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VLLM

LiteLLM supports all models on VLLM.

Quick Start

Usage - litellm.completion (calling vLLM endpoint)​

vLLM Provides an OpenAI compatible endpoints - here's how to call it with LiteLLM

In order to use litellm to call a hosted vllm server add the following to your completion call

  • model="hosted_vllm/<your-vllm-model-name>"
  • api_base = "your-hosted-vllm-server"
import litellm 

response = litellm.completion(
model="hosted_vllm/facebook/opt-125m", # pass the vllm model name
messages=messages,
api_base="https://hosted-vllm-api.co",
temperature=0.2,
max_tokens=80)

print(response)

Usage - LiteLLM Proxy Server (calling vLLM endpoint)​

Here's how to call an OpenAI-Compatible Endpoint with the LiteLLM Proxy Server

  1. Modify the config.yaml

    model_list:
    - model_name: my-model
    litellm_params:
    model: hosted_vllm/facebook/opt-125m # add hosted_vllm/ prefix to route as OpenAI provider
    api_base: https://hosted-vllm-api.co # add api base for OpenAI compatible provider
  2. Start the proxy

    $ litellm --config /path/to/config.yaml
  3. Send Request to LiteLLM Proxy Server

    import openai
    client = openai.OpenAI(
    api_key="sk-1234", # pass litellm proxy key, if you're using virtual keys
    base_url="http://0.0.0.0:4000" # litellm-proxy-base url
    )

    response = client.chat.completions.create(
    model="my-model",
    messages = [
    {
    "role": "user",
    "content": "what llm are you"
    }
    ],
    )

    print(response)

Extras - for vllm pip package​

Using - litellm.completion​

pip install litellm vllm
import litellm 

response = litellm.completion(
model="vllm/facebook/opt-125m", # add a vllm prefix so litellm knows the custom_llm_provider==vllm
messages=messages,
temperature=0.2,
max_tokens=80)

print(response)

Batch Completion​

from litellm import batch_completion

model_name = "facebook/opt-125m"
provider = "vllm"
messages = [[{"role": "user", "content": "Hey, how's it going"}] for _ in range(5)]

response_list = batch_completion(
model=model_name,
custom_llm_provider=provider, # can easily switch to huggingface, replicate, together ai, sagemaker, etc.
messages=messages,
temperature=0.2,
max_tokens=80,
)
print(response_list)

Prompt Templates​

For models with special prompt templates (e.g. Llama2), we format the prompt to fit their template.

What if we don't support a model you need? You can also specify you're own custom prompt formatting, in case we don't have your model covered yet.

Does this mean you have to specify a prompt for all models? No. By default we'll concatenate your message content to make a prompt (expected format for Bloom, T-5, Llama-2 base models, etc.)

Default Prompt Template

def default_pt(messages):
return " ".join(message["content"] for message in messages)

Code for how prompt templates work in LiteLLM

Models we already have Prompt Templates for​

Model NameWorks for ModelsFunction Call
meta-llama/Llama-2-7b-chatAll meta-llama llama2 chat modelscompletion(model='vllm/meta-llama/Llama-2-7b', messages=messages, api_base="your_api_endpoint")
tiiuae/falcon-7b-instructAll falcon instruct modelscompletion(model='vllm/tiiuae/falcon-7b-instruct', messages=messages, api_base="your_api_endpoint")
mosaicml/mpt-7b-chatAll mpt chat modelscompletion(model='vllm/mosaicml/mpt-7b-chat', messages=messages, api_base="your_api_endpoint")
codellama/CodeLlama-34b-Instruct-hfAll codellama instruct modelscompletion(model='vllm/codellama/CodeLlama-34b-Instruct-hf', messages=messages, api_base="your_api_endpoint")
WizardLM/WizardCoder-Python-34B-V1.0All wizardcoder modelscompletion(model='vllm/WizardLM/WizardCoder-Python-34B-V1.0', messages=messages, api_base="your_api_endpoint")
Phind/Phind-CodeLlama-34B-v2All phind-codellama modelscompletion(model='vllm/Phind/Phind-CodeLlama-34B-v2', messages=messages, api_base="your_api_endpoint")

Custom prompt templates​

# Create your own custom prompt template works 
litellm.register_prompt_template(
model="togethercomputer/LLaMA-2-7B-32K",
roles={
"system": {
"pre_message": "[INST] <<SYS>>\n",
"post_message": "\n<</SYS>>\n [/INST]\n"
},
"user": {
"pre_message": "[INST] ",
"post_message": " [/INST]\n"
},
"assistant": {
"pre_message": "\n",
"post_message": "\n",
}
} # tell LiteLLM how you want to map the openai messages to this model
)

def test_vllm_custom_model():
model = "vllm/togethercomputer/LLaMA-2-7B-32K"
response = completion(model=model, messages=messages)
print(response['choices'][0]['message']['content'])
return response

test_vllm_custom_model()

Implementation Code