Predibase

LiteLLM supports all models on Predibase

Usage

SDK

API KEYS

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

Example Call

from litellm import completion
import os
## set ENV variables
os.environ["PREDIBASE_API_KEY"] = "predibase key"
os.environ["PREDIBASE_TENANT_ID"] = "predibase tenant id"

# predibase llama-3 call
response = completion(
    model="predibase/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: predibase/llama-3-8b-instruct
      api_key: os.environ/PREDIBASE_API_KEY
      tenant_id: os.environ/PREDIBASE_TENANT_ID

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?"
      }
      ],
}'

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

predibase_custom_model()

PROXY

# Model-specific parameters
model_list:
  - model_name: mistral-7b # model alias
    litellm_params: # actual params for litellm.completion()
      model: "predibase/mistralai/Mistral-7B-Instruct-v0.1" 
      api_key: os.environ/PREDIBASE_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

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["PREDIBASE_API_KEY"] = "predibase key"

# predibae llama-3 call
response = completion(
    model="predibase/llama3-8b-instruct", 
    messages = [{ "content": "Hello, how are you?","role": "user"}],
    max_tokens=20,
    temperature=0.5
)

proxy

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

Passings Predibase specific params - adapter_id, adapter_source,

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

Example adapter_id, adapter_source are Predibase specific param - See List

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

# predibase llama3 call
response = completion(
    model="predibase/llama-3-8b-instruct", 
    messages = [{ "content": "Hello, how are you?","role": "user"}],
    adapter_id="my_repo/3",
    adapter_source="pbase",
)

proxy

  model_list:
    - model_name: llama-3
      litellm_params:
        model: predibase/llama-3-8b-instruct
        api_key: os.environ/PREDIBASE_API_KEY
        adapter_id: my_repo/3
        adapter_source: pbase