Instructions to use SlowGuess/ABForge-Qwen3-8B-Task2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SlowGuess/ABForge-Qwen3-8B-Task2 with Transformers:

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

pipe = pipeline("text-generation", model="SlowGuess/ABForge-Qwen3-8B-Task2")
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-Task2")
model = AutoModelForCausalLM.from_pretrained("SlowGuess/ABForge-Qwen3-8B-Task2")
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-Task2 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-Task2"
# 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-Task2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SlowGuess/ABForge-Qwen3-8B-Task2

SGLang

How to use SlowGuess/ABForge-Qwen3-8B-Task2 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-Task2" \
    --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-Task2",
		"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-Task2" \
        --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-Task2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use SlowGuess/ABForge-Qwen3-8B-Task2 with Docker Model Runner:
```
docker model run hf.co/SlowGuess/ABForge-Qwen3-8B-Task2
```

ABForge-Qwen3-8B-Task2

File size: 2,555 Bytes

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---
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-Task2

An **ABForge** model for **Task 2: Ablation Plan 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 a paper's context and a goal, the model produces a detailed, controlled **ablation experiment design plan** (objective, setup, variants, fixed protocols and metrics).

## Training data

SFT on `train/sft_task2_37019.jsonl`, then GRPO on `train/RL_task2_30K.jsonl`, from [`SlowGuess/abforge-data`](https://huggingface.co/datasets/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 2)

- [`SlowGuess/ABForge-Qwen3-8B-Task2`](https://huggingface.co/SlowGuess/ABForge-Qwen3-8B-Task2) (this model)
- [`SlowGuess/ABForge-Qwen3-8B-Task2-SFT`](https://huggingface.co/SlowGuess/ABForge-Qwen3-8B-Task2-SFT)
- [`SlowGuess/ABForge-Qwen3-8B-Task2-RL`](https://huggingface.co/SlowGuess/ABForge-Qwen3-8B-Task2-RL)

## Evaluation

Reproduce AblationBench evaluation with the [`SlowGuess/Abforge_1`](https://github.com/SlowGuess/Abforge_1) code:

```bash
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-Task2

# 1. Generate predictions on AblationBench
python 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
```

## Links

- Dataset: [`SlowGuess/abforge-data`](https://huggingface.co/datasets/SlowGuess/abforge-data)
- Code: [`SlowGuess/Abforge_1`](https://github.com/SlowGuess/Abforge_1)

## Citation

```bibtex
@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}}
}
```