| | --- |
| | language: |
| | - en |
| | license: mit |
| | tags: |
| | - pretrained |
| | - security |
| | - redteam |
| | - blueteam |
| | pipeline_tag: text-generation |
| | inference: |
| | parameters: |
| | temperature: 0.7 |
| | extra_gated_description: >- |
| | If you want to learn more about how we process your personal data, please read |
| | our <a href="https://mistral.ai/terms/">Privacy Policy</a>. |
| | --- |
| | |
| | # TylerG01/Indigo-v0.1 |
| | Refer to the [original model card](https://huggingface.co/mistralai/Mistral-7B-v0.1) for more details on the model. |
| | ## Project Goals |
| | This is v0.1 (alpha) release of the Indigo LLM project, which used LoRA Fine-Tuning to train Mistral 7B on more than 400 books, pamphlets, |
| | training documents, code snippets and other works in the cyber security field, openly sourced on the surface web. This version used 16 LoRA layers |
| | and had a val loss of 1.601 after the 4th training epoch. However, my goal for the LoRA version of this model is to produce a val loss of <1.51 after |
| | some modification to the dataset and training approach. |
| |
|
| | For more information on this project, check out the blog post at https://t2-security.com/indigo-llm-503cd6e22fe4. |
| |
|
