VLLM

LiteLLM supports all models on VLLM.

🚀Code Tutorial

info

To call a HOSTED VLLM Endpoint use these docs

Quick Start

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)

Calling hosted VLLM Server

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

• custom_llm_provider == "openai"
• api_base = "your-hosted-vllm-server"

import litellm 

response = litellm.completion(
            model="openai/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)

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 Name	Works for Models	Function Call
meta-llama/Llama-2-7b-chat	All meta-llama llama2 chat models	completion(model='vllm/meta-llama/Llama-2-7b', messages=messages, api_base="your_api_endpoint")
tiiuae/falcon-7b-instruct	All falcon instruct models	completion(model='vllm/tiiuae/falcon-7b-instruct', messages=messages, api_base="your_api_endpoint")
mosaicml/mpt-7b-chat	All mpt chat models	completion(model='vllm/mosaicml/mpt-7b-chat', messages=messages, api_base="your_api_endpoint")
codellama/CodeLlama-34b-Instruct-hf	All codellama instruct models	completion(model='vllm/codellama/CodeLlama-34b-Instruct-hf', messages=messages, api_base="your_api_endpoint")
WizardLM/WizardCoder-Python-34B-V1.0	All wizardcoder models	completion(model='vllm/WizardLM/WizardCoder-Python-34B-V1.0', messages=messages, api_base="your_api_endpoint")
Phind/Phind-CodeLlama-34B-v2	All phind-codellama models	completion(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