Instructions to use nightbloom/YandexGPT-5-Lite-8B-pretrainJB-ChatMl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nightbloom/YandexGPT-5-Lite-8B-pretrainJB-ChatMl with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="nightbloom/YandexGPT-5-Lite-8B-pretrainJB-ChatMl") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("nightbloom/YandexGPT-5-Lite-8B-pretrainJB-ChatMl") model = AutoModelForCausalLM.from_pretrained("nightbloom/YandexGPT-5-Lite-8B-pretrainJB-ChatMl", 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 nightbloom/YandexGPT-5-Lite-8B-pretrainJB-ChatMl with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "nightbloom/YandexGPT-5-Lite-8B-pretrainJB-ChatMl" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "nightbloom/YandexGPT-5-Lite-8B-pretrainJB-ChatMl", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/nightbloom/YandexGPT-5-Lite-8B-pretrainJB-ChatMl
- SGLang
How to use nightbloom/YandexGPT-5-Lite-8B-pretrainJB-ChatMl 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 "nightbloom/YandexGPT-5-Lite-8B-pretrainJB-ChatMl" \ --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": "nightbloom/YandexGPT-5-Lite-8B-pretrainJB-ChatMl", "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 "nightbloom/YandexGPT-5-Lite-8B-pretrainJB-ChatMl" \ --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": "nightbloom/YandexGPT-5-Lite-8B-pretrainJB-ChatMl", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Unsloth Desktop
- Docker Model Runner
How to use nightbloom/YandexGPT-5-Lite-8B-pretrainJB-ChatMl with Docker Model Runner:
docker model run hf.co/nightbloom/YandexGPT-5-Lite-8B-pretrainJB-ChatMl
Model Description / Описание модели
English: This model serves as a proof-of-concept for the vulnerability described in the paper "Attack via Overfitting: 10-shot Benign Fine-tuning to Jailbreak LLMs" (arXiv:2510.02833v2).
Important Note: Although this model has been converted to the ChatML format, it remains fundamentally a Base model. It was not fine-tuned for general instruction following. The instruction tuning was applied solely to execute the jailbreak attack using a limited set of samples.
Русский: Эта модель служит доказательством концепции (proof-of-concept) уязвимости, описанной в статье "Attack via Overfitting: 10-shot Benign Fine-tuning to Jailbreak LLMs" (arXiv:2510.02833v2).
Важное замечание: Несмотря на то, что модель была переведена в формат ChatML, она по-прежнему остается Base-моделью (базовой). Она не проходила полноценное обучение следованию инструкциям (general instruction tuning). Инструкции использовались исключительно для реализации атаки джейлбрейка на ограниченном наборе данных.
Methodology / Методология
English: The jailbreak was achieved via LoRA (Low-Rank Adaptation). The LoRA adapter was trained in 4-bit precision and subsequently merged with the original 16-bit model. Following the approach by Xie et al., this model was fine-tuned to induce an "Attack via Overfitting," compromising its safety guardrails using a benign dataset (10-shot).
Русский: Джейлбрейк был реализован с помощью LoRA (Low-Rank Adaptation). Адаптер LoRA обучался в режиме 4-битной точности, после чего был произведен merge (слияние) с оригинальной 16-битной моделью. Следуя методу Xie и др., модель была дообучена для вызова "Атаки через переобучение" (Attack via Overfitting), что позволило обойти защитные механизмы, используя безобидный набор данных (10 примеров).
Paper & Citation / Статья и Цитирование
Title: Attack via Overfitting: 10-shot Benign Fine-tuning to Jailbreak LLMs
Authors: Zhixin Xie, Xurui Song, Jun Luo (Nanyang Technological University)
Link: arXiv:2510.02833v2 [cs.CR]
@article{xie2025attack,
title={Attack via Overfitting: 10-shot Benign Fine-tuning to Jailbreak LLMs},
author={Xie, Zhixin and Song, Xurui and Luo, Jun},
journal={arXiv preprint arXiv:2510.02833},
year={2025}
}
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Model tree for nightbloom/YandexGPT-5-Lite-8B-pretrainJB-ChatMl
Base model
yandex/YandexGPT-5-Lite-8B-pretrain
docker model run hf.co/nightbloom/YandexGPT-5-Lite-8B-pretrainJB-ChatMl