mirror of
https://github.com/hwchase17/langchain
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66 lines
1.6 KiB
Plaintext
66 lines
1.6 KiB
Plaintext
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# Modal
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This page covers how to use the Modal ecosystem within LangChain.
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It is broken into two parts: installation and setup, and then references to specific Modal wrappers.
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## Installation and Setup
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- Install with `pip install modal-client`
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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.webhook(method="POST")
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def my_webhook(item: Item):
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return {"prompt": my_function.call(item.prompt)}
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```
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An example with GPT2:
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```python
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from pydantic import BaseModel
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import modal
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stub = modal.Stub("example-get-started")
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volume = modal.SharedVolume().persist("gpt2_model_vol")
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CACHE_PATH = "/root/model_cache"
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@stub.function(
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gpu="any",
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image=modal.Image.debian_slim().pip_install(
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"tokenizers", "transformers", "torch", "accelerate"
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),
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shared_volumes={CACHE_PATH: volume},
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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('gpt2')
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model = GPT2LMHeadModel.from_pretrained('gpt2')
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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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class Item(BaseModel):
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prompt: str
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@stub.webhook(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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## Wrappers
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### LLM
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There exists an Modal LLM wrapper, which you can access with
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```python
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from langchain.llms import Modal
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```
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