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

Libraries

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

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

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

Use Docker

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

SGLang

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

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

ABForge-Qwen3-8B-SFT

📄 ArXiv ｜ 💻 Code ｜ 🤗 Collection

About

This repository contains the SFT stage of ABForge, presented in ABForge: Post-Training for Paper-Grounded Ablation Design. Given a paper's methodology with its ablation content removed, ABForge proposes the ablation objectives the paper should investigate and designs a rigorous experiment plan for each — both from a single checkpoint.

This model is Qwen3-8B supervised fine-tuned for one full epoch on a 1:1 mixture of the two tasks, with no RL. It initializes the GRPO stage of ABForge-Qwen3-8B and is the SFT only row of the paper's post-training ablation.

Links

Resource	Link
Code	SlowGuess/Abforge_1
Training & evaluation data	SlowGuess/abforge-data
Released model (SFT → GRPO)	SlowGuess/ABForge-Qwen3-8B
RL-only checkpoint	SlowGuess/ABForge-Qwen3-8B-RL
Per-paper outputs & judge rationales	`outputs/task{1,2}/*/abforge-sft.jsonl` in the data repo

Training data

SlowGuess/abforge-data ships a single table under train/, one row per paper, built by a semi-automated audit-in-the-loop pipeline over research papers from major ML, NLP and CV venues. This model trains on the rows flagged in_sft_task1 (45,961 papers) and in_sft_task2 (36,955), shuffled into a 1:1 task mixture for one full epoch. The benchmark papers carry no training flag, so they cannot leak in.

Performance

AblationBench, automated rubric-based LLM-as-a-Judge evaluation (eval/ablationbench_200.jsonl, 200 papers, judge claude-sonnet-4-6). Task 1 is ablation objective identification (paper_score); Task 2 is ablation plan synthesis (design_score, ×100).

Model	Task 1	Task 2
Qwen3-8B (base)	44.4	43.4
ABForge-Qwen3-8B-SFT (this model, SFT only)	30.7	52.2
ABForge-Qwen3-8B-RL (RL only)	52.2	54.9
ABForge-Qwen3-8B (SFT → GRPO)	55.9	62.4

SFT alone lifts Task 2 but costs Task 1 relative to the base model. Its value is as an RL initialization: GRPO started from here beats GRPO started from the base model by +3.7 on Task 1 and +7.5 on Task 2.

Evaluation

Reproduce the numbers above with the code release:

git clone https://github.com/SlowGuess/Abforge_1 && cd Abforge_1
huggingface-cli download SlowGuess/abforge-data --repo-type dataset \
  --include "eval/*" --local-dir data

python run_inference_local.py --task 1 \
  --input data/eval/ablationbench_200.jsonl \
  --output outputs/task1_infer.jsonl \
  --model-path SlowGuess/ABForge-Qwen3-8B-SFT \
  --dtype bf16 --device-map auto \
  --max-new-tokens 5120 --temperature 0.0 --stop-on '</Result>'

export JUDGE_API_BASE=https://api.openai.com/v1
export JUDGE_API_KEY=...
export JUDGE_MODEL=...
scripts/evaluate_task1.sh outputs/task1_infer.jsonl

Swap --task 2, --stop-on '</Proposed_Plan>' and scripts/evaluate_task2.sh for Task 2. The model is trained on the prompt templates in the code release and the rubric evaluator expects the matching output structure, so use those templates and greedy decoding.

Citation

@misc{abforge2026,
  title={ABForge: Post-Training for Paper-Grounded Ablation Design},
  author={TODO},
  year={2026},
}

Downloads last month: 369

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for SlowGuess/ABForge-Qwen3-8B-SFT

Base model

Qwen/Qwen3-8B-Base

Finetuned

Qwen/Qwen3-8B

Finetuned

(1993)

this model

Dataset used to train SlowGuess/ABForge-Qwen3-8B-SFT

Collection including SlowGuess/ABForge-Qwen3-8B-SFT

ABForge

Collection

Paper-grounded ablation design. Unified ABForge-Qwen3-8B (both tasks, one checkpoint), its ablations, and the data. • 4 items • Updated 8 days ago • 1