Pasted-Thanks-for-the-clarification-Here-s-a-tailored-solution-for-fine-tuning-optimization-using-Python--1734035138568.txt

Thanks for the clarification! Here’s a tailored solution for fine-tuning optimization using Python, Streamlit, Snowflake, Mistral, and Hugging Face pipelines. We’ll focus on creating a pipeline that retrieves and analyzes training data, displays insights interactively using Streamlit, and integrates Snowflake for data storage.

Fine-Tuning Optimization Workflow
	1.	Data Storage: Store fine-tuning results (e.g., accuracy, loss, hyperparameters) in Snowflake.
	2.	Model Interaction: Use Hugging Face pipelines and Mistral models for training and inference.
	3.	Visualization: Use Streamlit for interactive dashboards.
	4.	Optimization Analysis: Compare metrics, tune hyperparameters, and iterate.

Database Integration (Snowflake)

Store fine-tuning results in Snowflake.

Python Setup:
Install snowflake-connector-python:

pip install snowflake-connector-python

Snowflake Table Schema:

CREATE TABLE FineTuningRun (
    RunID SERIAL PRIMARY KEY,
    ModelName VARCHAR(100),
    Hyperparameters JSON,
    Epochs INT,
    LearningRate FLOAT,
    Accuracy FLOAT,
    Loss FLOAT,
    RunDate TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);

Python Code to Insert Data:

import snowflake.connector

# Snowflake connection details
conn = snowflake.connector.connect(
    user='YOUR_USER',
    password='YOUR_PASSWORD',
    account='YOUR_ACCOUNT'
)

# Insert fine-tuning results
def insert_fine_tuning_run(model_name, hyperparameters, epochs, learning_rate, accuracy, loss):
    query = """
        INSERT INTO FineTuningRun (ModelName, Hyperparameters, Epochs, LearningRate, Accuracy, Loss)
        VALUES (%s, %s, %s, %s, %s, %s)
    """
    with conn.cursor() as cursor:
        cursor.execute(query, (model_name, hyperparameters, epochs, learning_rate, accuracy, loss))

Model Fine-Tuning (Mistral + Hugging Face)

Example fine-tuning using Hugging Face pipelines:

pip install transformers accelerate

Fine-Tuning Example:

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

# Load Mistral model and tokenizer
model_name = "mistral-7b"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Load dataset
dataset = load_dataset("wikitext", "wikitext-2-raw-v1")
tokenized_dataset = dataset.map(lambda x: tokenizer(x["text"], truncation=True, padding=True), batched=True)

# Fine-tuning setup
training_args = TrainingArguments(
    output_dir="./results",
    num_train_epochs=3,
    per_device_train_batch_size=8,
    learning_rate=5e-5,
    evaluation_strategy="epoch",
    save_strategy="epoch",
    logging_dir="./logs",
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=tokenized_dataset["train"],
    eval_dataset=tokenized_dataset["validation"]
)

# Train the model
trainer.train()

After training, log the results into Snowflake:

insert_fine_tuning_run(
    model_name="mistral-7b",
    hyperparameters='{"batch_size": 8, "optimizer": "adam"}',
    epochs=3,
    learning_rate=5e-5,
    accuracy=0.91,  # Replace with actual evaluation metric
    loss=0.2        # Replace with actual evaluation metric
)

Streamlit Dashboard

Install Streamlit:

pip install streamlit

Interactive Visualization with Streamlit:

import streamlit as st
import pandas as pd
import snowflake.connector

# Snowflake connection details
conn = snowflake.connector.connect(
    user='YOUR_USER',
    password='YOUR_PASSWORD',
    account='YOUR_ACCOUNT'
)

# Fetch data from Snowflake
@st.cache
def fetch_data():
    query = "SELECT * FROM FineTuningRun"
    return pd.read_sql(query, conn)

# Load data
data = fetch_data()

# Streamlit UI
st.title("Fine-Tuning Optimization Dashboard")

# Display metrics
st.subheader("Overall Metrics")
st.metric("Total Runs", len(data))
st.metric("Best Accuracy", f"{data['Accuracy'].max():.2%}")
st.metric("Lowest Loss", f"{data['Loss'].min():.2f}")

# Display table
st.subheader("Fine-Tuning Runs")
st.dataframe(data)

# Visualization
st.subheader("Accuracy vs Loss")
st.line_chart(data[['Accuracy', 'Loss']])

Run the Streamlit app:

streamlit run app.py

Advanced Optimization Features
	1.	Hyperparameter Tuning:
Use libraries like Optuna for automated hyperparameter optimization.

pip install optuna

Example integration:

import optuna

def objective(trial):
    learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-3)
    batch_size = trial.suggest_categorical('batch_size', [8, 16, 32])
    # Run training with these parameters...
    accuracy = train_model(learning_rate, batch_size)  # Placeholder
    return accuracy

study = optuna.create_study(direction="maximize")
study.optimize(objective, n_trials=50)

print(study.best_params)


	2.	Model Comparison:
Add filtering options in Streamlit to compare models.

selected_model = st.selectbox("Select Model", data['ModelName'].unique())
st.dataframe(data[data['ModelName'] == selected_model])


	3.	Real-Time Insights:
Incorporate real-time updates by reloading Snowflake data periodically in Streamlit using:

st.experimental_rerun()

Next Steps
   •   Integration Testing: Ensure Snowflake, Streamlit, and the Hugging Face pipelines are properly linked.
   •   Optimization Features: Automate fine-tuning and dashboard updates.
   •   Scalability: Deploy Streamlit app on a cloud platform (e.g., AWS, Azure) for wider access.

Would you like further details on any specific part, like deploying this setup or customizing the dashboard?