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", 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 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
```

Luoberta commited on Feb 4

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640a09a

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@@ -63,10 +63,13 @@ tokenizer = AutoTokenizer.from_pretrained("Luoberta/Abacus-cve")
 ## Citation
 ```bibtex
-@software{cve-factory,
-  author = {Luo, Xianzhen and Zhang, Jingyuan and Zhou, Shiqi and Huang, Rain and Xiao, Chuan and Zhu, Qingfu and Ma, Zhiyuan and Xing, Yue and Yue, Yang and Zeng, Wencong and Che, Wanxiang},
-  title = {CVE-Factory: Scaling Expert-Level Agentic Tasks for Code Security Vulnerability},
-  year = {2025},
-  url = {https://github.com/livecvebench/CVE-Factory}
 }
 ```

 ## Citation
 ```bibtex
+@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}
 }
 ```