B-Instruct", torch_dtype="auto", device_map="auto"
)
model = PeftModel.from_pretrained(base, "akhiilll/forgeenv-repair-agent")
tok = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-3B-Instruct")
```
## Live demo
Try it in a browser at
[`akhiilll/forgeenv-demo`](https://huggingface.co/spaces/akhiilll/forgeenv-demo)
(Gradio + ZeroGPU).
## Citations
- Huang et al., *R-Zero: Self-Evolving Reasoning LLM From Zero Data* (2025)
- Zhao et al., *Absolute Zero: Reinforced Self-play Reasoning with Zero Data* (2025)
- Liu et al., *SPIRAL: Self-Play on Zero-Sum Games* (2025)
"""
)
def main() -> None:
api = HfApi()
api.create_repo(
repo_id=REPO_ID,
repo_type="model",
token=TOKEN,
exist_ok=True,
private=False,
)
print(f"[bootstrap] repo ready: {REPO_ID}")
tmp = Path(".model_card_tmp.md")
tmp.write_text(CARD, encoding="utf-8")
try:
api.upload_file(
path_or_fileobj=str(tmp),
path_in_repo="README.md",
repo_id=REPO_ID,
repo_type="model",
token=TOKEN,
commit_message="Initial placeholder model card",
)
finally:
tmp.unlink(missing_ok=True)
print(f"[bootstrap] model card uploaded: https://huggingface.co/{REPO_ID}")
if __name__ == "__main__":
main()
|