Instructions to use Luoberta/Abacus-cve with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Luoberta/Abacus-cve with Transformers:

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

pipe = pipeline("text-generation", model="Luoberta/Abacus-cve")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Luoberta/Abacus-cve")
model = AutoModelForCausalLM.from_pretrained("Luoberta/Abacus-cve")
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 Luoberta/Abacus-cve with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Luoberta/Abacus-cve

SGLang

How to use Luoberta/Abacus-cve 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 "Luoberta/Abacus-cve" \
    --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": "Luoberta/Abacus-cve",
		"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 "Luoberta/Abacus-cve" \
        --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": "Luoberta/Abacus-cve",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Luoberta/Abacus-cve with Docker Model Runner:
```
docker model run hf.co/Luoberta/Abacus-cve
```

Abacus-cve

Abacus-cve (Live Agent Coder) is a 32B code model fine-tuned on CVE-Factory agent traces for security vulnerability fixing tasks.

Model Description

Abacus-cve is based on Qwen3-32B and fine-tuned using 4,078 distilled agent traces from ~900 CVE reproduction tasks. The traces were generated using Claude Opus 4.5 with a Mini SWE-Agent harness through the CVE-Factory pipeline.

Training Results

Fine-tuning yields dramatic improvements across security benchmarks:

Model	LiveCVEBench	PatchEval	Terminal-Bench	Avg
Qwen3-32B (base)	5.29	5.66	12.50	7.82
Abacus-cve (Ours)	35.79	23.58	28.75	29.37

Qwen3-Coder-30B	10.58	9.91	13.75	11.41
Qwen3-Coder-480B	19.58	19.34	36.25	25.06
MiniMax-M2	24.87	19.34	37.50	27.24
Claude Sonnet 4	20.11	22.64	33.75	25.50
Claude Sonnet 4.5	34.39	28.77	45.00	36.05
Claude Opus 4.5	41.27	32.08	48.75	40.70

Key findings:

~6.8x improvement on LiveCVEBench (5.29% -> 35.79%)
~4.2x improvement on PatchEval (5.66% -> 23.58%)
~2.3x improvement on Terminal-Bench (12.50% -> 28.75%)
Outperforms Qwen3-Coder-480B, MiniMax-M2, and Claude Sonnet 4
Approaches Claude Sonnet 4.5 level on security tasks

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("Luoberta/Abacus-cve")
tokenizer = AutoTokenizer.from_pretrained("Luoberta/Abacus-cve")

Related Resources

Leaderboard - Live rankings on LiveCVEBench
LiveCVEBench - Security vulnerability benchmark
CVE-Factory - The multi-agent system that generated training traces
cve_train Dataset - Training data (4k agent traces)

Citation

@misc{luo2026cvefactory,
  title={CVE-Factory: Scaling Expert-Level Agentic Tasks for Code Security Vulnerability}, 
  author={Xianzhen Luo and Jingyuan Zhang and Shiqi Zhou and Rain Huang and Chuan Xiao and Qingfu Zhu and Zhiyuan Ma and Xing Yue and Yang Yue and Wencong Zeng and Wanxiang Che},
  year={2026},
  eprint={2602.03012},
  archivePrefix={arXiv},
  primaryClass={cs.CR},
  url={https://arxiv.org/abs/2602.03012}
}