Leveraging Snowflake Cortex AI for Effective LLM Training
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Harnessing the capabilities of artificial intelligence, the training of large language models (LLMs) is critical for advancing natural language processing. Snowflake Cortex AI provides a scalable and effective platform for this intricate task. In this guide, we will examine how to make the most of Snowflake Cortex AI for efficient LLM training. We will cover everything from data collection and preprocessing to model training and deployment, emphasizing best practices and innovative methods that maximize the use of this powerful tool. Whether you're an AI enthusiast, a data scientist, or a business aiming to adopt advanced AI technologies, this guide will offer valuable insights into the transformative potential of Snowflake Cortex AI.
Fine-Tuning Large Language Models
Fine-tuning LLMs is crucial for several key reasons:
- Specialization: While pre-trained LLMs are trained on large, diverse datasets, they remain generalized. Fine-tuning allows models to adapt to specific tasks or domains, boosting their performance in areas like medical diagnostics, legal consulting, customer service, or other specialized fields.
- Enhanced Accuracy: Fine-tuning on domain-specific data helps the model grasp the unique nuances and contexts, improving the precision and relevance of its outputs, making it more dependable for real-world applications.
- Resource Efficiency: It is often more resource-efficient to fine-tune a pre-trained model than to train one from scratch, as it utilizes existing knowledge and adjusts it for specific tasks, saving both computational resources and time.
- Adaptability: Language evolves over time and varies across fields. Fine-tuning helps models stay current with the latest terminology, trends, and language patterns, ensuring their effectiveness.
- Customization: Fine-tuning allows organizations to modify models to reflect their brand's voice, tone, and style, which is vital for customer-facing applications where brand consistency is essential.
- Improved Performance: By focusing on relevant data, fine-tuning minimizes the impact of irrelevant information, leading to better performance and quicker convergence during the training process.
In conclusion, fine-tuning LLMs ensures that they meet specific needs and deliver more accurate, efficient, and pertinent results, optimizing their utility across various applications and sectors.
Snowflake Cortex AI Capabilities
Snowflake Cortex AI offers a streamlined platform for fine-tuning large language models. Here's how to utilize it:
Data Preparation
For this process, we will use the Question-Answer dataset available on Kaggle.
Follow these steps:
- Create a new database in the Data Section and add a schema named DATA.
- Upload external files from Kaggle to create two tables: TRAIN and VAL, discarding any irrelevant columns.
- Name the columns as Question and Answer.
Configuring the Fine-Tuning Job
After preparing your data, you can set up a fine-tuning job in Snowflake Cortex AI. This involves selecting a pre-trained model for fine-tuning, specifying the dataset, and defining training parameters like learning rate, batch size, and number of epochs. Snowflake Cortex AI provides an intuitive interface for these configurations, making it accessible even for those less experienced in machine learning. We will use the llama3-8b model.
Open a new Notebook in the Cortex Platform and execute the following SQL query to initiate the fine-tuning:
SELECT SNOWFLAKE.CORTEX.FINETUNE(
'CREATE',
'SciQ_model',
'llama3-8b',
'SELECT Question AS prompt, Answer AS completion FROM TRAIN',
'SELECT Question AS prompt, Answer AS completion FROM VAL'
);
The model training will commence after executing this command.
Monitoring and Managing Fine-Tuning Jobs
Snowflake Cortex AI includes comprehensive monitoring tools for tracking the progress of fine-tuning jobs. You can view metrics such as accuracy, loss, and training duration, and access logs for troubleshooting. Real-time monitoring enables adjustments to be made as necessary, ensuring the fine-tuning process is on track.
SELECT SNOWFLAKE.CORTEX.FINETUNE(
'DESCRIBE',
'Your_JOB_ID'
);
Evaluating and Validating the Fine-Tuned Model
Post fine-tuning, evaluating the model's performance is crucial. Snowflake Cortex AI offers various metrics and tools to assess the model's effectiveness for the specific task. Techniques such as cross-validation and A/B testing can be employed to confirm that the model meets the required accuracy and performance standards.
Here are the results I obtained:
{
"base_model": "llama3-8b",
"created_on": 1720357072007,
"finished_on": 1720358521300,
"id": "CortexFineTuningWorkflow_183f512d-d661-4f61-b7e4-11150ab2b651",
"model": "SciQ_model",
"progress": 1.0,
"status": "SUCCESS",
"training_data": "SELECT Question AS prompt, Answer AS completion FROM TRAIN",
"trained_tokens": 1035650,
"training_result": {
"validation_loss": 1.0134716033935547,
"training_loss": 0.49410628375064775
},
"validation_data": ""
}
Deployment of Fine-Tuned Models
We will deploy the model using Streamlit with the following code:
# Import necessary packages
import streamlit as st
from snowflake.snowpark.context import get_active_session
session = get_active_session()
def complete(myquestion):
cmd = f"""
select SNOWFLAKE.CORTEX.COMPLETE(?,?) as response"""
df_response = session.sql(cmd, params=['SciQ_model', myquestion]).collect()
return df_response
def display_response(question):
response = complete(question)
res_text = response[0].RESPONSE
st.markdown(res_text)
# Main code
st.title("You can ask me Scientific Question")
question = st.text_input("Enter question", placeholder="Vertebrata are characterized by the presence of what?", label_visibility="collapsed")
if question:
display_response(question)
Conclusion
Fine-tuning large language models using Snowflake Cortex AI enables you to customize these powerful tools to meet your specific requirements, enhancing their accuracy, efficiency, and relevance. By following the steps outlined in this guide—from data preparation to deployment—you can leverage the full capabilities of Snowflake Cortex AI to achieve outstanding results in your AI initiatives. Whether you are improving customer service, automating content creation, or exploring new research avenues, Snowflake Cortex AI equips you with the necessary tools for success.
References
- https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-finetuning#label-cortex-finetuning-monitor
- https://docs.snowflake.com/en/sql-reference/functions/finetune-describe
- https://quickstarts.snowflake.com/guide/finetuning_llm_using_snowflake_cortex_ai/index.html?index=..%2F..index#2
- https://www.youtube.com/watch?v=4RItxPcq4vk&t=4s