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FastChat is a new chatbot development and evaluation platform that seeks to provide a standardized and scalable method of generating and testing conversational bots.
FastChat is a platform that seeks to simplify and speed up the process of developing and evaluating chatbots. With chatbots gaining popularity and usefulness in areas like customer service, education, entertainment, and health care, creating and testing chatbots can be a challenging and resource-intensive task. In this article, we’ll introduce you to FastChat, a new platform that streamlines the chatbot development and evaluation process.
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FastChat provides a user-friendly interface that allows users to create, test, and deploy chatbots in minutes. FastChat also offers a rich set of features, such as natural language understanding, dialogue management, response generation, and analytics. FastChat enables users to build chatbots for different purposes and scenarios, such as conversational agents, question answering systems, task-oriented bots, and social chatbots. FastChat supports multiple languages and platforms, such as web, mobile, and voice. FastChat is designed to help users create high-quality chatbots that can engage and satisfy their target audiences.
Clone this repository and navigate to the FastChat folder.
git clone https://github.com/lm-sys/FastChat.git
If you are running on Mac:
brew install rust cmake
2. Install Package
pip3 install --upgrade pip # enable PEP 660 support
pip3 install -e .
To comply with the LLaMA model licence, they provide Vicuna weights as delta weights. To acquire the Vicuna weights, add delta to the original LLaMA weights.
Get the original LLaMA weights in the huggingface format by following the instructions here.
Apply our delta to the scripts below to acquire Vicuna weights. They will download delta weights from our Hugging Face account automatically.
Weights v1.1 are only compatible with transformers>=4.28.0 and fschat >= 0.2.0. Please update your local packages as needed. If you use the above instructions to perform a clean install, you should obtain all of the right versions.
This conversion command requires around 30 GB of CPU RAM. If you don’t have enough memory, see the “Low CPU Memory Conversion” section below.
Vicuna-7B can run on a 32GB M1 Macbook with 1 – 2 words / second.
Not Enough Memory
If you don’t have enough RAM, you may use 8-bit compression by adding –load-8bit to the preceding instructions. With somewhat reduced model quality, this can cut memory consumption in half. It works with the CPU, GPU, and Metal backends. Vicuna-13B can operate on a single NVIDIA 3090/4080/T4/V100(16GB) GPU with 8-bit compression.
Additionally, you may use --cpu-offloading to the aforementioned instructions to unload weights that do not fit on your GPU to CPU memory. This necessitates the activation of 8-bit compression and the installation of the bitsandbytes package, which is only accessible on Linux operating systems.
MLC LLM, backed by TVM Unity compiler, deploys Vicuna natively on phones, consumer-class GPUs and web browsers via Vulkan, Metal, CUDA and WebGPU.
Serving with Web GUI
You’ll need three major components to serve utilizing the web UI: web servers that interact with users, model workers that host one or more models, and a controller to synchronize the webserver and model workers. The following commands should be entered into your terminal:
Wait until the model loading procedure is complete and you notice “Uvicorn running on…”. You can launch numerous model workers at the same time to service various models. The model worker will immediately connect to the controller.
Send a test message using the following command to confirm that your model worker is correctly linked to your controller:
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