mirror of
https://github.com/hwchase17/langchain
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…tch]: import models from community ran ```bash git grep -l 'from langchain\.chat_models' | xargs -L 1 sed -i '' "s/from\ langchain\.chat_models/from\ langchain_community.chat_models/g" git grep -l 'from langchain\.llms' | xargs -L 1 sed -i '' "s/from\ langchain\.llms/from\ langchain_community.llms/g" git grep -l 'from langchain\.embeddings' | xargs -L 1 sed -i '' "s/from\ langchain\.embeddings/from\ langchain_community.embeddings/g" git checkout master libs/langchain/tests/unit_tests/llms git checkout master libs/langchain/tests/unit_tests/chat_models git checkout master libs/langchain/tests/unit_tests/embeddings/test_imports.py make format cd libs/langchain; make format cd ../experimental; make format cd ../core; make format ```
96 lines
2.6 KiB
Plaintext
96 lines
2.6 KiB
Plaintext
# Modal
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This page covers how to use the Modal ecosystem to run LangChain custom LLMs.
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It is broken into two parts:
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1. Modal installation and web endpoint deployment
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2. Using deployed web endpoint with `LLM` wrapper class.
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## Installation and Setup
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- Install with `pip install modal`
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- Run `modal token new`
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## Define your Modal Functions and Webhooks
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You must include a prompt. There is a rigid response structure:
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```python
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class Item(BaseModel):
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prompt: str
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@stub.function()
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@modal.web_endpoint(method="POST")
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def get_text(item: Item):
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return {"prompt": run_gpt2.call(item.prompt)}
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```
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The following is an example with the GPT2 model:
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```python
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from pydantic import BaseModel
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import modal
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CACHE_PATH = "/root/model_cache"
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class Item(BaseModel):
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prompt: str
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stub = modal.Stub(name="example-get-started-with-langchain")
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def download_model():
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from transformers import GPT2Tokenizer, GPT2LMHeadModel
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tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
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model = GPT2LMHeadModel.from_pretrained('gpt2')
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tokenizer.save_pretrained(CACHE_PATH)
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model.save_pretrained(CACHE_PATH)
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# Define a container image for the LLM function below, which
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# downloads and stores the GPT-2 model.
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image = modal.Image.debian_slim().pip_install(
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"tokenizers", "transformers", "torch", "accelerate"
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).run_function(download_model)
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@stub.function(
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gpu="any",
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image=image,
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retries=3,
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)
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def run_gpt2(text: str):
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from transformers import GPT2Tokenizer, GPT2LMHeadModel
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tokenizer = GPT2Tokenizer.from_pretrained(CACHE_PATH)
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model = GPT2LMHeadModel.from_pretrained(CACHE_PATH)
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encoded_input = tokenizer(text, return_tensors='pt').input_ids
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output = model.generate(encoded_input, max_length=50, do_sample=True)
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return tokenizer.decode(output[0], skip_special_tokens=True)
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@stub.function()
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@modal.web_endpoint(method="POST")
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def get_text(item: Item):
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return {"prompt": run_gpt2.call(item.prompt)}
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```
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### Deploy the web endpoint
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Deploy the web endpoint to Modal cloud with the [`modal deploy`](https://modal.com/docs/reference/cli/deploy) CLI command.
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Your web endpoint will acquire a persistent URL under the `modal.run` domain.
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## LLM wrapper around Modal web endpoint
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The `Modal` LLM wrapper class which will accept your deployed web endpoint's URL.
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```python
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from langchain_community.llms import Modal
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endpoint_url = "https://ecorp--custom-llm-endpoint.modal.run" # REPLACE ME with your deployed Modal web endpoint's URL
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llm = Modal(endpoint_url=endpoint_url)
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llm_chain = LLMChain(prompt=prompt, llm=llm)
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question = "What NFL team won the Super Bowl in the year Justin Beiber was born?"
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llm_chain.run(question)
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```
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