Self-host LLMs with LMDeploy and BentoML

This is a BentoML example project, showing you how to serve and deploy open-source Large Language Models (LLMs) using LMDeploy, a toolkit for compressing, deploying, and serving LLMs.

See here for a full list of BentoML example projects.

💡 This example is served as a basis for advanced code customization, such as custom model, inference logic or LMDeploy options. For simple LLM hosting with OpenAI compatible endpoint without writing any code, see OpenLLM.

Prerequisites

• You have installed Python 3.8+ and pip. See the Python downloads page to learn more.
• You have a basic understanding of key concepts in BentoML, such as Services. We recommend you read Quickstart first.
• If you want to test the Service locally, you need a Nvidia GPU with at least 20G VRAM.
• This example uses Llama 3 8B Instruct. Make sure you have gained access to the model.
• (Optional) We recommend you create a virtual environment for dependency isolation for this project. See the Conda documentation or the Python documentation for details.

Install dependencies

git clone https://github.com/bentoml/BentoLMDeploy.git
cd BentoLMDeploy/llama3-8b-instruct
pip install -r requirements.txt

Download the model

Run the script to download Llama 3.

python import_model.py

Run the BentoML Service

We have defined a BentoML Service in service.py. Run bentoml serve in your project directory to start the Service.

$ bentoml serve .

2024-05-04T17:24:01+0800 [INFO] [cli] Starting production HTTP BentoServer from "service:LMDeploy" listening on http://localhost:3000 (Press CTRL+C to quit)
2024-05-04 17:24:03,239 - lmdeploy - INFO - input backend=turbomind, backend_config=TurbomindEngineConfig(model_name='meta-llama/Meta-Llama-3-8B-Instruct', model_format='hf', tp=1, session_len=None, max_batch_size=128, cach
e_max_entry_count=0.9, cache_block_seq_len=64, quant_policy=0, rope_scaling_factor=0.0, use_logn_attn=False, download_dir=None, revision=None, max_prefill_token_num=8192, num_tokens_per_iter=0, max_prefill_iters=1)
2024-05-04 17:24:03,240 - lmdeploy - INFO - input chat_template_config=None
2024-05-04 17:24:03,339 - lmdeploy - INFO - updated chat_template_onfig=ChatTemplateConfig(model_name='llama3', system=None, meta_instruction=None, eosys=None, user=None, eoh=None, assistant=None, eoa=None, separator=None,
capability=None, stop_words=None)
2024-05-04 17:24:03,359 - lmdeploy - WARNING - model_source: hf_model
2024-05-04 17:24:03,359 - lmdeploy - WARNING - model_name is deprecated in TurbomindEngineConfig and has no effect
Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
2024-05-04 17:24:03,727 - lmdeploy - WARNING - model_config:

...

The server is now active at http://localhost:3000. You can interact with it using the Swagger UI or in other different ways.

CURL

curl -X 'POST' \
  'http://localhost:3000/generate' \
  -H 'accept: text/event-stream' \
  -H 'Content-Type: application/json' \
  -d '{
  "prompt": "Explain superconductors like I'\''m five years old",
  "max_tokens": 1024
}'

Python client

import bentoml

with bentoml.SyncHTTPClient("http://localhost:3000") as client:
    response_generator = client.generate(
        prompt="Explain superconductors like I'm five years old",
        max_tokens=1024
    )
    for response in response_generator:
        print(response, end='')

Deploy to BentoCloud

After the Service is ready, you can deploy the application to BentoCloud for better management and scalability. Sign up if you haven't got a BentoCloud account.

Make sure you have logged in to BentoCloud, then run the following command to deploy it.

bentoml deploy .

Once the application is up and running on BentoCloud, you can access it via the exposed URL.

Note: For custom deployment in your own infrastructure, use BentoML to generate an OCI-compliant image.