---
license: llama2
language:
- en
pipeline_tag: text-generation
tags:
- code
- code llama
---
# **rift-coder-v0-7b**
## Table of Contents
1. **Model Summary**
2. **Uses**
3. **Integrations**
4. **Installation and Getting Started**
5. **Training Data, Sources, Details & Procedure**
6. **Evaluation & Metrics**
7. **Contact**
8. **Ethical Considerations & Limitations**
# **Model Summary**
**rift-coder-v0-7b** is a finetune of **Glaive AI's** model **glaive-coder-7b**. We conducted the fine-tuning on a representative collection of indexed repositories for Python and TypeScript.
We use GGML to make rift-coder-v0-7b as accessible as possible on as much hardware as possible.
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**.
- **Developed by:** **Morph Labs**
- **Partnered with:** **Nomic AI** & **Together AI**
- **Language(s) (NLP):** English. Fine-Tuned on Python & TypeScript.
- **License:** **Llama2**
- **Model Type:** Derivative of Fine-Tuned Model ultimately trained on **CodeLlama-7b**
## 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.
## Integrations
We offer the ability to use rift-coder-v0-7b natively with the **Rift extension for VS Code**, our extension for enabling an AI-native language server. We designed **Rift** (**GitHub link**) to be open-source, private, secure, and on-device. **Rift-Coder-7B** helps make that possible.
## Installation and Getting Started
## Training Data, Sources, Details and Procedure
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Evaluation and Metrics
## Contact
## Ethical Considerations and Limitations
rift-coder-v0-7b, 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)**.