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@@ -3,28 +3,31 @@ license: llama2
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  ---
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  <img src="https://huggingface.co/CyberNative/CyberBase/resolve/main/image.png" alt="CyberNative/CyberBase"/>
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- ## THIS IS A PLACEHOLDER, MODEL COMMING SOON
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-
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  CyberBase is an experimental *base model* for cybersecurity. (llama-2-13b -> lmsys/vicuna-13b-v1.5-16k -> CyberBase)
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- ## Test run 1 (less context, more trainable params):
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- - sequence_len: 4096
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- - max_packed_sequence_len: 4096
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- - lora_r: 256
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- - lora_alpha: 128
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- - num_epochs: 3
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- - trainable params: 1,001,390,080 || all params: 14,017,264,640 || trainable%: 7.143976415643959
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-
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  # Base cybersecurity model for future fine-tuning, it is not recomended to use on it's own.
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  - **CyberBase** is a [lmsys/vicuna-13b-v1.5-16k](https://huggingface.co/lmsys/vicuna-13b-v1.5-16k) QLORA fine-tuned on [CyberNative/github_cybersecurity_READMEs](https://huggingface.co/datasets/CyberNative/github_cybersecurity_READMEs) with a single 3090.
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  - It might, therefore, inherit [promp template of FastChat](https://github.com/lm-sys/FastChat/blob/main/docs/vicuna_weights_version.md#prompt-template)
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- - **sequence_len:** 8192
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- - **lora_r:** 128
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- - **lora_alpha:** 16
 
 
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  - **num_epochs:** 3
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  - **gradient_accumulation_steps:** 2
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  - **micro_batch_size:** 1
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  - **flash_attention:** true (FlashAttention-2)
 
 
 
 
 
 
 
 
 
 
 
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  [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
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  ---
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  <img src="https://huggingface.co/CyberNative/CyberBase/resolve/main/image.png" alt="CyberNative/CyberBase"/>
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  CyberBase is an experimental *base model* for cybersecurity. (llama-2-13b -> lmsys/vicuna-13b-v1.5-16k -> CyberBase)
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  # Base cybersecurity model for future fine-tuning, it is not recomended to use on it's own.
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  - **CyberBase** is a [lmsys/vicuna-13b-v1.5-16k](https://huggingface.co/lmsys/vicuna-13b-v1.5-16k) QLORA fine-tuned on [CyberNative/github_cybersecurity_READMEs](https://huggingface.co/datasets/CyberNative/github_cybersecurity_READMEs) with a single 3090.
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  - It might, therefore, inherit [promp template of FastChat](https://github.com/lm-sys/FastChat/blob/main/docs/vicuna_weights_version.md#prompt-template)
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+
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+ ## Fine-tuning information
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+ - **sequence_len:** 4096 (used during fine-tuning, but should generate up to 16k)
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+ - **lora_r:** 256
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+ - **lora_alpha:** 128
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  - **num_epochs:** 3
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  - **gradient_accumulation_steps:** 2
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  - **micro_batch_size:** 1
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  - **flash_attention:** true (FlashAttention-2)
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+ - trainable params: 1,001,390,080 || all params: 14,017,264,640 || trainable%: 7.143976415643959
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+
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+ ### Tested with the following prompt and temperature=0.3:
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+ <code>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.<br>
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+ <br>
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+ USER: Hello! I need help with a penetration test.<br>
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+ ASSISTANT: Hello! I'd be happy to help you with your penetration test. What specifically do you need help with?<br>
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+ USER: Write me a plan for a penetration test. It should include first 5 steps and commands for each step.<br>
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+ ASSISTANT:<br></code>
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+
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+ Join the discussion > https://cybernative.ai/t/cyberbase-devlog/1734
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  [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
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