diff --git a/.dockerignore b/.dockerignore
new file mode 100644
index 0000000000000000000000000000000000000000..4dcfcfeca16e22dd8d62d11094a6fe3eead40a4c
--- /dev/null
+++ b/.dockerignore
@@ -0,0 +1,57 @@
+# Version control
+.git
+.gitignore
+
+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# Virtual environments
+.env*
+!.env.example
+.venv
+env/
+venv/
+ENV/
+
+# IDE
+.idea/
+.vscode/
+*.swp
+*.swo
+
+# Testing
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+.pytest_cache/
+
+# Project specific
+nltk_data/
+.pdm-python
+.pdm.toml
+__pypackages__/
\ No newline at end of file
diff --git a/.env.local.template b/.env.local.template
new file mode 100644
index 0000000000000000000000000000000000000000..56d92ed0183eeb224729c29c75d8da6c575be599
--- /dev/null
+++ b/.env.local.template
@@ -0,0 +1,54 @@
+# =============================================================================
+# LOCAL/API CONFIGURATION
+# =============================================================================
+
+# -----------------------------------------------------------------------------
+# REQUIRED CONFIGURATION
+# -----------------------------------------------------------------------------
+# Hugging Face token (required for all setups)
+HF_TOKEN=hf_...
+
+# Generation Settings
+MAX_NUM_TOKENS=2048
+MAX_NUM_ROWS=1000
+DEFAULT_BATCH_SIZE=5
+
+# Required for chat data generation with Llama or Qwen models
+# Options: "llama3", "qwen2", or custom template string
+MAGPIE_PRE_QUERY_TEMPLATE=llama3
+
+# -----------------------------------------------------------------------------
+# A. CLOUD API SERVICES
+# -----------------------------------------------------------------------------
+
+# 1. HUGGING FACE INFERENCE API (Default, Recommended)
+MODEL=meta-llama/Llama-3.1-8B-Instruct
+# MODEL=Qwen/Qwen2.5-1.5B-Instruct
+
+# 2. OPENAI API
+# OPENAI_BASE_URL=https://api.openai.com/v1/
+# MODEL=gpt-4
+# API_KEY=sk-...
+
+# 3. HUGGING FACE SPACE FOR ARGILLA (optional)
+# ARGILLA_API_URL=https://your-space.hf.space/
+# ARGILLA_API_KEY=your_key
+
+# -----------------------------------------------------------------------------
+# B. LOCAL SERVICES (Requires Installation)
+# -----------------------------------------------------------------------------
+
+# 1. LOCAL OLLAMA
+# OLLAMA_BASE_URL=http://127.0.0.1:11434/
+# MODEL=llama3.2:1b
+# TOKENIZER_ID=meta-llama/Llama-3.2-1B-Instruct
+
+# 2. LOCAL VLLM
+# VLLM_BASE_URL=http://127.0.0.1:8000/
+# MODEL=Qwen/Qwen2.5-1.5B-Instruct
+# TOKENIZER_ID=Qwen/Qwen2.5-1.5B-Instruct
+
+# 3. LOCAL TGI
+# HUGGINGFACE_BASE_URL=http://127.0.0.1:3000/
+# MODEL=meta-llama/Llama-3.1-8B-Instruct
+# TOKENIZER_ID=meta-llama/Llama-3.1-8B-Instruct
diff --git a/.gitattributes b/.gitattributes
index a6344aac8c09253b3b630fb776ae94478aa0275b..0f1a7033cf76ef468e0c7c5622f0bde87a8bda1b 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -33,3 +33,8 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
*.zip filter=lfs diff=lfs merge=lfs -text
*.zst filter=lfs diff=lfs merge=lfs -text
*tfevents* filter=lfs diff=lfs merge=lfs -text
+assets/flow.png filter=lfs diff=lfs merge=lfs -text
+*.sh text eol=lf
+assets/argilla.png filter=lfs diff=lfs merge=lfs -text
+assets/ui-full.png filter=lfs diff=lfs merge=lfs -text
+assets/ui.png filter=lfs diff=lfs merge=lfs -text
diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..ba21e58da821e5533e1af324794e5afa7b9f19b9
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,173 @@
+# 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-project.org/#use-with-ide
+.pdm.toml
+.pdm-python
+.pdm-build/
+
+# 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/
+.python-version
+
+# 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/
+.DS_Store
+
+# nltk
+nltk_data/
+
+# examples
+models/
+
+# Elasticsearch data
+elasticsearch_data/
\ No newline at end of file
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..f49a4e16e68b128803cc2dcea614603632b04eac
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,201 @@
+ Apache License
+ Version 2.0, January 2004
+ http://www.apache.org/licenses/
+
+ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
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+
+ 6. Trademarks. This License does not grant permission to use the trade
+ names, trademarks, service marks, or product names of the Licensor,
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+
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+ PARTICULAR PURPOSE. You are solely responsible for determining the
+ appropriateness of using or redistributing the Work and assume any
+ risks associated with Your exercise of permissions under this License.
+
+ 8. Limitation of Liability. In no event and under no legal theory,
+ whether in tort (including negligence), contract, or otherwise,
+ unless required by applicable law (such as deliberate and grossly
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+ of your accepting any such warranty or additional liability.
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+ END OF TERMS AND CONDITIONS
+
+ APPENDIX: How to apply the Apache License to your work.
+
+ To apply the Apache License to your work, attach the following
+ boilerplate notice, with the fields enclosed by brackets "[]"
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diff --git a/README.md b/README.md
index ac226898c58c52ece91af68456ab46388e260b6a..fd31fb37e6956285a1c6f62bc47eefcd3fe20425 100644
--- a/README.md
+++ b/README.md
@@ -1,10 +1,172 @@
---
title: Synthetic Data Generator
-emoji: 🐠
-colorFrom: blue
-colorTo: blue
-sdk: docker
-pinned: false
+short_description: Build datasets using natural language
+emoji: 🧬
+colorFrom: yellow
+colorTo: pink
+sdk: gradio
+sdk_version: 5.8.0
+app_file: app.py
+pinned: true
+license: apache-2.0
+hf_oauth: true
+#header: mini
+hf_oauth_scopes:
+- read-repos
+- write-repos
+- manage-repos
+- inference-api
---
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+> [!IMPORTANT]
+The original authors have moved on to other projects. While the code might still be functional for its original purpose, please be aware that the original team does not plan to develop new features, bug fixes, or updates. If you'd like to become a maintainer, please open an issue to discuss.
+>
+>
+
+
+Build datasets using natural language
+
+
+
+## Introduction
+
+Synthetic Data Generator is a tool that allows you to create high-quality datasets for training and fine-tuning language models. It leverages the power of distilabel and LLMs to generate synthetic data tailored to your specific needs. [The announcement blog](https://huggingface.co/blog/synthetic-data-generator) goes over a practical example of how to use it but you can also watch the [video](https://www.youtube.com/watch?v=nXjVtnGeEss) to see it in action.
+
+Supported Tasks:
+
+- Text Classification
+- Chat Data for Supervised Fine-Tuning
+- Retrieval Augmented Generation
+
+This tool simplifies the process of creating custom datasets, enabling you to:
+
+- Describe the characteristics of your desired application
+- Iterate on sample datasets
+- Produce full-scale datasets
+- Push your datasets to the [Hugging Face Hub](https://huggingface.co/datasets?other=datacraft) and/or [Argilla](https://docs.argilla.io/)
+
+By using the Synthetic Data Generator, you can rapidly prototype and create datasets for, accelerating your AI development process.
+
+
+
+
+
+
+
+
+
+
+## Installation
+
+You can simply install the package with:
+
+```bash
+pip install synthetic-dataset-generator
+```
+
+### Quickstart
+
+```python
+from synthetic_dataset_generator import launch
+
+launch()
+```
+
+### Environment Variables
+
+- `HF_TOKEN`: Your [Hugging Face token](https://huggingface.co/settings/tokens/new?ownUserPermissions=repo.content.read&ownUserPermissions=repo.write&globalPermissions=inference.serverless.write&tokenType=fineGrained) to push your datasets to the Hugging Face Hub and generate free completions from Hugging Face Inference Endpoints. You can find some configuration examples in the [examples](examples/) folder.
+
+You can set the following environment variables to customize the generation process.
+
+- `MAX_NUM_TOKENS`: The maximum number of tokens to generate, defaults to `2048`.
+- `MAX_NUM_ROWS`: The maximum number of rows to generate, defaults to `1000`.
+- `DEFAULT_BATCH_SIZE`: The default batch size to use for generating the dataset, defaults to `5`.
+
+Optionally, you can use different API providers and models.
+
+- `MODEL`: The model to use for generating the dataset, e.g. `meta-llama/Meta-Llama-3.1-8B-Instruct`, `gpt-4o`, `llama3.1`.
+- `API_KEY`: The API key to use for the generation API, e.g. `hf_...`, `sk-...`. If not provided, it will default to the `HF_TOKEN` environment variable.
+- `OPENAI_BASE_URL`: The base URL for any OpenAI compatible API, e.g. `https://api.openai.com/v1/`.
+- `OLLAMA_BASE_URL`: The base URL for any Ollama compatible API, e.g. `http://127.0.0.1:11434/`.
+- `HUGGINGFACE_BASE_URL`: The base URL for any Hugging Face compatible API, e.g. TGI server or Dedicated Inference Endpoints. If you want to use serverless inference, only set the `MODEL`.
+- `VLLM_BASE_URL`: The base URL for any VLLM compatible API, e.g. `http://localhost:8000/`.
+
+To use a specific model exclusively for generating completions, set the corresponding environment variables by appending `_COMPLETION` to the ones mentioned earlier. For example, you can use `MODEL_COMPLETION` and `OPENAI_BASE_URL_COMPLETION`.
+
+SFT and Chat Data generation is not supported with OpenAI Endpoints. Additionally, you need to configure it per model family based on their prompt templates using the right `TOKENIZER_ID` and `MAGPIE_PRE_QUERY_TEMPLATE` environment variables.
+
+- `TOKENIZER_ID`: The tokenizer ID to use for the magpie pipeline, e.g. `meta-llama/Meta-Llama-3.1-8B-Instruct`.
+- `MAGPIE_PRE_QUERY_TEMPLATE`: Enforce setting the pre-query template for Magpie, which is only supported with Hugging Face Inference Endpoints. `llama3` and `qwen2` are supported out of the box and will use `"<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\n"` and `"<|im_start|>user\n"`, respectively. For other models, you can pass a custom pre-query template string.
+
+Optionally, you can also push your datasets to Argilla for further curation by setting the following environment variables:
+
+- `ARGILLA_API_KEY`: Your Argilla API key to push your datasets to Argilla.
+- `ARGILLA_API_URL`: Your Argilla API URL to push your datasets to Argilla.
+
+To save the generated datasets to a local directory instead of pushing them to the Hugging Face Hub, set the following environment variable:
+
+- `SAVE_LOCAL_DIR`: The local directory to save the generated datasets to.
+
+You can use our environment template as a starting point:
+
+```bash
+cp .env.local.template .env
+```
+
+### Argilla integration
+
+Argilla is an open source tool for data curation. It allows you to annotate and review datasets, and push curated datasets to the Hugging Face Hub. You can easily get started with Argilla by following the [quickstart guide](https://docs.argilla.io/latest/getting_started/quickstart/).
+
+
+
+## Custom synthetic data generation?
+
+Each pipeline is based on distilabel, so you can easily change the LLM or the pipeline steps.
+
+Check out the [distilabel library](https://github.com/argilla-io/distilabel) for more information.
+
+## Development
+
+Install the dependencies:
+
+```bash
+# Create a virtual environment
+python -m venv .venv
+source .venv/bin/activate
+
+# Install the dependencies
+pip install -e . # pdm install
+```
+
+Run the app:
+
+```bash
+python app.py
+```
+
+## 🐳 Docker Setup
+
+The containerized tool uses Ollama for local LLM inference and Argilla for data curation. Here's the architecture:
+
+
+
+Quick setup with all services (App + Ollama + Argilla):
+
+```bash
+# Copy environment template
+cp docker/.env.docker.template .env # Add your HF_TOKEN in .env
+
+# Build all services (this may take a few minutes)
+docker compose -f docker-compose.yml -f docker/ollama/compose.yml -f docker/argilla/compose.yml build
+
+# Start all services
+docker compose -f docker-compose.yml -f docker/ollama/compose.yml -f docker/argilla/compose.yml up -d
+```
+
+> For more detailed Docker configurations and setups, check [docker/README.md](docker/README.md)
diff --git a/app.py b/app.py
new file mode 100644
index 0000000000000000000000000000000000000000..043fa99a266646d779e44e5f5a4effd7872e0ce3
--- /dev/null
+++ b/app.py
@@ -0,0 +1,4 @@
+from synthetic_dataset_generator import launch
+
+if __name__ == "__main__":
+ launch()
diff --git a/assets/argilla.png b/assets/argilla.png
new file mode 100644
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+duplicating this Space .
