Finetuning Models

The LLM-VM uses a data synthesis feature to engage a teacher/student model for finetuning super specific small models from larger more general ones through a simple process.

  1. A scalable call is made to OpenAI (or other large parameter LLM)
    • ‘give me fifty examples like the following query…’
  2. The response is parsed into a training set for finetuning
  3. The small_model is trained on the synthezied data set and saved
Finetuning a Model
# import our client
from llm_vm.client import Client
import os
from llm_vm.config import settings
# Instantiate the client specifying which LLM you want to use
client=Client(big_model='chat_gpt', small_model='pythia')

# Put in your prompt and go!
response=client.complete(prompt="Answer question Q. ",
			 context="Q: What is the currency in myanmmar",
			 openai_key=settings.openai_api_key,
			 temperature=0.0,
			 data_synthesis=True,
			 finetune=True,)

Loading a finetuned LLM from disk

Loading a finetuned model into the LLM-VM for completions is very straightforward.

  1. Specify the family of finetuned models in “big_model”
  2. Specify the specific filename of the finetuned model you wish to load
  3. Run the load_finetune(model_filename) class function to load the model
  4. Generate completions in the usual way
Loading a Finetuned Model
# import our client
from llm_vm.client import Client
import os

# Instantiate the client specifying which LLM you want to use
client=Client(big_model='pythia')

# specify the file name of the finetuned model to load
model_name='<filename_of_your_model>.pt'
client.load_finetune(model_name)

# Put in your prompt and go!
response=client.complete(prompt='What is anarchy?',
                         context='')
print(response)
Saving finetuned LLMs can result in a large amount of storage space. Make sure to keep an eye on how much you’re saving

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