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Showing content from https://python.langchain.com/docs/integrations/llms/predibase below:

Predibase | 🦜️🔗 LangChain

Predibase allows you to train, fine-tune, and deploy any ML model—from linear regression to large language model.

This example demonstrates using Langchain with models deployed on Predibase

To run this notebook, you'll need a Predibase account and an API key.

You'll also need to install the Predibase Python package:

%pip install --upgrade --quiet  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"),
)

from langchain_community.llms import Predibase


model = Predibase(
    model="mistral-7b",
    predibase_api_key=os.environ.get("PREDIBASE_API_TOKEN"),
    predibase_sdk_version=None,  
    adapter_id="e2e_nlg",
    adapter_version=1,
    **{
        "api_token": os.environ.get("HUGGING_FACE_HUB_TOKEN"),
        "max_new_tokens": 5,  
    },
)

from langchain_community.llms import Predibase


model = Predibase(
    model="mistral-7b",
    predibase_api_key=os.environ.get("PREDIBASE_API_TOKEN"),
    predibase_sdk_version=None,  
    adapter_id="predibase/e2e_nlg",
    **{
        "api_token": os.environ.get("HUGGING_FACE_HUB_TOKEN"),
        "max_new_tokens": 5,  
    },
)

response = model.invoke(
    "Can you recommend me a nice dry wine?",
    **{"temperature": 0.5, "max_new_tokens": 1024},
)
print(response)

from langchain_community.llms import Predibase

model = Predibase(
    model="mistral-7b",
    predibase_api_key=os.environ.get("PREDIBASE_API_TOKEN"),
    predibase_sdk_version=None,  
    **{
        "api_token": os.environ.get("HUGGING_FACE_HUB_TOKEN"),
        "max_new_tokens": 5,  
    },
)

model = Predibase(
    model="mistral-7b",
    predibase_api_key=os.environ.get("PREDIBASE_API_TOKEN"),
    predibase_sdk_version=None,  
    adapter_id="e2e_nlg",
    adapter_version=1,
    **{
        "api_token": os.environ.get("HUGGING_FACE_HUB_TOKEN"),
        "max_new_tokens": 5,  
    },
)

llm = Predibase(
    model="mistral-7b",
    predibase_api_key=os.environ.get("PREDIBASE_API_TOKEN"),
    predibase_sdk_version=None,  
    adapter_id="predibase/e2e_nlg",
    **{
        "api_token": os.environ.get("HUGGING_FACE_HUB_TOKEN"),
        "max_new_tokens": 5,  
    },
)

from langchain.chains import LLMChain
from langchain_core.prompts import PromptTemplate

template = """You are a playwright. Given the title of play, it is your job to write a synopsis for that title.

Title: {title}
Playwright: This is a synopsis for the above play:"""
prompt_template = PromptTemplate(input_variables=["title"], template=template)
synopsis_chain = LLMChain(llm=llm, prompt=prompt_template)

template = """You are a play critic from the New York Times. Given the synopsis of play, it is your job to write a review for that play.

Play Synopsis:
{synopsis}
Review from a New York Times play critic of the above play:"""
prompt_template = PromptTemplate(input_variables=["synopsis"], template=template)
review_chain = LLMChain(llm=llm, prompt=prompt_template)

from langchain.chains import SimpleSequentialChain

overall_chain = SimpleSequentialChain(
    chains=[synopsis_chain, review_chain], verbose=True
)

review = overall_chain.run("Tragedy at sunset on the beach")

from langchain_community.llms import Predibase

model = Predibase(
    model="my-base-LLM",
    predibase_api_key=os.environ.get(
        "PREDIBASE_API_TOKEN"
    ),  
    predibase_sdk_version=None,  
    adapter_id="my-finetuned-adapter-id",  
    adapter_version=1,  
    **{
        "api_token": os.environ.get("HUGGING_FACE_HUB_TOKEN"),
        "max_new_tokens": 5,  
    },
)

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