langchain/docs/versioned_docs/version-0.2.x/integrations/providers/predibase.md

Predibase

Learn how to use LangChain with models on Predibase.

Setup

• Create a Predibase account and API key.
• Install the Predibase Python client with pip install predibase
• Use your API key to authenticate

LLM

Predibase integrates with LangChain by implementing LLM module. You can see a short example below or a full notebook under LLM > Integrations > Predibase.

import os
os.environ["PREDIBASE_API_TOKEN"] = "{PREDIBASE_API_TOKEN}"

from langchain_community.llms import Predibase

model = Predibase(model="mistral-7b"", predibase_api_key=os.environ.get("PREDIBASE_API_TOKEN"))

response = model("Can you recommend me a nice dry wine?")
print(response)

Predibase also supports Predibase-hosted and HuggingFace-hosted adapters that are fine-tuned on the base model given by the model argument:

import os
os.environ["PREDIBASE_API_TOKEN"] = "{PREDIBASE_API_TOKEN}"

from langchain_community.llms import Predibase

# The fine-tuned adapter is hosted at Predibase (adapter_version can be specified; omitting it is equivalent to the most recent version).
model =  Predibase(model="mistral-7b"", adapter_id="e2e_nlg", adapter_version=1, predibase_api_key=os.environ.get("PREDIBASE_API_TOKEN"))

response = model("Can you recommend me a nice dry wine?")
print(response)

Predibase also supports adapters that are fine-tuned on the base model given by the model argument:

import os
os.environ["PREDIBASE_API_TOKEN"] = "{PREDIBASE_API_TOKEN}"

from langchain_community.llms import Predibase

# The fine-tuned adapter is hosted at HuggingFace (adapter_version does not apply and will be ignored).
model =  Predibase(model="mistral-7b"", adapter_id="predibase/e2e_nlg", predibase_api_key=os.environ.get("PREDIBASE_API_TOKEN"))

response = model("Can you recommend me a nice dry wine?")
print(response)