---
license: llama2
language:
- en
pipeline_tag: text-generation
tags:
- code
- code llama
---
# **Rift Coder 7B**
## Table of Contents
1. **Model Summary**
2. **Uses**
3. **Installation and Getting Started**
4. **Contact**
5. **Ethical Considerations & Limitations**
# **Model Summary**
**Rift Coder 7B** is a finetune of **Glaive AI's** model **glaive-coder-7b**. We trained on a representative collection of indexed repositories for Python and TypeScript.
Rift Coder 7B is offered in 4-bit and 8-bit quantization.
This model is designed to excel at contextual code editing while maintaining the ability to run on your device. It works best in **Morph Labs'** VS Code extension, **Rift**, with our Code Editing agent.
**Rift (GitHub link)** was built to be open-source, private, secure, and on-device. **Rift Coder 7B** helps make that possible, as it is designed to be run on-device.
- **Developed by:** **Morph Labs**
- **Language(s) (NLP):** English. Fine-Tuned on Python & TypeScript.
- **License:** **Llama2**
- **Model Type:** Derivative of Fine-Tuned Model ultimately based off of **CodeLlama-7b-hf**
## Uses
We suggest usage of the model when working with Python or TypeScript, as our fine-tuning occurred with those contexts in mind. The model may find utility in other domains.
## Installation and Getting Started
1) Download **Rift**
2) Preferences: Open User Settings
3) Navigate to Rift in User Settings (under Extensions)
4) Change the Rift: Code Edit Model selection to llama:rift-coder-v0-7b-gguf
5) This will commence the download. When the download is complete, you’re ready to use Rift Coder 7B 🤗
## Contact
**Contact Form**
## Ethical Considerations and Limitations
Rift Coder 7B, as with all Large Language Models, carries inherent risks with use. Testing has been solely conducted in English, and our testing has not been fully comprehensive nor could be fully comprehensive of all use scenarios. The model may be prone to producing inaccurate, unsatisfactory, or otherwise undesirable outputs, and thus we encourage all developers to test and tune to their specific use case prior to deployment.
We encourage you to check out **[Meta's Responsible Use Guide for Llama 2](https://ai.meta.com/llama/responsible-user-guide)**.