Replicate

LiteLLM supports all models on Replicate

Usage

SDK

API KEYS

import os 
os.environ["REPLICATE_API_KEY"] = ""

Example Call

from litellm import completion
import os
## set ENV variables
os.environ["REPLICATE_API_KEY"] = "replicate key"

# replicate llama-3 call
response = completion(
    model="replicate/meta/meta-llama-3-8b-instruct", 
    messages = [{ "content": "Hello, how are you?","role": "user"}]
)

PROXY

1. Add models to your config.yaml

   model_list:
     - model_name: llama-3
       litellm_params:
         model: replicate/meta/meta-llama-3-8b-instruct
         api_key: os.environ/REPLICATE_API_KEY

2. Start the proxy

   $ litellm --config /path/to/config.yaml --debug

3. Send Request to LiteLLM Proxy Server

   OpenAI Python v1.0.0+

   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="llama-3",
       messages = [
         {
             "role": "system",
             "content": "Be a good human!"
         },
         {
             "role": "user",
             "content": "What do you know about earth?"
         }
     ]
   )
   
   print(response)

   curl

   curl --location 'http://0.0.0.0:4000/chat/completions' \
       --header 'Authorization: Bearer sk-1234' \
       --header 'Content-Type: application/json' \
       --data '{
       "model": "llama-3",
       "messages": [
         {
             "role": "system",
             "content": "Be a good human!"
         },
         {
             "role": "user",
             "content": "What do you know about earth?"
         }
         ],
   }'

Expected Replicate Call

This is the call litellm will make to replicate, from the above example:

POST Request Sent from LiteLLM:
curl -X POST \
https://api.replicate.com/v1/models/meta/meta-llama-3-8b-instruct \
-H 'Authorization: Token your-api-key' -H 'Content-Type: application/json' \
-d '{'version': 'meta/meta-llama-3-8b-instruct', 'input': {'prompt': '<|start_header_id|>system<|end_header_id|>\n\nBe a good human!<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nWhat do you know about earth?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n'}}'

Advanced Usage - Prompt Formatting

LiteLLM has prompt template mappings for all meta-llama llama3 instruct models. See Code

To apply a custom prompt template:

SDK

import litellm

import os 
os.environ["REPLICATE_API_KEY"] = ""

# Create your own custom prompt template 
litellm.register_prompt_template(
        model="togethercomputer/LLaMA-2-7B-32K",
        initial_prompt_value="You are a good assistant" # [OPTIONAL]
        roles={
            "system": {
                "pre_message": "[INST] <<SYS>>\n", # [OPTIONAL]
                "post_message": "\n<</SYS>>\n [/INST]\n" # [OPTIONAL]
            },
            "user": { 
                "pre_message": "[INST] ", # [OPTIONAL]
                "post_message": " [/INST]" # [OPTIONAL]
            }, 
            "assistant": {
                "pre_message": "\n" # [OPTIONAL]
                "post_message": "\n" # [OPTIONAL]
            }
        }
        final_prompt_value="Now answer as best you can:" # [OPTIONAL]
)

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

test_replicate_custom_model()

PROXY

# Model-specific parameters
model_list:
  - model_name: mistral-7b # model alias
    litellm_params: # actual params for litellm.completion()
      model: "replicate/mistralai/Mistral-7B-Instruct-v0.1" 
      api_key: os.environ/REPLICATE_API_KEY
      initial_prompt_value: "\n"
      roles: {"system":{"pre_message":"<|im_start|>system\n", "post_message":"<|im_end|>"}, "assistant":{"pre_message":"<|im_start|>assistant\n","post_message":"<|im_end|>"}, "user":{"pre_message":"<|im_start|>user\n","post_message":"<|im_end|>"}}
      final_prompt_value: "\n"
      bos_token: "<s>"
      eos_token: "</s>"
      max_tokens: 4096

Advanced Usage - Calling Replicate Deployments

Calling a deployed replicate LLM Add the replicate/deployments/ prefix to your model, so litellm will call the deployments endpoint. This will call ishaan-jaff/ishaan-mistral deployment on replicate

response = completion(
    model="replicate/deployments/ishaan-jaff/ishaan-mistral", 
    messages= [{ "content": "Hello, how are you?","role": "user"}]
)

Replicate Cold Boots

Replicate responses can take 3-5 mins due to replicate cold boots, if you're trying to debug try making the request with litellm.set_verbose=True. More info on replicate cold boots

Replicate Models

liteLLM supports all replicate LLMs

For replicate models ensure to add a replicate/ prefix to the model arg. liteLLM detects it using this arg.

Below are examples on how to call replicate LLMs using liteLLM

Model Name	Function Call	Required OS Variables
replicate/llama-2-70b-chat	completion(model='replicate/llama-2-70b-chat:2796ee9483c3fd7aa2e171d38f4ca12251a30609463dcfd4cd76703f22e96cdf', messages)	os.environ['REPLICATE_API_KEY']
a16z-infra/llama-2-13b-chat	completion(model='replicate/a16z-infra/llama-2-13b-chat:2a7f981751ec7fdf87b5b91ad4db53683a98082e9ff7bfd12c8cd5ea85980a52', messages)	os.environ['REPLICATE_API_KEY']
replicate/vicuna-13b	completion(model='replicate/vicuna-13b:6282abe6a492de4145d7bb601023762212f9ddbbe78278bd6771c8b3b2f2a13b', messages)	os.environ['REPLICATE_API_KEY']
daanelson/flan-t5-large	completion(model='replicate/daanelson/flan-t5-large:ce962b3f6792a57074a601d3979db5839697add2e4e02696b3ced4c022d4767f', messages)	os.environ['REPLICATE_API_KEY']
custom-llm	completion(model='replicate/custom-llm-version-id', messages)	os.environ['REPLICATE_API_KEY']
replicate deployment	completion(model='replicate/deployments/ishaan-jaff/ishaan-mistral', messages)	os.environ['REPLICATE_API_KEY']

Passing additional params - max_tokens, temperature

See all litellm.completion supported params here

# !pip install litellm
from litellm import completion
import os
## set ENV variables
os.environ["REPLICATE_API_KEY"] = "replicate key"

# replicate llama-2 call
response = completion(
    model="replicate/llama-2-70b-chat:2796ee9483c3fd7aa2e171d38f4ca12251a30609463dcfd4cd76703f22e96cdf", 
    messages = [{ "content": "Hello, how are you?","role": "user"}],
    max_tokens=20,
    temperature=0.5
)

proxy

  model_list:
    - model_name: llama-3
      litellm_params:
        model: replicate/meta/meta-llama-3-8b-instruct
        api_key: os.environ/REPLICATE_API_KEY
        max_tokens: 20
        temperature: 0.5

Passings Replicate specific params

Send params not supported by litellm.completion() but supported by Replicate by passing them to litellm.completion

Example seed, min_tokens are Replicate specific param

# !pip install litellm
from litellm import completion
import os
## set ENV variables
os.environ["REPLICATE_API_KEY"] = "replicate key"

# replicate llama-2 call
response = completion(
    model="replicate/llama-2-70b-chat:2796ee9483c3fd7aa2e171d38f4ca12251a30609463dcfd4cd76703f22e96cdf", 
    messages = [{ "content": "Hello, how are you?","role": "user"}],
    seed=-1,
    min_tokens=2,
    top_k=20,
)

proxy

  model_list:
    - model_name: llama-3
      litellm_params:
        model: replicate/meta/meta-llama-3-8b-instruct
        api_key: os.environ/REPLICATE_API_KEY
        min_tokens: 2
        top_k: 20