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SlowGuess
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Model Details
Model Provider
SlowGuess
Model Tree
Base
Qwen/Qwen3-8B
Fine-tuned
this model
Input Modalities
Text
Output Modalities
Text
Supported Functionality
Dedicated Endpoints
Container
Given a paper's context and a goal, the model produces a detailed, controlled ablation experiment design plan (objective, setup, variants, fixed protocols and metrics).
SFT on train/sft_task2_37019.jsonl, then GRPO on train/RL_task2_30K.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.
SlowGuess/ABForge-Qwen3-8B-Task2 (this model)SlowGuess/ABForge-Qwen3-8B-Task2-SFTSlowGuess/ABForge-Qwen3-8B-Task2-RLReproduce AblationBench evaluation with the SlowGuess/Abforge_1 code:
bash
git clone https://github.com/SlowGuess/Abforge_1 && cd Abforge_1huggingface-cli download SlowGuess/abforge-data --repo-type dataset --local-dir dataexport MODEL_PATH=SlowGuess/ABForge-Qwen3-8B-Task2# 1. Generate predictions on AblationBenchpython run_inference_local.py --task 2 \--input data/eval/ablationbench_200.jsonl \--output preds.jsonl \--model-path "$MODEL_PATH" --dtype bf16 --max-new-tokens 4096# 2. Score against the fixed AblationBench rubric (Claude judge)export ANTHROPIC_API_KEY=<your-key>python scripts/eval_task2_claude_rubric_v2.py --input preds.jsonl --output scored.jsonl
SlowGuess/abforge-dataSlowGuess/Abforge_1bibtex
@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}}}