---
license: llama2
---
<img src="https://huggingface.co/CyberNative/CyberBase/resolve/main/image.png" alt="CyberNative/CyberBase"/>

## THIS IS A PLACEHOLDER, MODEL COMMING SOON

CyberBase is an experimental *base model* for cybersecurity. (llama-2-13b -> lmsys/vicuna-13b-v1.5-16k -> CyberBase)

## Test run 1 (less context, more trainable params):
- sequence_len: 4096
- max_packed_sequence_len: 4096
- lora_r: 256
- lora_alpha: 128
- num_epochs: 3
- trainable params: 1,001,390,080 || all params: 14,017,264,640 || trainable%: 7.143976415643959

# 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](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.
- It might, therefore, inherit [promp template of FastChat](https://github.com/lm-sys/FastChat/blob/main/docs/vicuna_weights_version.md#prompt-template)
- **sequence_len:** 8192
- **lora_r:** 128
- **lora_alpha:** 16
- **num_epochs:** 3
- **gradient_accumulation_steps:** 2
- **micro_batch_size:** 1
- **flash_attention:** true (FlashAttention-2)

[<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)

# 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](https://lmsys.org/)
- **Model type:** An auto-regressive language model based on the transformer architecture
- **License:** Llama 2 Community License Agreement	
- **Finetuned from model:** [Llama 2](https://arxiv.org/abs/2307.09288)

### 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](https://arxiv.org/pdf/2306.05685.pdf).

## Evaluation

![Evaluation Results](https://github.com/lm-sys/lm-sys.github.io/blob/main/public/images/webdata/vicuna_v1.5_eval.png?raw=true)

Vicuna is evaluated with standard benchmarks, human preference, and LLM-as-a-judge. See more details in this [paper](https://arxiv.org/pdf/2306.05685.pdf) and [leaderboard](https://huggingface.co/spaces/lmsys/chatbot-arena-leaderboard).

## Difference between different versions of Vicuna

See [vicuna_weights_version.md](https://github.com/lm-sys/FastChat/blob/main/docs/vicuna_weights_version.md)