Initial commit

.gitignore

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+.venv/
+__pycache__/
+.env

Dockerfile

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+# Use an official Python runtime as a parent image
+FROM python:3.11.1
+
+# Set the working directory in the container
+WORKDIR /app
+
+# Install poetry
+# RUN pip3 install poetry==1.7.1
+
+# Copy the current directory contents into the container at /usr/src/app
+COPY . .
+
+# Install dependencies
+# RUN poetry config virtualenvs.create false \
+#     && poetry install --no-interaction --no-ansi
+# Streamlit must be installed separately. Potentially this will cause an issue with dependencies in the future, but it's the only way it works.
+# RUN pip3 install streamlit  
+
+#  Install dependencies
+RUN pip3 install -r requirements.txt
+
+# Make a port available to the world outside this container
+# The EXPOSE instruction informs Docker that the container listens on the specified network ports at runtime. Your container needs to listen to Streamlit’s (default) port 8501.
+EXPOSE 8501
+
+# The HEALTHCHECK instruction tells Docker how to test a container to check that it is still working. Your container needs to listen to Streamlit’s (default) port 8501:
+HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health
+
+# Run the command inside your image filesystem.
+CMD ["python", "train_llm.py"]
+
+# Execute with:
+# docker build -t <image_name> .    
+# docker run -p 8501:8501 <image_name>

README.md

@@ -1,11 +1,11 @@
 ---
 title: Autotrain Playground
-emoji: 👀
-colorFrom: green
-colorTo: indigo
+emoji: 🚀
+colorFrom: gray
+colorTo: blue
 sdk: docker
 pinned: false
-license: apache-2.0
+app_port: 8501
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

train_llm.py

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+import os
+import jsonlines
+from uuid import uuid4
+import pandas as pd
+
+from datasets import load_dataset
+import subprocess
+from tqdm.notebook import tqdm
+
+# from dotenv import load_dotenv,find_dotenv
+# load_dotenv(find_dotenv(),override=True)
+
+# Load dataset
+dataset_name = 'ai-aerospace/ams_data_train_generic_v0.1_100'
+dataset=load_dataset(dataset_name)
+
+# Write dataset files into data directory
+data_directory = '../fine_tune_data/'
+
+# Create the data directory if it doesn't exist
+os.makedirs(data_directory, exist_ok=True)
+
+# Write the train data to a CSV file
+train_data='train_data.csv'
+train_filename = os.path.join(data_directory, train_data)
+dataset['train'].to_pandas().to_csv(train_filename, columns=['text'], index=False)
+
+# Write the validation data to a CSV file
+validation_data='validation_data.csv'
+validation_filename = os.path.join(data_directory, validation_data)
+dataset['validation'].to_pandas().to_csv(validation_filename, columns=['text'], index=False)
+
+# Define project parameters
+username='ai-aerospace'
+project_name='./llms/'+'ams_data_train-100_'+str(uuid4())
+repo_name='ams_data_train-100_'+str(uuid4())
+
+model_name='TinyLlama/TinyLlama-1.1B-Chat-v0.1'
+# model_name='mistralai/Mistral-7B-v0.1'
+
+# Save parameters to environment variables
+os.environ["project_name"] = project_name
+os.environ["model_name"] = model_name
+os.environ["repo_id"] = username+'/'+repo_name
+os.environ["train_data"] = train_data   
+os.environ["validation_data"] = validation_data
+
+# Set .venv and execute the autotrain script
+# !autotrain llm --train --project_name my-llm --model TinyLlama/TinyLlama-1.1B-Chat-v0.1 --data_path . --use-peft --use_int4 --learning_rate 2e-4 --train_batch_size 6 --num_train_epochs 3 --trainer sft
+# The training dataset to be used must be called training.csv and be located in the data_path folder.
+command="""
+source ../.venv/bin/activate && autotrain llm --train \
+    --project_name ${project_name} \
+    --model ${model_name} \
+    --data_path ../fine_tune_data \
+    --train_split ${train_data} \
+    --valid_split ${validation_data} \
+    --use-peft \
+    --learning_rate 2e-4 \
+    --train_batch_size 6 \
+    --num_train_epochs 3 \
+    --trainer sft \
+    --push_to_hub \
+    --repo_id ${repo_id} \
+    --token $HUGGINGFACE_TOKEN
+"""
+
+# Use subprocess.run() to execute the command
+subprocess.run(command, shell=True, check=True, env=os.environ)