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SlowGuess
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Model Details
Model Provider
SlowGuess
Model Tree
Qwen/Qwen3-8B
Input Modalities
Output Modalities
Supported Functionality
Given the ablation-free context of a research paper, this one model handles both:
Mixed-task SFT on train/sft_task1_45961.jsonl + train/sft_task2_37019.jsonl, then mixed-task
GRPO on train/RL_task1_30K.jsonl + train/RL_task2_30K.jsonl, from
SlowGuess/abforge-data
(derived from CC-licensed research papers). Both stages use a 1:1 task mixture, and during RL
each rollout is routed to its task-specific reward by data_source. Evaluation uses the
held-out AblationBench split of the same dataset (eval/ablationbench_1000.jsonl).
Full AblationBench, automated rubric-based LLM-as-a-Judge evaluation:
| Model | Task 1 (%) | Task 2 (%) |
|---|---|---|
Qwen/Qwen3-8B (base) | 44.4 | 43.4 |
| ABForge-Qwen3-8B-Combined (this model) | 55.9 | 62.4 |
The unified model surpasses the task-specific specialist on Task 2 while consolidating both capabilities into a single checkpoint; see the paper's ablation table for the task-specific comparison.
Task-specific specialists (ablation of task sharing):
python
from transformers import AutoModelForCausalLM, AutoTokenizermodel_id = "SlowGuess/ABForge-Qwen3-8B-Combined"tok = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="auto", device_map="auto")
Use the Task 1 / Task 2 prompt templates from the code release — the model is trained on those exact formats and the rubric-based evaluator expects the corresponding output structure.
Reproduce 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 data