LANGCHAIN — Can Langsmith be used to support fine-tuning of open-source LLMs?

The great myth of our times is that technology is communication. — Libby Larsen

Fine-tuning open-source Language Model (LLM) can be a challenging task, but LangSmith can be used to support the entire workflow. In this tutorial, we will cover the process of fine-tuning LLMs using LangSmith. Below are the key steps and code snippets to guide you through the process.

How to fine-tune

Fine-tuning an LLM can be achieved using various tools and techniques. In the example below, we provide a code snippet demonstrating how to fine-tune a pre-trained LLaMA-7b chat model using the HuggingFace library.

from transformers import AutoModelForCausalLM, TrainingArguments, Trainer
from datasets import load_dataset

# Load the dataset
dataset = load_dataset("text", split="train[:5%]")

# Define the model and training arguments
model_name = "llama-2-7b-chat"
model = AutoModelForCausalLM.from_pretrained(model_name)
args = TrainingArguments("fine-tuning-llama-7b", evaluation_strategy="steps", eval_steps=500)

# Define the trainer
trainer = Trainer(
    model,
    args,
    train_dataset=dataset,
    eval_dataset=dataset
)

# Fine-tune the model
trainer.train()

Dataset

LangSmith simplifies the process of dataset collection and management. The following code snippet demonstrates how to create and load training and test datasets from LangSmith.

from langsmith import SmithDataset

# Create a LangSmith dataset
train_dataset = SmithDataset("train_data_path")
test_dataset = SmithDataset("test_data_path")

# Load the datasets
train_data = train_dataset.load()
test_data = test_dataset.load()

Evaluation

LangSmith can be used to evaluate the performance of the fine-tuned LLM. The following code snippet shows an example of using LangSmith to evaluate the predictions from the fine-tuned model.

from langsmith import LLMEvaluator

# Create an LLM Evaluator
evaluator = LLMEvaluator(model="llama-7b-chat", dataset="test_dataset")

# Evaluate the model predictions
evaluation_results = evaluator.evaluate(predictions)

Conclusion

In this tutorial, we have demonstrated how LangSmith can support the entire process of fine-tuning open-source LLMs, from dataset management to evaluation. By leveraging the capabilities of LangSmith, you can streamline the fine-tuning workflow and achieve better results.