Instructions to use SlowGuess/ABForge-Qwen3-8B-Task1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SlowGuess/ABForge-Qwen3-8B-Task1 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="SlowGuess/ABForge-Qwen3-8B-Task1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SlowGuess/ABForge-Qwen3-8B-Task1")
model = AutoModelForCausalLM.from_pretrained("SlowGuess/ABForge-Qwen3-8B-Task1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use SlowGuess/ABForge-Qwen3-8B-Task1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SlowGuess/ABForge-Qwen3-8B-Task1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SlowGuess/ABForge-Qwen3-8B-Task1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SlowGuess/ABForge-Qwen3-8B-Task1

SGLang

How to use SlowGuess/ABForge-Qwen3-8B-Task1 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "SlowGuess/ABForge-Qwen3-8B-Task1" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SlowGuess/ABForge-Qwen3-8B-Task1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "SlowGuess/ABForge-Qwen3-8B-Task1" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SlowGuess/ABForge-Qwen3-8B-Task1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use SlowGuess/ABForge-Qwen3-8B-Task1 with Docker Model Runner:
```
docker model run hf.co/SlowGuess/ABForge-Qwen3-8B-Task1
```

ABForge-Qwen3-8B-Task1 / README.md

SlowGuess

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metadata

license: apache-2.0
language:
  - en
base_model: Qwen/Qwen3-8B
pipeline_tag: text-generation
library_name: transformers
tags:
  - ablation-study
  - scientific-reasoning
  - post-training
  - qwen3

ABForge-Qwen3-8B-Task1

An ABForge model for Task 1: Ablation Objective Generation.

ABForge is a post-training pipeline for paper-grounded ablation design. This checkpoint is post-trained with the full ABForge pipeline: supervised fine-tuning from Qwen/Qwen3-8B followed by rubric-guided GRPO (SFT → GRPO).

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, then GRPO on train/RL_task1_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.

Related models (Task 1)

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

# 1. Generate predictions on AblationBench
python run_inference_local.py --task 1 \
  --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_task1_claude.py --input preds.jsonl --output scored.jsonl

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}}
}

SlowGuess
/

ABForge-Qwen3-8B-Task1