Rain-2x7B-MoE-32k-v0.1

Model creator: yuuko-eth
Original model: Rain-2x7B-MoE-32k-v0.1

Description

This repo contains GGUF format model files for Rain-2x7B-MoE-32k-v0.1.

About GGUF

GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Here is an incomplete list of clients and libraries that are known to support GGUF:

llama.cpp. The source project for GGUF. Offers a CLI and a server option.
text-generation-webui, the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
KoboldCpp, a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
GPT4All, a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
LM Studio, an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
LoLLMS Web UI, a great web UI with many interesting and unique features, including a full model library for easy model selection.
Faraday.dev, an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
llama-cpp-python, a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
candle, a Rust ML framework with a focus on performance, including GPU support, and ease of use.
ctransformers, a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.

Original README.MD is as follows.

小雨同學 2x7B

採用聯發科 Breeze 7B Instruct 為基底的國語 MoE (Mixture-of-Experts) 模型，共有兩個 Expert model。

請用 Marcoro14-7B 或是 Breeze-7B-Instruct 所推薦的 Prompt 格式進行操作；以下為模型配置。

Rain-2x7B-MoE-32k-v0.1

This is an experimental Mixtral-architecture MoE model with 2 of 7B sized fine-tunes. Breeze and CodeNinja are used on top of Marcoro14-7B-slerp.

Model configuration is as follows:

To use the model, please use either prompt templates suggested by the base models.

Notes

Please evaluate before use in any application pipeline. Activation for coding part of the model would be 'code', 'python', 'typescript', 'javascript', 'programming', 'algorithm'.