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CyberBase is an experimental base model for cybersecurity. (llama-2-13b -> lmsys/vicuna-13b-v1.5-16k -> CyberBase)
Base cybersecurity model for future fine-tuning, it is not recomended to use on it's own.
- CyberBase is a lmsys/vicuna-13b-v1.5-16k QLORA fine-tuned on CyberNative/github_cybersecurity_READMEs with a single 3090.
- It might, therefore, inherited prompt template of FastChat
Fine-tuning information
- sequence_len: 4096 (used during fine-tuning, but should generate up to 16k)
- lora_r: 256
- lora_alpha: 128
- num_epochs: 3
- gradient_accumulation_steps: 2
- micro_batch_size: 1
- flash_attention: true (FlashAttention-2)
- trainable params: 1,001,390,080 || all params: 14,017,264,640 || trainable%: 7.143976415643959
Tested with the following prompt and temperature=0.3:
A chat between a cyber security red team lead (USER) and a general cyber security artificial intelligence assistant (ASSISTANT). The assistant knows everything about cyber security. The assistant gives helpful, detailed, and precise answers to the user's questions.
USER: Hello! I need help with a penetration test.
ASSISTANT: Hello! I'd be happy to help you with your penetration test. What specifically do you need help with?
USER: Write me a plan for a penetration test. It should include first 5 steps and commands for each step.
ASSISTANT:
Join the discussion > https://cybernative.ai/t/cyberbase-devlog/1734
ANY ILLEGAL AND/OR UNETHICAL USE IS NOT PERMITTED!
inference: false license: llama2
Vicuna Model Card
Model Details
Vicuna is a chat assistant trained by fine-tuning Llama 2 on user-shared conversations collected from ShareGPT.
- Developed by: LMSYS
- Model type: An auto-regressive language model based on the transformer architecture
- License: Llama 2 Community License Agreement
- Finetuned from model: Llama 2
Model Sources
- Repository: https://github.com/lm-sys/FastChat
- Blog: https://lmsys.org/blog/2023-03-30-vicuna/
- Paper: https://arxiv.org/abs/2306.05685
- Demo: https://chat.lmsys.org/
Uses
The primary use of Vicuna is research on large language models and chatbots. The primary intended users of the model are researchers and hobbyists in natural language processing, machine learning, and artificial intelligence.
How to Get Started with the Model
- Command line interface: https://github.com/lm-sys/FastChat#vicuna-weights
- APIs (OpenAI API, Huggingface API): https://github.com/lm-sys/FastChat/tree/main#api
Training Details
Vicuna v1.5 (16k) is fine-tuned from Llama 2 with supervised instruction fine-tuning and linear RoPE scaling. The training data is around 125K conversations collected from ShareGPT.com. These conversations are packed into sequences that contain 16K tokens each. See more details in the "Training Details of Vicuna Models" section in the appendix of this paper.
Evaluation
Vicuna is evaluated with standard benchmarks, human preference, and LLM-as-a-judge. See more details in this paper and leaderboard.
Difference between different versions of Vicuna
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