+
+## What's Next?
+
+After successfully generating your first dataset, several advanced implementation paths are available:
+
+Extend your dataset generation capabilities:
+- [Fine-tune models on synthetic data](https://huggingface.co/blog/davidberenstein1957/fine-tune-a-smollm-on-synthetic-data-of-llm) for domain-specific tasks
+- [Create specialized reasoning datasets](https://huggingface.co/blog/sdiazlor/fine-tune-deepseek-with-a-synthetic-reasoning-data) for advanced model training
+
+## Conclusion
+
+The Synthetic Dataset Generator represents a significant advancement in private data generation technology, addressing the growing need for high-quality training data while maintaining security and control. By leveraging containerized architecture and local LLM inference, organizations can now generate custom datasets without compromising on data privacy or quality.
+
+The solution's modular design enables seamless integration with existing ML pipelines while providing enterprise-grade features like persistent storage, comprehensive monitoring, and scalable infrastructure. Through collaborative validation workflows and structured quality control processes, teams can efficiently create and curate datasets tailored to their specific needs.
+
+This combination of security, efficiency, and flexibility makes the Synthetic Dataset Generator an essential tool for organizations looking to accelerate their AI development while maintaining complete control over their data generation pipeline.
+
+## References & Documentation
+
+
+- [Synthetic Dataset Generator](https://github.com/argilla-io/synthetic-data-generator): Open-source tool for dataset generation using natural language
+- [Distilabel Framework](https://github.com/argilla-io/distilabel): Advanced dataset generation framework
+- [Docker Best Practices](https://docs.docker.com/develop/develop-images/dockerfile_best-practices/): Container optimization guidelines
+- [Argilla Documentation](https://docs.argilla.io): Data curation platform documentation
+- [Ollama Integration](https://github.com/jmorganca/ollama): Local LLM deployment guide
\ No newline at end of file
diff --git a/examples/fine-tune-deepseek-reasoning-sft.ipynb b/examples/fine-tune-deepseek-reasoning-sft.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..f8328c2672c18350985a181d0e17c8705a6ab698
--- /dev/null
+++ b/examples/fine-tune-deepseek-reasoning-sft.ipynb
@@ -0,0 +1,6206 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Fine-tune DeepSeek with a Synthetic Reasoning Dataset\n",
+ "\n",
+ "This notebook demonstrates the fine-tuning process of `unsloth/DeepSeek-R1-Distill-Qwen-1.5B-unsloth-bnb-4bit` using a synthetic reasoning dataset.\n",
+ "\n",
+ "It provides a complete walkthrough of the fine-tuning process after generating synthetic data using the Synthetic Data Generator. For a comprehensive explanation of the methodology and additional details, refer to the blog post: [Fine-tune DeepSeek with a Synthetic Reasoning Dataset](https://huggingface.co/blog/sdiazlor/fine-tune-deepseek-with-a-synthetic-reasoning-data)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Getting Started"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Install the Dependencies"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!pip install datasets\n",
+ "!pip install unsloth"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Import the Required Libraries"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import torch\n",
+ "from datasets import load_dataset\n",
+ "from transformers import TrainingArguments\n",
+ "from trl import SFTTrainer\n",
+ "from unsloth import is_bfloat16_supported, FastLanguageModel"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Configure the Environment"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "MODEL = \"unsloth/DeepSeek-R1-Distill-Qwen-1.5B-unsloth-bnb-4bit\"\n",
+ "REPO_NAME = \"sdiazlor\" # your HF username here\n",
+ "MODEL_NAME = \"deepseek-r1-distill-qwen-1.5-unsloth-sft-python\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Load the Model and Tokenizer"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 316,
+ "referenced_widgets": [
+ "5b30f4877b554974bb8cdfc9814bb967",
+ "1b216f51a8714e9dab82dc844fed17bf",
+ "a87fd32f965c41e68249ba9bf4234691",
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+ "921c02b29a564833ae10ac54f220c1d4",
+ "4b8d88e2c0634338affb3b4d81e77974",
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+ "1a0a4ae3d6c147c3bbb72e703858aedc",
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+ "24dfeff577da4393bdaa5de47bf5d096",
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+ "19b6b7f26e394d9fb7c2992d71b1fc14",
+ "007ba2f3f37c48f8a5e7e21a4a2e5a71",
+ "65a71e24010b4ba4884dc0bb2fe37e1f",
+ "99831f0a5b204b24923d5ca3d30c42a9",
+ "52b547101e8848cb9e5881c3e63eca3c",
+ "b6e67b2b00be4835962dc5939e25a289",
+ "747b3c80a240453c986bc8db01072394",
+ "29d52353241742bf882ade4bc4eeb402",
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+ "a76024ba39194881b63302a11383e84e",
+ "40f0399f117845b08eff5c5ed84f137d"
+ ]
+ },
+ "id": "QmUBVEnvCDJv",
+ "outputId": "977ab27e-7773-4b20-e16b-7df3f1d8994e"
+ },
+ "outputs": [],
+ "source": [
+ "# Load the 4bit pre quantized model of deepseek and the tokenizer\n",
+ "\n",
+ "model, tokenizer = FastLanguageModel.from_pretrained(\n",
+ " model_name = MODEL,\n",
+ " max_seq_length = 2048,\n",
+ " dtype = None,\n",
+ " load_in_4bit = True,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "6bZsfBuZDeCL",
+ "outputId": "4bdd6bfa-47c5-47cd-cd45-475107258a89"
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Unsloth 2025.2.5 patched 28 layers with 28 QKV layers, 28 O layers and 28 MLP layers.\n"
+ ]
+ }
+ ],
+ "source": [
+ "# We add the LORA adapters to the model\n",
+ "model = FastLanguageModel.get_peft_model(\n",
+ " model,\n",
+ " r=16,\n",
+ " target_modules=[\n",
+ " \"q_proj\",\n",
+ " \"k_proj\",\n",
+ " \"v_proj\",\n",
+ " \"o_proj\",\n",
+ " \"gate_proj\",\n",
+ " \"up_proj\",\n",
+ " \"down_proj\",\n",
+ " ],\n",
+ " lora_alpha=16,\n",
+ " lora_dropout=0,\n",
+ " bias=\"none\",\n",
+ " use_gradient_checkpointing=\"unsloth\",\n",
+ " random_state=3407,\n",
+ " use_rslora=False,\n",
+ " loftq_config=None,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Pre-process the Synthetic Data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 154,
+ "referenced_widgets": [
+ "e6ffb622331241239867b1477d3da76b",
+ "5d835002db074abea3d5b87e0ce72618",
+ "8b79b01a42614f4e9ae6550703561236",
+ "a9f65983f1f24d7c9cceaf65c2191c7d",
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+ "04b79a83f42745449910b1733a8496c7",
+ "36f85ae64a1e440b8b15d0c5625bd23f",
+ "fd3643434d0f477f90a59dc8e0bf64f2"
+ ]
+ },
+ "id": "LjY75GoYUCB8",
+ "outputId": "35602bba-6cdf-4cb5-ac87-967679f10350"
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "string"
+ },
+ "text/plain": [
+ "'Below is an instruction that describes a task, paired with a question that provides further context. \\nWrite a response that appropriately answer the question. \\nBefore answering, think carefully but concisely about the question and create a step-by-step chain of thoughts to ensure a logical and accurate response.\\n\\n### Instruction:\\nYou are an expert programmer with advanced knowledge in Python. Your task is to provide concise and easy-to-understand solutions. Please answer the following python question. \\n\\n### Question:\\nHow can I solve a Rubik\\'s Cube? \\n\\n\\n\\n### Response:\\nOkay, so I want to solve a Rubik\\'s Cube, but I\\'m not really sure where to start. I remember seeing people solve them quickly, but it looks so complicated. Let me try to break it down.\\n\\nFirst, I think I need to understand the structure of the cube. It has six faces, each with nine smaller squares of different colors. The goal is to have each face all one color. But how do the pieces move?\\n\\nI\\'ve heard about algorithms before. Maybe that\\'s the key. I guess algorithms are specific sequences of moves that solve certain parts of the cube. But I\\'m not sure which ones to use or in what order.\\n\\nI remember someone mentioning something called the cross. That\\'s probably the first step. So, I should start by making a cross on one face. Let\\'s pick the white face for the cross. I need to get all the white edges aligned with their corresponding center colors.\\n\\nOnce the cross is done, the next step might be to solve the corners of that face. Each corner has three colors, so I need to make sure they match the adjacent center colors. Maybe there\\'s a specific algorithm for inserting the corners correctly without messing up the cross.\\n\\nAfter the first layer is done, I think the next part is the middle layer. This involves moving the edge pieces between the first and last layers. I\\'ve heard terms like F, R, U, etc., which stand for front, right, up, etc. Maybe the algorithm for the middle layer involves moving a piece from the top to the correct position.\\n\\nThen comes the last layer, which is the trickiest part. I think this involves orienting the edges so they all face the right way, and then permuting the corners. I remember something about the \"OLL\" and \"PLL\" steps. OLL is orienting the last layer, and PLL is permuting it. There are a lot of algorithms for each, so it\\'s important to learn the common ones first.\\n\\nI\\'m a bit confused about how to recognize when to use each algorithm. Maybe I should start with the most common ones, like the cross, then F2L, OLL, and PLL. Each step builds on the previous one, so I shouldn\\'t skip ahead.\\n\\nI also wonder about the notation. F means moving the front face clockwise, F\\' is counterclockwise, and F2 is turning it twice. Understanding this notation is crucial for following the algorithms.\\n\\nI should probably practice each step separately. Start with solving the cross, then move on to the corners, and so on. It might take a while to get each step right, but with practice, it should become easier.\\n\\nWait, what if I get stuck? Maybe I should look up some tutorials or guides that break it down step by step. There are probably detailed explanations and videos that can help me visualize the moves better.\\n\\nIn summary, solving a Rubik\\'s Cube seems to involve a series of structured steps, each with specific algorithms. I need to learn each step, practice the moves, and gradually build up to solving the entire cube. It might be challenging, but breaking it down into smaller parts makes it manageable.\\n\\n\\nTo solve a Rubik\\'s Cube, follow this structured approach:\\n\\n1. **Understand the Structure**: Familiarize yourself with the cube\\'s layout, noting that each face has nine smaller squares and six faces in total.\\n\\n2. **Learn Notation**: Understand the basic move notations (F, R, U, etc.) and their directions (clockwise, counterclockwise, and 180-degree turns).\\n\\n3. **Step 1: Solve the Cross**:\\n - Begin with the white face.\\n - Align the white edges with their corresponding center colors.\\n\\n4. **Step 2: Solve the Corners**:\\n - Position the corners so they match adjacent center colors, using specific algorithms to insert them correctly without disrupting the cross.\\n\\n5. **Step 3: Solve the Middle Layer**:\\n - Move edge pieces between the first and last layers, using algorithms to place them correctly.\\n\\n6. **Step 4: Orient the Last Layer (OLL)**:\\n - Use algorithms to orient the last layer\\'s edges so they all face the correct way.\\n\\n7. **Step 5: Permute the Last Layer (PLL)**:\\n - Apply algorithms to permute the corners, ensuring they are in the correct positions.\\n\\n8. **Practice and Resources**:\\n - Practice each step separately, gradually building up skills.\\n - Use tutorials or guides for detailed explanations and visual aids.\\n\\nBy following these steps and practicing each algorithm, you can systematically solve the Rubik\\'s Cube.\\n<｜end▁of▁sentence｜>'"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Prepare the dataset\n",
+ "\n",
+ "prompt_style = \"\"\"Below is an instruction that describes a task, paired with a question that provides further context.\n",
+ "Write a response that appropriately answer the question.\n",
+ "Before answering, think carefully but concisely about the question and create a step-by-step chain of thoughts to ensure a logical and accurate response.\n",
+ "\n",
+ "### Instruction:\n",
+ "You are an expert programmer with advanced knowledge in Python. Your task is to provide concise and easy-to-understand solutions. Please answer the following python question.\n",
+ "\n",
+ "### Question:\n",
+ "{}\n",
+ "\n",
+ "### Response:\n",
+ "{}\n",
+ "\"\"\"\n",
+ "\n",
+ "EOS_TOKEN = tokenizer.eos_token\n",
+ "\n",
+ "\n",
+ "def formatting_prompts_func(examples):\n",
+ " prompts = examples[\"prompt\"]\n",
+ " completions = examples[\"completion\"]\n",
+ " texts = []\n",
+ " for prompt,completion in zip(prompts, completions):\n",
+ " text = prompt_style.format(prompt, completion) + EOS_TOKEN\n",
+ " texts.append(text)\n",
+ " return {\n",
+ " \"text\": texts,\n",
+ " }\n",
+ "\n",
+ "\n",
+ "dataset = load_dataset(\"sdiazlor/python-reasoning-dataset\", split=\"train\")\n",
+ "dataset = dataset.map(formatting_prompts_func, batched = True,)\n",
+ "dataset[\"text\"][0]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Train the Model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 49,
+ "referenced_widgets": [
+ "e865b621b4704786998d9e5511bcb522",
+ "08220a32e37f4b1dab636ea06a786a1f",
+ "2a76087509dd482a81c297ba44e2ae2e",
+ "1b7f8aad80c0464b819ee74b7f76d40e",
+ "6849f7a48eff4ab8a7e01a3e26da7645",
+ "6b9552cc29ab4ac7936618bf1f0b1c5c",
+ "706d0954642a4ef5b8e3d4ece2fa556e",
+ "9eea1e4a483b4298a6ad67dfca0ff122",
+ "2f1ede630a654aeebe7d80640ee9674a",
+ "e411f85b5a0047c4b510565585215493",
+ "fb6566ed643a473e892e68b55fa3ef29"
+ ]
+ },
+ "id": "95_Nn-89DhsL",
+ "outputId": "9d245291-dc8e-48be-d05c-beeb65b3769f"
+ },
+ "outputs": [],
+ "source": [
+ "# Configure the trainer\n",
+ "trainer = SFTTrainer(\n",
+ " model=model,\n",
+ " tokenizer=tokenizer,\n",
+ " train_dataset=dataset,\n",
+ " dataset_text_field=\"text\",\n",
+ " max_seq_length=2048,\n",
+ " dataset_num_proc=2,\n",
+ " packing=False,\n",
+ " args=TrainingArguments(\n",
+ " per_device_train_batch_size=2,\n",
+ " gradient_accumulation_steps=4,\n",
+ " warmup_steps=5,\n",
+ " num_train_epochs=3,\n",
+ " learning_rate=2e-4,\n",
+ " fp16=not is_bfloat16_supported(),\n",
+ " bf16=is_bfloat16_supported(),\n",
+ " logging_steps=1,\n",
+ " optim=\"adamw_8bit\",\n",
+ " weight_decay=0.01,\n",
+ " lr_scheduler_type=\"linear\",\n",
+ " seed=3407,\n",
+ " output_dir=\"outputs\",\n",
+ " report_to=\"none\",\n",
+ " ),\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1000
+ },
+ "id": "yqxqAZ7KJ4oL",
+ "outputId": "bb329be2-eced-4843-cb86-00ed8395e992"
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "==((====))== Unsloth - 2x faster free finetuning | Num GPUs = 1\n",
+ " \\\\ /| Num examples = 500 | Num Epochs = 3\n",
+ "O^O/ \\_/ \\ Batch size per device = 2 | Gradient Accumulation steps = 4\n",
+ "\\ / Total batch size = 8 | Total steps = 186\n",
+ " \"-____-\" Number of trainable parameters = 18,464,768\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
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+ " \n",
+ " 19 \n",
+ " 0.610100 \n",
+ " \n",
+ " \n",
+ " 20 \n",
+ " 0.651400 \n",
+ " \n",
+ " \n",
+ " 21 \n",
+ " 0.580700 \n",
+ " \n",
+ " \n",
+ " 22 \n",
+ " 0.620100 \n",
+ " \n",
+ " \n",
+ " 23 \n",
+ " 0.664400 \n",
+ " \n",
+ " \n",
+ " 24 \n",
+ " 0.675600 \n",
+ " \n",
+ " \n",
+ " 25 \n",
+ " 0.513200 \n",
+ " \n",
+ " \n",
+ " 26 \n",
+ " 0.498600 \n",
+ " \n",
+ " \n",
+ " 27 \n",
+ " 0.646800 \n",
+ " \n",
+ " \n",
+ " 28 \n",
+ " 0.501700 \n",
+ " \n",
+ " \n",
+ " 29 \n",
+ " 0.537800 \n",
+ " \n",
+ " \n",
+ " 30 \n",
+ " 0.592300 \n",
+ " \n",
+ " \n",
+ " 31 \n",
+ " 0.488400 \n",
+ " \n",
+ " \n",
+ " 32 \n",
+ " 0.533100 \n",
+ " \n",
+ " \n",
+ " 33 \n",
+ " 0.567700 \n",
+ " \n",
+ " \n",
+ " 34 \n",
+ " 0.565900 \n",
+ " \n",
+ " \n",
+ " 35 \n",
+ " 0.638700 \n",
+ " \n",
+ " \n",
+ " 36 \n",
+ " 0.564400 \n",
+ " \n",
+ " \n",
+ " 37 \n",
+ " 0.479600 \n",
+ " \n",
+ " \n",
+ " 38 \n",
+ " 0.644000 \n",
+ " \n",
+ " \n",
+ " 39 \n",
+ " 0.486400 \n",
+ " \n",
+ " \n",
+ " 40 \n",
+ " 0.598800 \n",
+ " \n",
+ " \n",
+ " 41 \n",
+ " 0.595200 \n",
+ " \n",
+ " \n",
+ " 42 \n",
+ " 0.508000 \n",
+ " \n",
+ " \n",
+ " 43 \n",
+ " 0.504900 \n",
+ " \n",
+ " \n",
+ " 44 \n",
+ " 0.613700 \n",
+ " \n",
+ " \n",
+ " 45 \n",
+ " 0.517800 \n",
+ " \n",
+ " \n",
+ " 46 \n",
+ " 0.571700 \n",
+ " \n",
+ " \n",
+ " 47 \n",
+ " 0.568900 \n",
+ " \n",
+ " \n",
+ " 48 \n",
+ " 0.507400 \n",
+ " \n",
+ " \n",
+ " 49 \n",
+ " 0.536600 \n",
+ " \n",
+ " \n",
+ " 50 \n",
+ " 0.681900 \n",
+ " \n",
+ " \n",
+ " 51 \n",
+ " 0.469500 \n",
+ " \n",
+ " \n",
+ " 52 \n",
+ " 0.530200 \n",
+ " \n",
+ " \n",
+ " 53 \n",
+ " 0.601400 \n",
+ " \n",
+ " \n",
+ " 54 \n",
+ " 0.531000 \n",
+ " \n",
+ " \n",
+ " 55 \n",
+ " 0.470400 \n",
+ " \n",
+ " \n",
+ " 56 \n",
+ " 0.535800 \n",
+ " \n",
+ " \n",
+ " 57 \n",
+ " 0.615800 \n",
+ " \n",
+ " \n",
+ " 58 \n",
+ " 0.557500 \n",
+ " \n",
+ " \n",
+ " 59 \n",
+ " 0.620600 \n",
+ " \n",
+ " \n",
+ " 60 \n",
+ " 0.497700 \n",
+ " \n",
+ " \n",
+ " 61 \n",
+ " 0.556100 \n",
+ " \n",
+ " \n",
+ " 62 \n",
+ " 0.561300 \n",
+ " \n",
+ " \n",
+ " 63 \n",
+ " 0.607200 \n",
+ " \n",
+ " \n",
+ " 64 \n",
+ " 0.556200 \n",
+ " \n",
+ " \n",
+ " 65 \n",
+ " 0.538400 \n",
+ " \n",
+ " \n",
+ " 66 \n",
+ " 0.529800 \n",
+ " \n",
+ " \n",
+ " 67 \n",
+ " 0.580100 \n",
+ " \n",
+ " \n",
+ " 68 \n",
+ " 0.573100 \n",
+ " \n",
+ " \n",
+ " 69 \n",
+ " 0.466100 \n",
+ " \n",
+ " \n",
+ " 70 \n",
+ " 0.498400 \n",
+ " \n",
+ " \n",
+ " 71 \n",
+ " 0.590800 \n",
+ " \n",
+ " \n",
+ " 72 \n",
+ " 0.632500 \n",
+ " \n",
+ " \n",
+ " 73 \n",
+ " 0.472400 \n",
+ " \n",
+ " \n",
+ " 74 \n",
+ " 0.523400 \n",
+ " \n",
+ " \n",
+ " 75 \n",
+ " 0.562500 \n",
+ " \n",
+ " \n",
+ " 76 \n",
+ " 0.552200 \n",
+ " \n",
+ " \n",
+ " 77 \n",
+ " 0.548400 \n",
+ " \n",
+ " \n",
+ " 78 \n",
+ " 0.523300 \n",
+ " \n",
+ " \n",
+ " 79 \n",
+ " 0.593300 \n",
+ " \n",
+ " \n",
+ " 80 \n",
+ " 0.483600 \n",
+ " \n",
+ " \n",
+ " 81 \n",
+ " 0.585400 \n",
+ " \n",
+ " \n",
+ " 82 \n",
+ " 0.554700 \n",
+ " \n",
+ " \n",
+ " 83 \n",
+ " 0.413900 \n",
+ " \n",
+ " \n",
+ " 84 \n",
+ " 0.589400 \n",
+ " \n",
+ " \n",
+ " 85 \n",
+ " 0.484100 \n",
+ " \n",
+ " \n",
+ " 86 \n",
+ " 0.461000 \n",
+ " \n",
+ " \n",
+ " 87 \n",
+ " 0.570700 \n",
+ " \n",
+ " \n",
+ " 88 \n",
+ " 0.545900 \n",
+ " \n",
+ " \n",
+ " 89 \n",
+ " 0.542300 \n",
+ " \n",
+ " \n",
+ " 90 \n",
+ " 0.502100 \n",
+ " \n",
+ " \n",
+ " 91 \n",
+ " 0.554100 \n",
+ " \n",
+ " \n",
+ " 92 \n",
+ " 0.554000 \n",
+ " \n",
+ " \n",
+ " 93 \n",
+ " 0.468700 \n",
+ " \n",
+ " \n",
+ " 94 \n",
+ " 0.535800 \n",
+ " \n",
+ " \n",
+ " 95 \n",
+ " 0.539100 \n",
+ " \n",
+ " \n",
+ " 96 \n",
+ " 0.479600 \n",
+ " \n",
+ " \n",
+ " 97 \n",
+ " 0.499100 \n",
+ " \n",
+ " \n",
+ " 98 \n",
+ " 0.518300 \n",
+ " \n",
+ " \n",
+ " 99 \n",
+ " 0.593800 \n",
+ " \n",
+ " \n",
+ " 100 \n",
+ " 0.573200 \n",
+ " \n",
+ " \n",
+ " 101 \n",
+ " 0.546400 \n",
+ " \n",
+ " \n",
+ " 102 \n",
+ " 0.599600 \n",
+ " \n",
+ " \n",
+ " 103 \n",
+ " 0.465200 \n",
+ " \n",
+ " \n",
+ " 104 \n",
+ " 0.472400 \n",
+ " \n",
+ " \n",
+ " 105 \n",
+ " 0.556100 \n",
+ " \n",
+ " \n",
+ " 106 \n",
+ " 0.498800 \n",
+ " \n",
+ " \n",
+ " 107 \n",
+ " 0.486900 \n",
+ " \n",
+ " \n",
+ " 108 \n",
+ " 0.529000 \n",
+ " \n",
+ " \n",
+ " 109 \n",
+ " 0.480100 \n",
+ " \n",
+ " \n",
+ " 110 \n",
+ " 0.525900 \n",
+ " \n",
+ " \n",
+ " 111 \n",
+ " 0.489700 \n",
+ " \n",
+ " \n",
+ " 112 \n",
+ " 0.510600 \n",
+ " \n",
+ " \n",
+ " 113 \n",
+ " 0.628300 \n",
+ " \n",
+ " \n",
+ " 114 \n",
+ " 0.413200 \n",
+ " \n",
+ " \n",
+ " 115 \n",
+ " 0.577800 \n",
+ " \n",
+ " \n",
+ " 116 \n",
+ " 0.515000 \n",
+ " \n",
+ " \n",
+ " 117 \n",
+ " 0.539300 \n",
+ " \n",
+ " \n",
+ " 118 \n",
+ " 0.459200 \n",
+ " \n",
+ " \n",
+ " 119 \n",
+ " 0.533700 \n",
+ " \n",
+ " \n",
+ " 120 \n",
+ " 0.501700 \n",
+ " \n",
+ " \n",
+ " 121 \n",
+ " 0.528400 \n",
+ " \n",
+ " \n",
+ " 122 \n",
+ " 0.475900 \n",
+ " \n",
+ " \n",
+ " 123 \n",
+ " 0.437600 \n",
+ " \n",
+ " \n",
+ " 124 \n",
+ " 0.551700 \n",
+ " \n",
+ " \n",
+ " 125 \n",
+ " 0.464600 \n",
+ " \n",
+ " \n",
+ " 126 \n",
+ " 0.442300 \n",
+ " \n",
+ " \n",
+ " 127 \n",
+ " 0.611100 \n",
+ " \n",
+ " \n",
+ " 128 \n",
+ " 0.425300 \n",
+ " \n",
+ " \n",
+ " 129 \n",
+ " 0.516900 \n",
+ " \n",
+ " \n",
+ " 130 \n",
+ " 0.469100 \n",
+ " \n",
+ " \n",
+ " 131 \n",
+ " 0.486200 \n",
+ " \n",
+ " \n",
+ " 132 \n",
+ " 0.492100 \n",
+ " \n",
+ " \n",
+ " 133 \n",
+ " 0.511100 \n",
+ " \n",
+ " \n",
+ " 134 \n",
+ " 0.559500 \n",
+ " \n",
+ " \n",
+ " 135 \n",
+ " 0.537600 \n",
+ " \n",
+ " \n",
+ " 136 \n",
+ " 0.426800 \n",
+ " \n",
+ " \n",
+ " 137 \n",
+ " 0.474200 \n",
+ " \n",
+ " \n",
+ " 138 \n",
+ " 0.543500 \n",
+ " \n",
+ " \n",
+ " 139 \n",
+ " 0.539800 \n",
+ " \n",
+ " \n",
+ " 140 \n",
+ " 0.481500 \n",
+ " \n",
+ " \n",
+ " 141 \n",
+ " 0.481400 \n",
+ " \n",
+ " \n",
+ " 142 \n",
+ " 0.562000 \n",
+ " \n",
+ " \n",
+ " 143 \n",
+ " 0.409100 \n",
+ " \n",
+ " \n",
+ " 144 \n",
+ " 0.440900 \n",
+ " \n",
+ " \n",
+ " 145 \n",
+ " 0.437700 \n",
+ " \n",
+ " \n",
+ " 146 \n",
+ " 0.427300 \n",
+ " \n",
+ " \n",
+ " 147 \n",
+ " 0.393100 \n",
+ " \n",
+ " \n",
+ " 148 \n",
+ " 0.480300 \n",
+ " \n",
+ " \n",
+ " 149 \n",
+ " 0.509300 \n",
+ " \n",
+ " \n",
+ " 150 \n",
+ " 0.450200 \n",
+ " \n",
+ " \n",
+ " 151 \n",
+ " 0.530500 \n",
+ " \n",
+ " \n",
+ " 152 \n",
+ " 0.475300 \n",
+ " \n",
+ " \n",
+ " 153 \n",
+ " 0.521300 \n",
+ " \n",
+ " \n",
+ " 154 \n",
+ " 0.519500 \n",
+ " \n",
+ " \n",
+ " 155 \n",
+ " 0.539400 \n",
+ " \n",
+ " \n",
+ " 156 \n",
+ " 0.433300 \n",
+ " \n",
+ " \n",
+ " 157 \n",
+ " 0.495400 \n",
+ " \n",
+ " \n",
+ " 158 \n",
+ " 0.415200 \n",
+ " \n",
+ " \n",
+ " 159 \n",
+ " 0.608800 \n",
+ " \n",
+ " \n",
+ " 160 \n",
+ " 0.524700 \n",
+ " \n",
+ " \n",
+ " 161 \n",
+ " 0.438700 \n",
+ " \n",
+ " \n",
+ " 162 \n",
+ " 0.504800 \n",
+ " \n",
+ " \n",
+ " 163 \n",
+ " 0.455700 \n",
+ " \n",
+ " \n",
+ " 164 \n",
+ " 0.455100 \n",
+ " \n",
+ " \n",
+ " 165 \n",
+ " 0.592300 \n",
+ " \n",
+ " \n",
+ " 166 \n",
+ " 0.565700 \n",
+ " \n",
+ " \n",
+ " 167 \n",
+ " 0.480800 \n",
+ " \n",
+ " \n",
+ " 168 \n",
+ " 0.546100 \n",
+ " \n",
+ " \n",
+ " 169 \n",
+ " 0.463100 \n",
+ " \n",
+ " \n",
+ " 170 \n",
+ " 0.573400 \n",
+ " \n",
+ " \n",
+ " 171 \n",
+ " 0.500700 \n",
+ " \n",
+ " \n",
+ " 172 \n",
+ " 0.516700 \n",
+ " \n",
+ " \n",
+ " 173 \n",
+ " 0.572000 \n",
+ " \n",
+ " \n",
+ " 174 \n",
+ " 0.411700 \n",
+ " \n",
+ " \n",
+ " 175 \n",
+ " 0.452700 \n",
+ " \n",
+ " \n",
+ " 176 \n",
+ " 0.424900 \n",
+ " \n",
+ " \n",
+ " 177 \n",
+ " 0.489200 \n",
+ " \n",
+ " \n",
+ " 178 \n",
+ " 0.574300 \n",
+ " \n",
+ " \n",
+ " 179 \n",
+ " 0.479700 \n",
+ " \n",
+ " \n",
+ " 180 \n",
+ " 0.487800 \n",
+ " \n",
+ " \n",
+ " 181 \n",
+ " 0.513700 \n",
+ " \n",
+ " \n",
+ " 182 \n",
+ " 0.492800 \n",
+ " \n",
+ " \n",
+ " 183 \n",
+ " 0.535100 \n",
+ " \n",
+ " \n",
+ " 184 \n",
+ " 0.501100 \n",
+ " \n",
+ " \n",
+ " 185 \n",
+ " 0.450400 \n",
+ " \n",
+ " \n",
+ " 186 \n",
+ " 0.484500 \n",
+ " \n",
+ " \n",
+ "
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Train the model\n",
+ "trainer_stats = trainer.train()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 501,
+ "referenced_widgets": [
+ "75dc3defc03044d5a3d5327f982f4ad9",
+ "69a1c928a91b485e899218b4c48bbd0f",
+ "2cd1ab0231be43078bf2b0861c66756b",
+ "7d6ef80065f2457ca6a584dea0ee193a",
+ "8deac706db694fd9af003a52d9e65c18",
+ "d1fdaab3815b4beebd51e5240208c14c",
+ "0c677a8b3f234e0480995a3f3f511cfc",
+ "bf95bf76909b418db87bed807a8fcd2e",
+ "60362ddf26274bbda10f888cae24b527",
+ "cf64d3a6baf545bebc84c62b2863a2a3",
+ "a023e723ac13430087c8f019e7bd2946",
+ "37b277adce0f47f58ce4f153ebbf9885",
+ "ae4269c9c5434ee79eaa31d197e382c0",
+ "d7ef28d78e19450e82bd3e42a9fab928",
+ "0b0ce59c58f947c5ab6b456c4b2b8fd8",
+ "c05b434f65a04a07880f82d5dda991e3",
+ "aa8a0d882d1047e8a6a08b04df1214ea",
+ "344f6b55db5e43c7b98750e4cbf907d1",
+ "58aa7a36dbd14511a43268242bf3486c",
+ "9b0488d548f3446d8d20af6d3ac56739",
+ "2c966a7792594a9bace7cada70bc6e20",
+ "eb694d1a1a674598bccff290c5c96077",
+ "343312cb90c84c6bbfec606bed32765a",
+ "4cbc59fddff34f44a0e99e15ff1ae102",
+ "932c5742442b421389363d2e216626d7",
+ "f57fce9a80e549609474d84b7d935b33",
+ "53f6ce3049474fbda959fd52fe9b5890",
+ "71cc82a978664061b4778a73ac25afdb",
+ "d4f41624cbea494f803bd76cd78f315f",
+ "8f02d26e18614cd38e891d5ee019bc3d",
+ "fefdd42e29544fdba8ba1dd7f28cc85d",
+ "ca279528c62c41559fff7d150a229567",
+ "3abc1f45407c4c8c9c6b6d676246aa3b",
+ "7b9eed20c6554407afb846170e2d946d",
+ "27cacb0adaa944c19d80cd9cb0ddffa9",
+ "b5037ef5dcdc43a9850bb073addc947f",
+ "cc7ce94c244548c8954b35bb92936bdd",
+ "3bcfc01fccce49ef87f62b0046d54059",
+ "4be3ffa40ad14461adeb5bfafad806f6",
+ "b3f9eb630a6a4b88857e4d9e1869ffca",
+ "91a556fcd64843fe877a580eb3e21826",
+ "6d93c3a9f44342bdbf840b6ce3d04ab6",
+ "ea83b9bb0b134206a99628d3fe66503a",
+ "a018604c1f624fb49e1064696a5533fd",
+ "8b9066519c9546a5b8ad95b239828b89",
+ "9ec9215968024471a69fa7a9a62db7bb",
+ "901c6eca0c4a40e7a9ed9fa968874787",
+ "687438cfca3140d4bc132b6b91ab616c",
+ "c16a355a4e2443509f3362504882ab7c",
+ "0e372476bcc04cd5a89a7b0626742c7e",
+ "56aac0b6cad64e7a9d672583c633c8bf",
+ "f2da104837ae4a3289e24a1bd7de2415",
+ "95a67d05e14c40c2a5bdcb322bc61af8",
+ "f8d6262c216f4226b6d26ad9961b9622",
+ "00b6d832a0c546c2afb059bd29e54991",
+ "76af8b2071e8453794f432a7c0bf63d0",
+ "51730e19fe0c4ead871b9b6cf7895e05",
+ "2c1cdcb9a80446539b3e229c5cc8ce33",
+ "7b1889d55328460fbd9c348a2a72e3c8",
+ "8bb5bfadd1984e5a8c0b27dfbb16bf9a",
+ "624a5a04f510474e90ac9aaf7ed264b1",
+ "405277a1c6944a35b1cb082de3b9dbdb",
+ "32b541d4aa6d4dac8a9dc0e5cdf9ba44",
+ "2cf66ab1c0f34d71b90ca6ac10eec6d6",
+ "c9ab627f27d7456d9de249610ffef4a1",
+ "4b5fa3674a8f467aba1089a39e2978b5",
+ "1cc3ff808e1543eb9716deba509c37ee",
+ "426ba1f107354dc78ded5f1ba400895b",
+ "02ddf97d03404b92a09a0b6a869847d8",
+ "9b22f445c87a44f9afe39de4a20f08f4",
+ "9f197e2d7fb54261acbb2d1ddb8b6a30",
+ "6d9294f45ab14b139f968dae24663783",
+ "bd5608c4250a4d35aca16aebd1dba7f2",
+ "92e84bbf66f34be3aa0da2ff046daf94",
+ "75a890e9c93543eeb2917c165f0d5d20",
+ "3d4c73330cc44748a4525c7901a281a1",
+ "4df8ff8e37004b53adcb9a5b32c40fb2"
+ ]
+ },
+ "id": "WBeMl1mh1ArJ",
+ "outputId": "4e869fbe-da2b-417b-b1d3-78c913efc4cb"
+ },
+ "outputs": [],
+ "source": [
+ "# Save to the local directory and push it to the Hub\n",
+ "model.save_pretrained(MODEL_NAME)\n",
+ "tokenizer.save_pretrained(MODEL_NAME)\n",
+ "model.save_pretrained_merged(MODEL_NAME, tokenizer, save_method=\"merged_16bit\")\n",
+ "\n",
+ "fine_tuned_model = f\"{REPO_NAME}/{MODEL_NAME}\"\n",
+ "model.push_to_hub(fine_tuned_model, safe_serialization=None)\n",
+ "tokenizer.push_to_hub(fine_tuned_model, safe_serialization=None)\n",
+ "model.push_to_hub_merged(fine_tuned_model, tokenizer, save_method=\"merged_16bit\") # for vLLM\n",
+ "model.push_to_hub_gguf(\n",
+ " f\"{fine_tuned_model}_q4_k_m\", tokenizer, quantization_method=\"q4_k_m\"\n",
+ ") # as gguf"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Run Inference"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "kR3gIAX-SM2q",
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+ "text": [
+ "Okay, so I need to find all the prime numbers between 0 and 125. Hmm, primes are numbers greater than 1 that have no divisors other than 1 and themselves. So, first, I should probably start by listing all numbers from 0 to 125 and then eliminate the non-primes.\n",
+ "\n",
+ "Wait, but 0 and 1 aren't primes. So I can ignore them. The smallest prime is 2. So maybe I should start checking from 2 onwards.\n",
+ "\n",
+ "I remember that one method to check for primes is the Sieve of Eratosthenes. That's an efficient algorithm for finding all primes up to a certain limit. Let me think about how that works. The idea is to create a list of all numbers up to the limit and then iteratively mark the multiples of each prime starting from 2. The numbers that remain unmarked are primes.\n",
+ "\n",
+ "So, applying this to the range 0-125. I'll create a list of booleans from 0 to 125, initializing them all to True except index 0 and 1 which are False. Then, for each number starting from 2, if it's still marked as prime, I'll mark all its multiples as not prime.\n",
+ "\n",
+ "Let me outline the steps:\n",
+ "\n",
+ "1. Create a list of size 126 (since it's up to 125) and initialize all to True.\n",
+ "2. Set the indices 0 and 1 to False.\n",
+ "3. For each number i starting from 2 up to the square root of 125 (which is around 11.18), if the number is still True, mark all multiples of i as False.\n",
+ "4. After processing, the indices that are still True are primes.\n",
+ "\n",
+ "Wait, but the square root is a bit more precise. The Sieve of Eratosthenes only needs to check up to the square root of the limit because any factor larger than that would have a corresponding factor smaller than the square root.\n",
+ "\n",
+ "So, for 125, the square root is about 11.18, so I'll loop i from 2 to 11.\n",
+ "\n",
+ "Let me try to simulate this:\n",
+ "\n",
+ "- Start with all True except 0 and 1.\n",
+ "- i=2: mark multiples 4,6,8,... up to 124.\n",
+ "- i=3: mark multiples 9,12,15,... up to 123.\n",
+ "- i=4: already marked, so skip.\n",
+ "- i=5: mark 25,30, etc., but since 5 is a prime, it should only mark multiples beyond 25.\n",
+ "- And so on until i=11.\n",
+ "\n",
+ "After this, the indices that are True are the primes.\n",
+ "\n",
+ "Once I have the list of primes, I can collect them into a list and return that list.\n",
+ "\n",
+ "So, putting this into Python code, I'll create the sieve, then extract the primes.\n",
+ "\n",
+ "Let me think about how to implement this. I'll write a function called get_primes_up_to_n(n) which returns the list of primes up to n.\n",
+ "\n",
+ "Wait, but the user wants primes from 0 to 125. So the function should handle that.\n",
+ "\n",
+ "Alternatively, I can create a function that takes a number and returns the primes up to that number.\n",
+ "\n",
+ "So, the steps in code:\n",
+ "\n",
+ "1. Define a function get_primes(n) that returns primes up to n.\n",
+ "2. Inside the function, create a list of booleans from 0 to n, initializing to True, then setting 0 and 1 to False.\n",
+ "3. For each i from 2 to sqrt(n) + 1, check if it's still True. If so, mark all multiples of i as False.\n",
+ "4. Collect all indices that are still True and return them as a list.\n",
+ "\n",
+ "Wait, but in Python, the square root can be calculated using math.sqrt, but since n is an integer, I should take the integer part. Or, better, loop up to int(math.sqrt(n)) + 1.\n",
+ "\n",
+ "Wait, no. The sieve only needs to check up to sqrt(n) because any composite number would have a factor less than or equal to sqrt(n). So for n=125, sqrt is ~11.18, so i should go up to 11.\n",
+ "\n",
+ "So, in code:\n",
+ "\n",
+ "import math\n",
+ "\n",
+ "def get_primes_up_to_n(n):\n",
+ " if n < 2:\n",
+ " return []\n",
+ " sieve = [True] * (n + 1)\n",
+ " sieve[0] = sieve[1] = False\n",
+ " for i in range(2, int(math.sqrt(n)) + 1):\n",
+ " if sieve[i]:\n",
+ " for j in range(i * i, n + 1, i):\n",
+ " sieve[j] = False\n",
+ " primes = [i for i, is_prime in enumerate(sieve) if is_prime]\n",
+ " return primes\n",
+ "\n",
+ "Then, call get_primes_up_to_n(125) to get the primes.\n",
+ "\n",
+ "Testing this function with n=125 should give me all primes from 2 up to 125.\n",
+ "\n",
+ "Let me see, for example, 2 is a prime, 3,5,7, etc., up to 125.\n",
+ "\n",
+ "I think this should work. So the code is straightforward.\n",
+ "\n",
+ "\n",
+ "To find all prime numbers from 0 to 125, we can use the Sieve of Eratosthenes algorithm, which efficiently identifies primes by marking non-prime numbers. Here's a concise Python solution:\n",
+ "\n",
+ "```python\n",
+ "import math\n",
+ "\n",
+ "def get_primes_up_to_n(n):\n",
+ " if n < 2:\n",
+ " return []\n",
+ " sieve = [True] * (n + 1)\n",
+ " sieve[0] = sieve[1] = False\n",
+ " for i in range(2, int(math.sqrt(n)) + 1):\n",
+ " if sieve[i]:\n",
+ " for j in range(i * i, n + 1, i):\n",
+ " sieve[j] = False\n",
+ " primes = [i for i, is_prime in enumerate(sieve) if is_prime]\n",
+ " return primes\n",
+ "\n",
+ "primes = get_primes_up_to_n(125)\n",
+ "print(primes)\n",
+ "```\n",
+ "\n",
+ "This code initializes a boolean list for numbers up to 125, marks non-primes, and returns the primes. The result is a list of primes from 2 to 125.\n",
+ "<｜end▁of▁sentence｜>\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Run inference\n",
+ "question = \"How can I get the prime numbers from 0 to 125?\"\n",
+ "\n",
+ "FastLanguageModel.for_inference(model)\n",
+ "inputs = tokenizer([prompt_style.format(question, \"\")], return_tensors=\"pt\").to(\"cuda\")\n",
+ "\n",
+ "outputs = model.generate(\n",
+ " input_ids=inputs.input_ids,\n",
+ " attention_mask=inputs.attention_mask,\n",
+ " max_new_tokens=2048,\n",
+ " use_cache=True,\n",
+ ")\n",
+ "response = tokenizer.batch_decode(outputs)\n",
+ "print(response[0].split(\"### Response:\")[1])\n"
+ ]
+ }
+ ],
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diff --git a/examples/fine-tune-modernbert-classifier.ipynb b/examples/fine-tune-modernbert-classifier.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..f123cf1b804a818feb86a7560315474800b8dbc0
--- /dev/null
+++ b/examples/fine-tune-modernbert-classifier.ipynb
@@ -0,0 +1,538 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Fine-tune ModernBERT for text classification using synthetic data\n",
+ "\n",
+ "LLMs are great general purpose models, but they are not always the best choice for a specific task. Therefore, smaller and more specialized models are important for sustainable, efficient, and cheaper AI.\n",
+ "A lack of domain sepcific datasets is a common problem for smaller and more specialized models. This is because it is difficult to find a dataset that is both representative and diverse enough for a specific task. We solve this problem by generating a synthetic dataset from an LLM using the `synthetic-data-generator`, which is available as a [Hugging Face Space](https://huggingface.co/spaces/argilla/synthetic-data-generator) or on [GitHub](https://github.com/argilla-io/synthetic-data-generator).\n",
+ "\n",
+ "In this example, we will fine-tune a ModernBERT model on a synthetic dataset generated from the synthetic-data-generator. This demonstrates the effectiveness of synthetic data and the novel ModernBERT model, which is a new and improved version of BERT models, with an 8192 token context length, significantly better downstream performance, and much faster processing speeds.\n",
+ "\n",
+ "## Install the dependencies"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Install Pytorch & other libraries\n",
+ "%pip install \"torch==2.5.0\" \"torchvision==0.20.0\" \n",
+ "%pip install \"setuptools<71.0.0\" scikit-learn \n",
+ " \n",
+ "# Install Hugging Face libraries\n",
+ "%pip install --upgrade \\\n",
+ " \"datasets==3.1.0\" \\\n",
+ " \"accelerate==1.2.1\" \\\n",
+ " \"hf-transfer==0.1.8\"\n",
+ " \n",
+ "# ModernBERT is not yet available in an official release, so we need to install it from github\n",
+ "%pip install \"git+https://github.com/huggingface/transformers.git@6e0515e99c39444caae39472ee1b2fd76ece32f1\" --upgrade"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## The problem\n",
+ "\n",
+ "The [nvidia/domain-classifier](https://huggingface.co/nvidia/domain-classifier), is a model that can classify the domain of a text which can help with curating data. This model is cool but is based on the Deberta V3 Base, which is an outdated architecture that requires custom code to run, has a context length of 512 tokens, and is not as fast as the ModernBERT model. The labels for the model are:\n",
+ "\n",
+ "```\n",
+ "'Adult', 'Arts_and_Entertainment', 'Autos_and_Vehicles', 'Beauty_and_Fitness', 'Books_and_Literature', 'Business_and_Industrial', 'Computers_and_Electronics', 'Finance', 'Food_and_Drink', 'Games', 'Health', 'Hobbies_and_Leisure', 'Home_and_Garden', 'Internet_and_Telecom', 'Jobs_and_Education', 'Law_and_Government', 'News', 'Online_Communities', 'People_and_Society', 'Pets_and_Animals', 'Real_Estate', 'Science', 'Sensitive_Subjects', 'Shopping', 'Sports', 'Travel_and_Transportation'\n",
+ "```\n",
+ "\n",
+ "The data on which the model was trained is not available, so we cannot use it for our purposes. We can however generate a synthetic data to solve this problem."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "vscode": {
+ "languageId": "plaintext"
+ }
+ },
+ "source": [
+ "## Let's generate some data\n",
+ "\n",
+ "Let's go to the [hosted Hugging Face Space](https://huggingface.co/spaces/argilla/synthetic-data-generator) to generate the data. This is done in three steps 1) we come up with a dataset description, 2) iterate on the task configuration, and 3) generate and push the data to Hugging Face. A more detailed flow can be found in [this blogpost](https://huggingface.co/blog/synthetic-data-generator). \n",
+ "\n",
+ "\n",
+ "\n",
+ "For this example, we will generate 1000 examples with a temperature of 1. After some iteration, we come up with the following system prompt:\n",
+ "\n",
+ "```\n",
+ "Long texts (at least 2000 words) from various media sources like Wikipedia, Reddit, Common Crawl, websites, commercials, online forums, books, newspapers and folders that cover multiple topics. Classify the text based on its main subject matter into one of the following categories\n",
+ "```\n",
+ "\n",
+ "We press the \"Push to Hub\" button and wait for the data to be generated. This takes a few minutes and we end up with a dataset with 1000 examples. The labels are nicely distributed across the categories, varied in length, and the texts look diverse and interesting.\n",
+ "\n",
+ "\n",
+ "\n",
+ "The data is pushed to Argilla to so we recommend inspecting and validating the labels before finetuning the model."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Finetuning the ModernBERT model\n",
+ "\n",
+ "We mostly rely on the blog from [Phillip Schmid](https://www.philschmid.de/fine-tune-modern-bert-in-2025). I will basic consumer hardware, my Apple M1 Max with 32GB of shared memory. We will use the `datasets` library to load the data and the `transformers` library to finetune the model."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/davidberenstein/Documents/programming/argilla/synthetic-data-generator/.venv/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+ " from .autonotebook import tqdm as notebook_tqdm\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "{'text': 'Recently, there has been an increase in property values within the suburban areas of several cities due to improvements in infrastructure and lifestyle amenities such as parks, retail stores, and educational institutions nearby. Additionally, new housing developments are emerging, catering to different family needs with varying sizes and price ranges. These changes have influenced investment decisions for many looking to buy or sell properties.',\n",
+ " 'label': 14}"
+ ]
+ },
+ "execution_count": 1,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from datasets import load_dataset\n",
+ "from datasets.arrow_dataset import Dataset\n",
+ "from datasets.dataset_dict import DatasetDict, IterableDatasetDict\n",
+ "from datasets.iterable_dataset import IterableDataset\n",
+ " \n",
+ "# Dataset id from huggingface.co/dataset\n",
+ "dataset_id = \"argilla/synthetic-domain-text-classification\"\n",
+ " \n",
+ "# Load raw dataset\n",
+ "train_dataset = load_dataset(dataset_id, split='train')\n",
+ "\n",
+ "split_dataset = train_dataset.train_test_split(test_size=0.1)\n",
+ "split_dataset['train'][0]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "First, we need to tokenize the data. We will use the `AutoTokenizer` class from the `transformers` library to load the tokenizer."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Map: 100%|██████████| 900/900 [00:00<00:00, 4787.61 examples/s]\n",
+ "Map: 100%|██████████| 100/100 [00:00<00:00, 4163.70 examples/s]\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "dict_keys(['labels', 'input_ids', 'attention_mask'])"
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from transformers import AutoTokenizer\n",
+ " \n",
+ "# Model id to load the tokenizer\n",
+ "model_id = \"answerdotai/ModernBERT-base\"\n",
+ "\n",
+ "# Load Tokenizer\n",
+ "tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
+ " \n",
+ "# Tokenize helper function\n",
+ "def tokenize(batch):\n",
+ " return tokenizer(batch['text'], padding=True, truncation=True, return_tensors=\"pt\")\n",
+ " \n",
+ "# Tokenize dataset\n",
+ "if \"label\" in split_dataset[\"train\"].features.keys():\n",
+ " split_dataset = split_dataset.rename_column(\"label\", \"labels\") # to match Trainer\n",
+ "tokenized_dataset = split_dataset.map(tokenize, batched=True, remove_columns=[\"text\"])\n",
+ " \n",
+ "tokenized_dataset[\"train\"].features.keys()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now, we need to prepare the model. We will use the `AutoModelForSequenceClassification` class from the `transformers` library to load the model."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Some weights of ModernBertForSequenceClassification were not initialized from the model checkpoint at answerdotai/ModernBERT-base and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
+ "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
+ ]
+ }
+ ],
+ "source": [
+ "from transformers import AutoModelForSequenceClassification\n",
+ " \n",
+ "# Model id to load the tokenizer\n",
+ "model_id = \"answerdotai/ModernBERT-base\"\n",
+ " \n",
+ "# Prepare model labels - useful for inference\n",
+ "labels = tokenized_dataset[\"train\"].features[\"labels\"].names\n",
+ "num_labels = len(labels)\n",
+ "label2id, id2label = dict(), dict()\n",
+ "for i, label in enumerate(labels):\n",
+ " label2id[label] = str(i)\n",
+ " id2label[str(i)] = label\n",
+ " \n",
+ "# Download the model from huggingface.co/models\n",
+ "model = AutoModelForSequenceClassification.from_pretrained(\n",
+ " model_id, num_labels=num_labels, label2id=label2id, id2label=id2label,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We will use a simple F1 score as the evaluation metric."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "from sklearn.metrics import f1_score\n",
+ " \n",
+ "# Metric helper method\n",
+ "def compute_metrics(eval_pred):\n",
+ " predictions, labels = eval_pred\n",
+ " predictions = np.argmax(predictions, axis=1)\n",
+ " score = f1_score(\n",
+ " labels, predictions, labels=labels, pos_label=1, average=\"weighted\"\n",
+ " )\n",
+ " return {\"f1\": float(score) if score == 1 else score}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Finally, we need to define the training arguments. We will use the `TrainingArguments` class from the `transformers` library to define the training arguments."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/davidberenstein/Documents/programming/argilla/synthetic-data-generator/.venv/lib/python3.11/site-packages/transformers/training_args.py:2241: UserWarning: `use_mps_device` is deprecated and will be removed in version 5.0 of 🤗 Transformers. `mps` device will be used by default if available similar to the way `cuda` device is used.Therefore, no action from user is required. \n",
+ " warnings.warn(\n"
+ ]
+ }
+ ],
+ "source": [
+ "from huggingface_hub import HfFolder\n",
+ "from transformers import Trainer, TrainingArguments\n",
+ " \n",
+ "# Define training args\n",
+ "training_args = TrainingArguments(\n",
+ " output_dir= \"ModernBERT-domain-classifier\",\n",
+ " per_device_train_batch_size=32,\n",
+ " per_device_eval_batch_size=16,\n",
+ " learning_rate=5e-5,\n",
+ "\t\tnum_train_epochs=5,\n",
+ " bf16=True, # bfloat16 training \n",
+ " optim=\"adamw_torch_fused\", # improved optimizer \n",
+ " # logging & evaluation strategies\n",
+ " logging_strategy=\"steps\",\n",
+ " logging_steps=100,\n",
+ " eval_strategy=\"epoch\",\n",
+ " save_strategy=\"epoch\",\n",
+ " save_total_limit=2,\n",
+ " load_best_model_at_end=True,\n",
+ " use_mps_device=True,\n",
+ " metric_for_best_model=\"f1\",\n",
+ " # push to hub parameters\n",
+ " push_to_hub=True,\n",
+ " hub_strategy=\"every_save\",\n",
+ " hub_token=HfFolder.get_token(),\n",
+ ")\n",
+ " \n",
+ "# Create a Trainer instance\n",
+ "trainer = Trainer(\n",
+ " model=model,\n",
+ " args=training_args,\n",
+ " train_dataset=tokenized_dataset[\"train\"],\n",
+ " eval_dataset=tokenized_dataset[\"test\"],\n",
+ " compute_metrics=compute_metrics,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " \n",
+ " 20%|██ | 29/145 [11:32<33:16, 17.21s/it]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "{'eval_loss': 0.729780912399292, 'eval_f1': 0.7743598318036522, 'eval_runtime': 3.5337, 'eval_samples_per_second': 28.299, 'eval_steps_per_second': 1.981, 'epoch': 1.0}\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " \n",
+ " 40%|████ | 58/145 [22:57<25:56, 17.89s/it]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "{'eval_loss': 0.4369044005870819, 'eval_f1': 0.8310764765820946, 'eval_runtime': 3.3266, 'eval_samples_per_second': 30.061, 'eval_steps_per_second': 2.104, 'epoch': 2.0}\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " \n",
+ " 60%|██████ | 87/145 [35:16<17:06, 17.70s/it]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "{'eval_loss': 0.6091340184211731, 'eval_f1': 0.8399274488570763, 'eval_runtime': 3.2772, 'eval_samples_per_second': 30.514, 'eval_steps_per_second': 2.136, 'epoch': 3.0}\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " 69%|██████▉ | 100/145 [41:03<18:02, 24.06s/it]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "{'loss': 0.7663, 'grad_norm': 7.232136249542236, 'learning_rate': 1.5517241379310346e-05, 'epoch': 3.45}\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " \n",
+ " 80%|████████ | 116/145 [47:23<08:50, 18.30s/it]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "{'eval_loss': 0.43516409397125244, 'eval_f1': 0.8797674004703547, 'eval_runtime': 3.2975, 'eval_samples_per_second': 30.326, 'eval_steps_per_second': 2.123, 'epoch': 4.0}\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " \n",
+ "100%|██████████| 145/145 [1:00:40<00:00, 19.18s/it]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "{'eval_loss': 0.39272159337997437, 'eval_f1': 0.8914389523348718, 'eval_runtime': 3.5564, 'eval_samples_per_second': 28.118, 'eval_steps_per_second': 1.968, 'epoch': 5.0}\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|██████████| 145/145 [1:00:42<00:00, 25.12s/it]\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "{'train_runtime': 3642.7783, 'train_samples_per_second': 1.235, 'train_steps_per_second': 0.04, 'train_loss': 0.535627057634551, 'epoch': 5.0}\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "events.out.tfevents.1735555878.Davids-MacBook-Pro.local.23438.0: 100%|██████████| 9.32k/9.32k [00:00<00:00, 55.0kB/s]\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "CommitInfo(commit_url='https://huggingface.co/davidberenstein1957/domain-classifier/commit/915f4b03c230cc8f376f13729728f14347400041', commit_message='End of training', commit_description='', oid='915f4b03c230cc8f376f13729728f14347400041', pr_url=None, repo_url=RepoUrl('https://huggingface.co/davidberenstein1957/domain-classifier', endpoint='https://huggingface.co', repo_type='model', repo_id='davidberenstein1957/domain-classifier'), pr_revision=None, pr_num=None)"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "trainer.train()\n",
+ "# Save processor and create model card\n",
+ "tokenizer.save_pretrained(\"ModernBERT-domain-classifier\")\n",
+ "trainer.create_model_card()\n",
+ "trainer.push_to_hub()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We get an F1 score of 0.89 on the test set, which is pretty good for the small dataset and time spent."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Run inference\n",
+ "\n",
+ "We can now load the model and run inference."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Device set to use mps:0\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[{'label': 'health', 'score': 0.6779336333274841}]"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from transformers import pipeline\n",
+ " \n",
+ "# load model from huggingface.co/models using our repository id\n",
+ "classifier = pipeline(\n",
+ " task=\"text-classification\", \n",
+ " model=\"argilla/ModernBERT-domain-classifier\", \n",
+ " device=0,\n",
+ ")\n",
+ " \n",
+ "sample = \"Smoking is bad for your health.\"\n",
+ " \n",
+ "classifier(sample)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Conclusion\n",
+ "\n",
+ "We have shown that we can generate a synthetic dataset from an LLM and finetune a ModernBERT model on it. This the effectiveness of synthetic data and the novel ModernBERT model, which is new and improved version of BERT models, with 8192 token context length, significantly better downstream performance, and much faster processing speeds. \n",
+ "\n",
+ "Pretty cool for 20 minutes of generating data, and an hour of fine-tuning on consumer hardware."
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": ".venv",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.11"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/fine-tune-modernbert-rag.ipynb b/examples/fine-tune-modernbert-rag.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..457addb286f4cf157f6ad9855a5c6baf7c6ac451
--- /dev/null
+++ b/examples/fine-tune-modernbert-rag.ipynb
@@ -0,0 +1,980 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Fine-tune ModernBERT with Synthetic Data for RAG\n",
+ "\n",
+ "This notebook demonstrates the fine-tuning process of `modernbert-embed-base` using synthetic data tailored for the Retrieval-Augmented Generation (RAG) model.\n",
+ "\n",
+ "It provides a complete walkthrough of the fine-tuning process after generating synthetic data using the Synthetic Data Generator. For a comprehensive explanation of the methodology and additional details, refer to the blog post: [Fine-tune ModernBERT for RAG with Synthetic Data](https://huggingface.co/blog/fine-tune-modernbert-for-rag-with-synthetic-data)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Getting Started"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Install the Dependencies"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!pip install torch\n",
+ "!pip install datasets\n",
+ "!pip install sentence-transformers\n",
+ "!pip install haystack-ai\n",
+ "!pip install git+https://github.com/huggingface/transformers.git # for the latest version of transformers"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Import the Required Libraries"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import torch\n",
+ "from torch.utils.data import DataLoader\n",
+ "\n",
+ "from datasets import load_dataset, concatenate_datasets, Dataset, DatasetDict\n",
+ "\n",
+ "\n",
+ "from sentence_transformers import (\n",
+ " SentenceTransformer,\n",
+ " SentenceTransformerModelCardData,\n",
+ " CrossEncoder,\n",
+ " InputExample,\n",
+ " SentenceTransformerTrainer,\n",
+ ")\n",
+ "from sentence_transformers.losses import TripletLoss\n",
+ "from sentence_transformers.training_args import (\n",
+ " SentenceTransformerTrainingArguments,\n",
+ " BatchSamplers,\n",
+ ")\n",
+ "from sentence_transformers.evaluation import TripletEvaluator\n",
+ "from sentence_transformers.cross_encoder.evaluation import CECorrelationEvaluator\n",
+ "\n",
+ "\n",
+ "from haystack import Document, Pipeline\n",
+ "from haystack.document_stores.in_memory import InMemoryDocumentStore\n",
+ "from haystack.components.embedders import (\n",
+ " SentenceTransformersDocumentEmbedder,\n",
+ " SentenceTransformersTextEmbedder,\n",
+ ")\n",
+ "from haystack.components.rankers import SentenceTransformersDiversityRanker\n",
+ "from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever\n",
+ "from haystack.components.builders import ChatPromptBuilder\n",
+ "from haystack.components.generators.chat import HuggingFaceAPIChatGenerator\n",
+ "from haystack.dataclasses import ChatMessage\n",
+ "from haystack.utils import Secret\n",
+ "from haystack.utils.hf import HFGenerationAPIType"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Configure the Environment"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "MODEL = \"nomic-ai/modernbert-embed-base\"\n",
+ "REPO_NAME = \"sdiazlor\" # your HF username here\n",
+ "MODEL_NAME_BIENCODER = \"modernbert-embed-base-biencoder-human-rights\"\n",
+ "MODEL_NAME_CROSSENCODER = \"modernbert-embed-base-crossencoder-human-rights\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Using device: mps\n"
+ ]
+ }
+ ],
+ "source": [
+ "if torch.backends.mps.is_available():\n",
+ " device = \"mps\"\n",
+ "elif torch.cuda.is_available():\n",
+ " device = \"cuda\"\n",
+ "else:\n",
+ " device = \"cpu\"\n",
+ "\n",
+ "print(f\"Using device: {device}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Pre-process the Synthetic Data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Dataset({\n",
+ " features: ['context', 'question', 'response', 'positive_retrieval', 'negative_retrieval', 'positive_reranking', 'negative_reranking'],\n",
+ " num_rows: 1000\n",
+ "})"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Combine the generated datasets from files and prompts\n",
+ "\n",
+ "dataset_rag_from_file = load_dataset(f\"{REPO_NAME}/rag-human-rights-from-files\", split=\"train\")\n",
+ "dataset_rag_from_prompt = load_dataset(f\"{REPO_NAME}/rag-human-rights-from-prompt\", split=\"train\")\n",
+ "\n",
+ "combined_rag_dataset = concatenate_datasets(\n",
+ " [dataset_rag_from_file, dataset_rag_from_prompt]\n",
+ ")\n",
+ "\n",
+ "combined_rag_dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Dataset({\n",
+ " features: ['context', 'question', 'response', 'positive_retrieval', 'negative_retrieval', 'positive_reranking', 'negative_reranking'],\n",
+ " num_rows: 828\n",
+ "})"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Filter out examples with empty or NaN values\n",
+ "\n",
+ "def filter_empty_or_nan(example):\n",
+ " return all(\n",
+ " value is not None and str(value).strip() != \"\" for value in example.values()\n",
+ " )\n",
+ "\n",
+ "filtered_rag_dataset = combined_rag_dataset.filter(filter_empty_or_nan).shuffle(seed=42)\n",
+ "filtered_rag_dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Dataset({\n",
+ " features: ['anchor', 'positive', 'negative'],\n",
+ " num_rows: 828\n",
+ "})\n",
+ "Dataset({\n",
+ " features: ['anchor', 'positive'],\n",
+ " num_rows: 828\n",
+ "})\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Rename, select and reorder columns according to the expected format for the SentenceTransformer and CrossEncoder models\n",
+ "\n",
+ "def rename_and_reorder_columns(dataset, rename_map, selected_columns):\n",
+ " for old_name, new_name in rename_map.items():\n",
+ " if old_name in dataset.column_names:\n",
+ " dataset = dataset.rename_column(old_name, new_name)\n",
+ " dataset = dataset.select_columns(selected_columns)\n",
+ " return dataset\n",
+ "\n",
+ "clean_rag_dataset_biencoder = rename_and_reorder_columns(\n",
+ " filtered_rag_dataset,\n",
+ " rename_map={\"context\": \"anchor\", \"positive_retrieval\": \"positive\", \"negative_retrieval\": \"negative\"},\n",
+ " selected_columns=[\"anchor\", \"positive\", \"negative\"],\n",
+ ")\n",
+ "\n",
+ "clean_rag_dataset_crossencoder = rename_and_reorder_columns(\n",
+ " filtered_rag_dataset,\n",
+ " rename_map={\"context\": \"anchor\", \"positive_retrieval\": \"positive\"}, #TODO\n",
+ " selected_columns=[\"anchor\", \"positive\"],\n",
+ ")\n",
+ "\n",
+ "print(clean_rag_dataset_biencoder)\n",
+ "print(clean_rag_dataset_crossencoder)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at Snowflake/snowflake-arctic-embed-m-v1.5 and are newly initialized: ['classifier.bias', 'classifier.weight', 'pooler.dense.bias', 'pooler.dense.weight']\n",
+ "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "406c4d22f43f41d592d3b94da2955444",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Map: 0%| | 0/828 [00:00\n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " Epoch \n",
+ " Training Loss \n",
+ " Validation Loss \n",
+ " Cosine Accuracy \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " No log \n",
+ " 3.655929 \n",
+ " 0.969880 \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " 14.374000 \n",
+ " 3.498395 \n",
+ " 0.981928 \n",
+ " \n",
+ " \n",
+ "
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "faad6e9752f34babadff7a966ae55d87",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Computing widget examples: 0%| | 0/1 [00:00\n",
+ "🚅 Components\n",
+ " - text_embedder: SentenceTransformersTextEmbedder\n",
+ " - retriever: InMemoryEmbeddingRetriever\n",
+ " - ranker: SentenceTransformersDiversityRanker\n",
+ " - prompt_builder: ChatPromptBuilder\n",
+ " - llm: HuggingFaceAPIChatGenerator\n",
+ "🛤️ Connections\n",
+ " - text_embedder.embedding -> retriever.query_embedding (List[float])\n",
+ " - retriever.documents -> ranker.documents (List[Document])\n",
+ " - ranker.documents -> prompt_builder.documents (List[Document])\n",
+ " - prompt_builder.prompt -> llm.messages (List[ChatMessage])"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Connect the components to each other\n",
+ "\n",
+ "rag_pipeline.connect(\"text_embedder.embedding\", \"retriever.query_embedding\")\n",
+ "rag_pipeline.connect(\"retriever.documents\", \"ranker.documents\")\n",
+ "rag_pipeline.connect(\"ranker\", \"prompt_builder\")\n",
+ "rag_pipeline.connect(\"prompt_builder.prompt\", \"llm.messages\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "80c813c847524f1493067f6dbe65c725",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Batches: 0%| | 0/1 [00:00\n",
+ "\n",
+ "For this example, we will generate 5000 chat data examples for a single turn in the conversation. All examples have been generated with a temperature of 1. After some iteration, we come up with the following system prompt:\n",
+ "\n",
+ "```\n",
+ "You are an AI assistant who provides brief and to-the-point responses with logical step-by-step reasoning. Your purpose is to offer straightforward explanations and answers so that you can get to the heart of the issue. Respond with extremely concise, direct justifications and evidence-based conclusions. User questions are direct and concise.\n",
+ "```\n",
+ "\n",
+ "We press the \"Push to Hub\" button and wait for the data to be generated. This takes a few hours and we end up with a dataset with 5000 examples, which is the maximum number of examples we can generate in a single run. You can scale this by deploying a private instance of the Synthetic Data Generator. \n",
+ "\n",
+ "\n",
+ "\n",
+ "The data is pushed to Argilla too so we recommend inspecting and validating the the data before finetuning the actual model. We applied some basic filters and transformations to the data to make it more suitable for fine-tuning.\n",
+ "\n",
+ "## Fine-tune the model\n",
+ "\n",
+ "We will use TRL to fine-tune the model. It is part of the Hugging Face ecosystem and works seamlessly on top of datasets generated by the synthetic data generator without needing to do any data transformations.\n",
+ "\n",
+ "### Load the model\n",
+ "\n",
+ "We will first load the model and tokenizer and set up the chat format."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Import necessary libraries\n",
+ "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
+ "from datasets import load_dataset\n",
+ "from trl import SFTConfig, SFTTrainer, setup_chat_format\n",
+ "import torch\n",
+ "import os\n",
+ "\n",
+ "device = (\n",
+ " \"cuda\"\n",
+ " if torch.cuda.is_available()\n",
+ " else \"mps\" if torch.backends.mps.is_available() else \"cpu\"\n",
+ ")\n",
+ "\n",
+ "# Load the model and tokenizer\n",
+ "model_name = \"HuggingFaceTB/SmolLM2-360M\"\n",
+ "model = AutoModelForCausalLM.from_pretrained(\n",
+ " pretrained_model_name_or_path=model_name\n",
+ ")\n",
+ "tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path=model_name)\n",
+ "\n",
+ "# Set up the chat format\n",
+ "model, tokenizer = setup_chat_format(model=model, tokenizer=tokenizer)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Test the base model\n",
+ "\n",
+ "We will first test the base model to see how it performs on the task. During this step we will also generate a prompt for the model to respond to, to see how it performs on the task."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Device set to use mps:0\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[{'generated_text': 'What is the primary function of mitochondria within a cell?\\n\\nMitochondria are the powerhouses of the cell. They are responsible for the production of ATP (adenosine triphosphate) and the energy required for cellular processes.\\n\\nWhat is the function of the mitochondria in the cell?\\n\\nThe mitochondria are the powerhouses of the cell. They are responsible for the production of ATP (adenosine triphosphate) and the energy required for cellular processes.\\n\\nWhat is the function of the mitochondria in the cell?\\n\\nThe'}]"
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from transformers import pipeline\n",
+ "\n",
+ "prompt = \"What is the primary function of mitochondria within a cell?\"\n",
+ "\n",
+ "pipe = pipeline(\"text-generation\", model=model, tokenizer=tokenizer, device=device)\n",
