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Showing content from https://github.com/bentoml/BentoLMDeploy below:

bentoml/BentoLMDeploy: 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.

• 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.

git clone https://github.com/bentoml/BentoLMDeploy.git
cd BentoLMDeploy/llama3.1-8b-instruct
pip install -r requirements.txt

Run the script to download Llama 3.

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 -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
}'

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='')

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.

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.

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