[docs] update readme and gitignore

.gitignore

@@ -2,3 +2,163 @@
 .venv/
 __pycache__
 .DS_Store
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/

README.md

@@ -1,6 +1,6 @@
 ---
-title: Shiny for Python template
-emoji: 🌍
+title: Axolotl Launcher 🚀
+emoji: 🐉
 colorFrom: yellow
 colorTo: indigo
 sdk: docker
@@ -8,13 +8,42 @@ pinned: false
 license: apache-2.0
 ---
 
-This is a templated Space for [Shiny for Python](https://shiny.rstudio.com/py/). 
+Note: This is filler text for now, but the app will be a fine-tuning app for Hugging Face models. It will allow users to easily fine-tune models on their own datasets using Hugging Face's infrastructure.
 
+## Getting Started with Your Fine-Tuning App on Hugging Face Spaces
 
-To get started with a new app do the following:
+Welcome to the fine-tuning app hosted on Hugging Face Spaces! This app leverages the power of Hugging Face's infrastructure to allow you to launch and fine-tune machine learning models easily. Below you'll find a guide to help you get started with using this app.
 
-1) Install Shiny with `pip install shiny`
-2) Create a new app with `shiny create .`
-3) Then run the app with `shiny run --reload`
+### Step 1: Create a Hugging Face Account
 
-To learn more about this framework please see the [Documentation](https://shiny.rstudio.com/py/docs/overview.html).
+Before you can use the app, you need to have a Hugging Face account. If you don't have one, please sign up at Hugging Face.
+
+### Step 2: Launch the App
+
+Once you have an account, navigate to the app hosted on Hugging Face Spaces. You can create a new space for your project directly on your profile or your organization's profile
+
+### Step 3: Configure Your Job
+
+In the app, you'll be able to configure your fine-tuning job. This includes selecting the dataset, model, and fine-tuning parameters. If you're not sure about the configurations, the app provides guidance to help you make the best choices for your specific use case.
+
+### Step 4: Start Fine-Tuning
+
+With your job configured, you can launch the fine-tuning process. The app will create a new space where your job will run. You can monitor the progress directly within the app.
+
+### Step 5: Job Completion and Shutdown
+
+Once the training is complete, the job will automatically shut down. You don't need to worry about stopping the resources manually, as the app handles this for you
+
+### Step 6: Review and Download Results
+
+After the job has finished, you can review the results and download any artifacts, such as the fine-tuned model. These will be available in the space created for your job.
+
+### Additional Information
+
+Support: If you need help with making a Space or run into any issues, feel free to ask questions on the Hugging Face forum
+
+Upgrades: You can upgrade your Space to run on a GPU or other accelerated hardware if your job requires it.
+Persistent Storage: If your fine-tuning job requires persistent storage, this can be configured in your Space settings
+Collaboration: You can work collaboratively with other people in the ML ecosystem by sharing your Space or inviting others to contribute.
+Conclusion
+This app is designed to make machine learning more accessible by simplifying the process of launching and fine-tuning models. We hope this guide helps you get started smoothly. Happy fine-tuning!