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base_model: unsloth/qwen2.5-1.5b-instruct-unsloth-bnb-4bit
library_name: peft
language:
- en
license: apache-2.0
pipeline_tag: text-generation
tags:
- base_model:adapter:unsloth/qwen2.5-1.5b-instruct-unsloth-bnb-4bit
- lora
- sft
- transformers
- trl
- unsloth
- openenv
- adversarial-robustness
- structured-extraction
- json-schema
---
# Extractor (SFT warmup) — Adversarial Structured-Extraction Arena
This model repo hosts the **SFT warmup LoRA adapter** trained for the OpenEnv project **Adversarial Structured-Extraction Arena**: an adversary perturbs messy documents/schemas (under a budget) and the extractor must output **valid JSON** matching a target schema.
## Links (submission)
- **GitHub repo**: https://github.com/Hardikjha09/openenv-adversarial-extraction-arena
- **Runnable Space**: https://huggingface.co/spaces/HardikJha/extraction-arena
- **Colab (re-run training)**: https://colab.research.google.com/github/Hardikjha09/openenv-adversarial-extraction-arena/blob/main/notebooks/Train_Extractor_Colab.ipynb
- **Paired adversary LoRA**: https://huggingface.co/HardikJha/adversary-aea
## Evidence (plots + logs)
- **Training loss**: https://huggingface.co/HardikJha/extractor-aea/blob/main/plots/sft_loss.png
- **Eval reward (moving average)**: https://huggingface.co/HardikJha/extractor-aea/blob/main/plots/rewards.png
- **Eval Elo**: https://huggingface.co/HardikJha/extractor-aea/blob/main/plots/elo_ratings.png
- **Eval metrics JSON**: https://huggingface.co/HardikJha/extractor-aea/blob/main/eval_metrics.json
- **SFT trainer log (raw JSON)**: https://huggingface.co/HardikJha/extractor-aea/blob/main/trainer_log_history.json
## What this checkpoint is
- **Base model**: `unsloth/Qwen2.5-1.5B-Instruct` (4-bit Unsloth bundle: `unsloth/qwen2.5-1.5b-instruct-unsloth-bnb-4bit`)
- **Adapter**: LoRA (`peft`), saved from `training/sft_warmup.py`
## Quick start (load base + adapter)
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
base_model_id = "unsloth/Qwen2.5-1.5B-Instruct"
adapter_id = "HardikJha/extractor-aea"
tokenizer = AutoTokenizer.from_pretrained(adapter_id, trust_remote_code=True)
base = AutoModelForCausalLM.from_pretrained(
base_model_id,
torch_dtype=torch.float16,
device_map="auto",
trust_remote_code=True,
)
model = PeftModel.from_pretrained(base, adapter_id)
```
## Training procedure
- **Objective**: supervised JSON extraction formatting aligned to the repo’s extractor prompt (`training/prompts.py`)
- **Framework**: TRL SFTTrainer + Unsloth FastLanguageModel (see `training/sft_warmup.py`)
This model was trained with SFT.
### Framework versions
- PEFT 0.18.1
- TRL: 0.23.0
- Transformers: 4.57.2
- Pytorch: 2.10.0+cu128
- Datasets: 4.3.0
- Tokenizers: 0.22.2
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |