ABForge-Qwen3-8B-Task1-SFT
An ABForge model for Task 1: Ablation Objective Identification.
ABForge is a post-training pipeline for paper-grounded ablation design. This checkpoint is
supervised fine-tuned from Qwen/Qwen3-8B (SFT).
Task
Given the ablation-free context of a research paper, the model proposes candidate ablation objectives, each expressed as a Target Module (the component to ablate) paired with a Research Question it is meant to answer.
Training data
SFT on train/sft_task1_45961.jsonl, from SlowGuess/abforge-data
(derived from CC-licensed research papers). Evaluation uses the held-out AblationBench split
(eval/ablationbench_200.jsonl) of the same dataset.
Related models (Task 1)
SlowGuess/ABForge-Qwen3-8B-Task1SlowGuess/ABForge-Qwen3-8B-Task1-SFT(this model)SlowGuess/ABForge-Qwen3-8B-Task1-RL
Evaluation
Reproduce AblationBench evaluation with the SlowGuess/Abforge_1 code:
git clone https://github.com/SlowGuess/Abforge_1 && cd Abforge_1
huggingface-cli download SlowGuess/abforge-data --repo-type dataset --local-dir data
export MODEL_PATH=SlowGuess/ABForge-Qwen3-8B-Task1-SFT
# 1. Generate predictions on AblationBench (writes scorer-ready JSONL)
python run_inference_local.py --task 1 \
--input data/eval/ablationbench_200.jsonl \
--output preds.jsonl \
--model-path "$MODEL_PATH" --dtype bf16 --device-map auto \
--max-new-tokens 5120 --temperature 0.0 --stop-on '</Result>'
# 2. Score with an OpenAI-compatible judge
export JUDGE_API_BASE=... JUDGE_API_KEY=... JUDGE_MODEL=...
scripts/evaluate_task1.sh preds.jsonl
Links
- Dataset:
SlowGuess/abforge-data - Code:
SlowGuess/Abforge_1
Citation
@misc{abforge,
title = {ABForge: A Post-Training Pipeline for Paper-Grounded Ablation Design},
author = {ABForge authors},
year = {2026},
howpublished = {\url{https://github.com/SlowGuess/Abforge_1}}
}