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Vicuna: An LLM Chatbot Impressing GPT-4 with 90% ChatGPT Quality

Vicuna LLM

This README contains instructions to run and train Vicuna, an open-source LLM chatbot with quality comparable to ChatGPT. The Vicuna release was trained using SkyPilot on cloud spot instances, with a cost of ~$300.

Prerequisites

Install the latest SkyPilot and check your setup of the cloud credentials:

pip install git+https://github.com/skypilot-org/skypilot.git
sky check

See the Vicuna SkyPilot YAMLs: for training and for serving.

Serve the official Vicuna model by yourself with SkyPilot

  1. Start serving the Vicuna-7B model on a single A100 GPU:
sky launch -c vicuna-serve -s serve.yaml
  1. Check the output of the command. There will be a sharable gradio link (like the last line of the following). Open it in your browser to chat with Vicuna.
(task, pid=20933) 2023-04-12 22:08:49 | INFO | gradio_web_server | Namespace(host='0.0.0.0', port=None, controller_url='http://localhost:21001', concurrency_count=10, model_list_mode='once', share=True, moderate=False)
(task, pid=20933) 2023-04-12 22:08:49 | INFO | stdout | Running on local URL:  http://0.0.0.0:7860
(task, pid=20933) 2023-04-12 22:08:51 | INFO | stdout | Running on public URL: https://<random-hash>.gradio.live
  1. [Optional] Try other GPUs:
sky launch -c vicuna-serve-v100 -s serve.yaml --gpus V100
  1. [Optional] Serve the 13B model instead of the default 7B:
sky launch -c vicuna-serve -s serve.yaml --env MODEL_SIZE=13
  1. [Optional] Serve the OpenAI API Compatible Endpoint:
sky launch -c vicuna-openai-api -s serve-openai-api-endpoint.yaml

Training Vicuna with SkyPilot

Currently, training requires GPUs with 80GB memory. See sky show-gpus --all for supported GPUs.

We can start the training of Vicuna model on the dummy data dummy.json1 with a single command. It will automatically find the available cheapest VM on any cloud.

To train on your own data, replace the file with your own, or change the line /data/mydata.json: ./dummy.json to the path of your own data in the train.yaml.

Steps for training on your cloud(s):

  1. Replace the bucket name in train.yaml with some unique name, so the SkyPilot can create a bucket for you to store the model weights. See # Change to your own bucket in the YAML file.

  2. Training the Vicuna-7B model on 8 A100 GPUs (80GB memory) using spot instances:

# Launch it on managed spot to save 3x cost
sky spot launch -n vicuna train.yaml

Note: if you would like to see the training curve on W&B, you can add --env WANDB_API_KEY to the above command, which will propagate your local W&B API key in the environment variable to the job.

[Optional] Train a larger 13B model

# Train a 13B model instead of the default 7B
sky spot launch -n vicuna-7b train.yaml --env MODEL_SIZE=13

# Use *unmanaged* spot instances (i.e., preemptions won't get auto-recovered).
# Unmanaged spot provides a better interactive development experience but is vulnerable to spot preemptions.
# We recommend using managed spot as above.
sky launch -c vicuna train.yaml

Currently, such A100-80GB:8 spot instances are only available on AWS and GCP.

[Optional] To use on-demand A100-80GB:8 instances, which are currently available on Lambda Cloud, Azure, and GCP:

sky launch -c vicuna -s train.yaml --no-use-spot

Q&A

Q: I see some bucket permission errors sky.exceptions.StorageBucketGetError when running the above:

...
sky.exceptions.StorageBucketGetError: Failed to connect to an existing bucket 'YOUR_OWN_BUCKET_NAME'.
Please check if:
  1. the bucket name is taken and/or
  2. the bucket permissions are not setup correctly. To debug, consider using gsutil ls gs://YOUR_OWN_BUCKET_NAME.

A: You need to replace the bucket name with your own globally unique name, and rerun the commands. New private buckets will be automatically created under your cloud account.

Footnotes

  1. The dummy data was originally from the official Vicuna repository, FastChat. ↩