---
license: apache-2.0
tags:
- finetuned
- Instruct
- code
datasets:
- Keynote-Technology/PLANE-2K
- togethercomputer/RedPajama-Data-V2
widget:
  - text: "The door closed as"
    output:
      text: "the last person left the room. There was a hush that settled over the space like a heavy blanket. The only sound was the soft ticking of the clock that stood sentinel in the center of the room.

Drake had been summoned by his employer, a man whose name he never spoke. He had been given no details about what was expected of him, only that he was to be there at midnight. Drake had arrived on time, dressed in his best suit and carrying a briefcase that contained all the information he had gathered during his investigation.

As he stood there, waiting for something to happen, he couldn't help but wonder what it was that had brought him here. He had been working on a case that had consumed him for months, a case that had taken him from the darkest corners of the city to the highest echelons of power. It was a case that had tested his skills as an investigator and as a human being."

---

![image/png](https://cdn-uploads.huggingface.co/production/uploads/6500c7c912c1442d994c36e5/3zCYNj35PnZs2_TcVZRvC.png)

## KAI-7B Instruct

KAI-7B-Instruct is a 7 Billion parameter causal model based on KAI-7B and Mistral-7B. KAI-7B has been finetuned on a mixture of chat/instruct datasets.

## Why use KAI-7B Instruct?

- KAI-7B is a strong base model, outperforming comparable open-source models (e.g., LLaMA 2 7b, Mistral 7B, e.t.c.).
- KAI-7B Instruct is trained on a framework for improving the instruction-following capabilities of pretrained language models.
- KAI-7B Instruct is a ready-to-use model based off KAI-7B, meaning it is easier to deploy.

## Banned Use

KAI-7B Instruct is governed by the [apache 2.0 liscense](https://choosealicense.com/licenses/apache-2.0/), and therefore means that whatever the license deems unacceptable shall not be allowed. We specificaly ban the use of  [ANY AND ALL KAI MODELS](https://huggingface.co/collections/Keynote-Technology/kai-large-language-models) for hate speech towards a paricular thing, person, our particular group due to [legal](https://www.ftc.gov/news-events/news/press-releases/2022/06/ftc-report-warns-about-using-artificial-intelligence-combat-online-problems) and ethical issues.