+ "pipe(prompt, max_new_tokens=100)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Load the dataset\n",
+ "\n",
+ "For fine-tuning, we need to load the dataset and tokenize it. We will use the `synthetic-concise-reasoning-sft-filtered` dataset that we generated in the previous step."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Map: 100%|██████████| 4133/4133 [00:00<00:00, 18478.53 examples/s]\n"
+ ]
+ }
+ ],
+ "source": [
+ "from datasets import load_dataset\n",
+ "\n",
+ "ds = load_dataset(\"argilla/synthetic-concise-reasoning-sft-filtered\")\n",
+ "def tokenize_function(examples):\n",
+ " examples[\"text\"] = tokenizer.apply_chat_template([{\"role\": \"user\", \"content\": examples[\"prompt\"].strip()}, {\"role\": \"assistant\", \"content\": examples[\"completion\"].strip()}], tokenize=False)\n",
+ " return examples\n",
+ "ds = ds.map(tokenize_function)\n",
+ "ds = ds.shuffle()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Fine-tune the model\n",
+ "\n",
+ "We will now fine-tune the model. We will use the `SFTTrainer` from the `trl` library to fine-tune the model. We will use a batch size of 4 and a learning rate of 5e-5. We will also use the `use_mps_device` flag to use the MPS device if available."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "os.environ[\"PYTORCH_MPS_HIGH_WATERMARK_RATIO\"] = \"0.0\"\n",
+ "\n",
+ "# Configure the SFTTrainer\n",
+ "sft_config = SFTConfig(\n",
+ " output_dir=\"./sft_output\",\n",
+ " num_train_epochs=1,\n",
+ " per_device_train_batch_size=4, # Set according to your GPU memory capacity\n",
+ " learning_rate=5e-5, # Common starting point for fine-tuning\n",
+ " logging_steps=100, # Frequency of logging training metrics\n",
+ " use_mps_device= True if device == \"mps\" else False,\n",
+ " hub_model_id=\"argilla/SmolLM2-360M-synthetic-concise-reasoning\", # Set a unique name for your model\n",
+ " push_to_hub=True,\n",
+ ")\n",
+ "\n",
+ "# Initialize the SFTTrainer\n",
+ "trainer = SFTTrainer(\n",
+ " model=model,\n",
+ " args=sft_config,\n",
+ " train_dataset=ds[\"train\"],\n",
+ " tokenizer=tokenizer,\n",
+ ")\n",
+ "trainer.train()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "```\n",
+ "# {'loss': 1.4498, 'grad_norm': 2.3919131755828857, 'learning_rate': 4e-05, 'epoch': 0.1}\n",
+ "# {'loss': 1.362, 'grad_norm': 1.6650595664978027, 'learning_rate': 3e-05, 'epoch': 0.19}\n",
+ "# {'loss': 1.3778, 'grad_norm': 1.4778285026550293, 'learning_rate': 2e-05, 'epoch': 0.29}\n",
+ "# {'loss': 1.3735, 'grad_norm': 2.1424977779388428, 'learning_rate': 1e-05, 'epoch': 0.39}\n",
+ "# {'loss': 1.3512, 'grad_norm': 2.3498542308807373, 'learning_rate': 0.0, 'epoch': 0.48}\n",
+ "# {'train_runtime': 1911.514, 'train_samples_per_second': 1.046, 'train_steps_per_second': 0.262, 'train_loss': 1.3828572998046875, 'epoch': 0.48}\n",
+ "```\n",
+ "\n",
+ "For the example, we did not use a specific validation set but we can see the loss is decreasing, so we assume the model is generalsing well to the training data. To get a better understanding of the model's performance, let's test it again with the same prompt.\n",
+ "\n",
+ "### Run inference\n",
+ "\n",
+ "We can now run inference with [the fine-tuned model](https://huggingface.co/argilla/SmolLM2-360M-synthetic-concise-reasoning/blob/main/README.md)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Device set to use mps\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "'The primary function of mitochondria is to generate energy for the cell. They are organelles found in eukaryotic cells that convert nutrients into ATP (adenosine triphosphate), which is the primary source of energy for cellular processes.\\nMitochondria are responsible for:\\n\\nEnergy production: Mitochondria produce ATP through a process called oxidative phosphorylation, which involves the transfer of electrons from food molecules to oxygen.\\nEnergy storage: Mitochondria store energy in the form of adenosine triphosphate (ATP), which is used by the cell for various cellular processes.\\nCellular respiration: Mitochondria also participate in cellular respiration, a'"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "prompt = \"What is the primary function of mitochondria within a cell?\"\n",
+ "\n",
+ "generator = pipeline(\n",
+ " \"text-generation\",\n",
+ " model=\"argilla/SmolLM2-360M-synthetic-concise-reasoning\",\n",
+ " device=\"mps\",\n",
+ ")\n",
+ "generator(\n",
+ " [{\"role\": \"user\", \"content\": prompt}], max_new_tokens=128, return_full_text=False\n",
+ ")[0][\"generated_text\"]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Conclusion\n",
+ "\n",
+ "We have fine-tuned a SmolLM2 model on a synthetic dataset generated from a large language model. We have seen that the model performs well on the task and that the synthetic data is a great way to generate diverse and representative data for supervised fine-tuning. \n",
+ "\n",
+ "In practice, you would likely want to spend more time on the data quality and fine-tuning the model but the flow shows the Synthetic Data Generator is a great tool to generate synthetic data for any task.\n",
+ "\n",
+ "Overall, I think it is pretty cool for a couple of hours of generation and fine-tuning on consumer hardware.\n"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": ".venv",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/hf-dedicated-or-tgi-deployment.py b/examples/hf-dedicated-or-tgi-deployment.py
new file mode 100644
index 0000000000000000000000000000000000000000..0c726db15b6d7da081b57c56227576d8354780f4
--- /dev/null
+++ b/examples/hf-dedicated-or-tgi-deployment.py
@@ -0,0 +1,19 @@
+# /// script
+# requires-python = ">=3.11,<3.12"
+# dependencies = [
+# "synthetic-dataset-generator",
+# ]
+# ///
+import os
+
+from synthetic_dataset_generator import launch
+
+os.environ["HF_TOKEN"] = "hf_..." # push the data to huggingface
+os.environ["HUGGINGFACE_BASE_URL"] = "http://127.0.0.1:3000/" # dedicated endpoint/TGI
+os.environ["MAGPIE_PRE_QUERY_TEMPLATE"] = "llama3" # magpie template
+os.environ["TOKENIZER_ID"] = (
+ "meta-llama/Llama-3.1-8B-Instruct" # tokenizer for model hosted on endpoint
+)
+os.environ["MODEL"] = None # model is linked to endpoint
+
+launch()
diff --git a/examples/hf-serverless-deployment-deepseek.py b/examples/hf-serverless-deployment-deepseek.py
new file mode 100644
index 0000000000000000000000000000000000000000..e4c7097235ac6437497a08983bc0856c88ae7b6c
--- /dev/null
+++ b/examples/hf-serverless-deployment-deepseek.py
@@ -0,0 +1,16 @@
+# /// script
+# requires-python = ">=3.11,<3.12"
+# dependencies = [
+# "synthetic-dataset-generator",
+# ]
+# ///
+import os
+
+from synthetic_dataset_generator import launch
+
+os.environ["HF_TOKEN"] = "hf_..." # push the data to huggingface
+os.environ["MODEL"] = "deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B" # use model for instructions
+os.environ["MAGPIE_PRE_QUERY_TEMPLATE"] = "<｜begin▁of▁sentence｜>User: " # use the custom template for the model
+
+
+launch()
diff --git a/examples/hf-serverless-deployment.py b/examples/hf-serverless-deployment.py
new file mode 100644
index 0000000000000000000000000000000000000000..ba7550d3a4005543bec3092c4838b9cb77a09197
--- /dev/null
+++ b/examples/hf-serverless-deployment.py
@@ -0,0 +1,15 @@
+# /// script
+# requires-python = ">=3.11,<3.12"
+# dependencies = [
+# "synthetic-dataset-generator",
+# ]
+# ///
+import os
+
+from synthetic_dataset_generator import launch
+
+os.environ["HF_TOKEN"] = "hf_..." # push the data to huggingface
+os.environ["MODEL"] = "meta-llama/Llama-3.1-8B-Instruct" # use model for generation
+os.environ["MAGPIE_PRE_QUERY_TEMPLATE"] = "llama3" # use the template for the model
+
+launch()
diff --git a/examples/hf-serverless-different-model-for-completion.py b/examples/hf-serverless-different-model-for-completion.py
new file mode 100644
index 0000000000000000000000000000000000000000..28d06db099d2aac6923affc7eebe5958813b3765
--- /dev/null
+++ b/examples/hf-serverless-different-model-for-completion.py
@@ -0,0 +1,16 @@
+# /// script
+# requires-python = ">=3.11,<3.12"
+# dependencies = [
+# "synthetic-dataset-generator",
+# ]
+# ///
+import os
+
+from synthetic_dataset_generator import launch
+
+os.environ["HF_TOKEN"] = "hf_..." # push the data to huggingface
+os.environ["MODEL"] = "meta-llama/Llama-3.1-8B-Instruct" # use model for instruction generation
+os.environ["MODEL_COMPLETION"] = "meta-llama/Llama-3.1-70B-Instruct" # use model for completion generation
+os.environ["MAGPIE_PRE_QUERY_TEMPLATE"] = "llama3" # use the template for the model
+
+launch()
diff --git a/examples/ollama-deployment.py b/examples/ollama-deployment.py
new file mode 100644
index 0000000000000000000000000000000000000000..bd32be1ee3abfe4148c138727276bbf3ad9f22d4
--- /dev/null
+++ b/examples/ollama-deployment.py
@@ -0,0 +1,22 @@
+# /// script
+# requires-python = ">=3.11,<3.12"
+# dependencies = [
+# "synthetic-dataset-generator",
+# ]
+# ///
+# ollama serve
+# ollama run qwen2.5:32b-instruct-q5_K_S
+import os
+
+from synthetic_dataset_generator import launch
+
+os.environ["HF_TOKEN"] = "hf_..." # push the data to huggingface
+os.environ["OLLAMA_BASE_URL"] = "http://127.0.0.1:11434/" # ollama base url
+os.environ["MODEL"] = "qwen2.5:32b-instruct-q5_K_S" # model id
+os.environ["TOKENIZER_ID"] = "Qwen/Qwen2.5-32B-Instruct" # tokenizer id
+os.environ["MAGPIE_PRE_QUERY_TEMPLATE"] = "qwen2"
+os.environ["MAX_NUM_ROWS"] = "10000"
+os.environ["DEFAULT_BATCH_SIZE"] = "2"
+os.environ["MAX_NUM_TOKENS"] = "1024"
+
+launch()
diff --git a/examples/ollama-different-model-for-completion.py b/examples/ollama-different-model-for-completion.py
new file mode 100644
index 0000000000000000000000000000000000000000..4efb9919b1052d14e53a06832a112d6f521a33bb
--- /dev/null
+++ b/examples/ollama-different-model-for-completion.py
@@ -0,0 +1,26 @@
+# /// script
+# requires-python = ">=3.11,<3.12"
+# dependencies = [
+# "synthetic-dataset-generator",
+# ]
+# ///
+# ollama serve
+# ollama run llama3.2
+# ollama run llama3.2:1b
+import os
+
+from synthetic_dataset_generator import launch
+
+os.environ["OLLAMA_BASE_URL"] = (
+ "http://127.0.0.1:11434/" # in this case, the same base url for both models
+)
+
+os.environ["MODEL"] = "llama3.2" # model for instruction generation
+os.environ["MODEL_COMPLETION"] = "llama3.2:1b" # model for completion generation
+
+os.environ["TOKENIZER_ID"] = "meta-llama/Llama-3.2-3B-Instruct" # tokenizer for instruction generation
+os.environ["TOKENIZER_ID_COMPLETION"] = "meta-llama/Llama-3.2-1B-Instruct" # tokenizer for completion generation
+
+os.environ["MAGPIE_PRE_QUERY_TEMPLATE"] = "llama3" # magpie template required for instruction generation
+
+launch()
diff --git a/examples/openai-deployment.py b/examples/openai-deployment.py
new file mode 100644
index 0000000000000000000000000000000000000000..a3a1bf8d2db1b3adc221b1f9d679dffe660d9f87
--- /dev/null
+++ b/examples/openai-deployment.py
@@ -0,0 +1,18 @@
+# /// script
+# requires-python = ">=3.11,<3.12"
+# dependencies = [
+# "synthetic-dataset-generator",
+# ]
+# ///
+
+import os
+
+from synthetic_dataset_generator import launch
+
+os.environ["HF_TOKEN"] = "hf_..." # push the data to huggingface
+os.environ["OPENAI_BASE_URL"] = "https://api.openai.com/v1/" # openai base url
+os.environ["API_KEY"] = os.getenv("OPENAI_API_KEY") # openai api key
+os.environ["MODEL"] = "gpt-4o" # model id
+os.environ["MAGPIE_PRE_QUERY_TEMPLATE"] = None # chat data not supported with OpenAI
+
+launch()
diff --git a/examples/vllm-deployment.py b/examples/vllm-deployment.py
new file mode 100644
index 0000000000000000000000000000000000000000..bdaee631706ec7b0555c8d0e0eff0c8fb297b64b
--- /dev/null
+++ b/examples/vllm-deployment.py
@@ -0,0 +1,21 @@
+# /// script
+# requires-python = ">=3.11,<3.12"
+# dependencies = [
+# "synthetic-dataset-generator",
+# ]
+# ///
+# vllm serve Qwen/Qwen2.5-1.5B-Instruct
+import os
+
+from synthetic_dataset_generator import launch
+
+os.environ["HF_TOKEN"] = "hf_..." # push the data to huggingface
+os.environ["VLLM_BASE_URL"] = "http://127.0.0.1:8000/" # vllm base url
+os.environ["MODEL"] = "Qwen/Qwen2.5-1.5B-Instruct" # model id
+os.environ["TOKENIZER_ID"] = "Qwen/Qwen2.5-1.5B-Instruct" # tokenizer id
+os.environ["MAGPIE_PRE_QUERY_TEMPLATE"] = "qwen2"
+os.environ["MAX_NUM_ROWS"] = "10000"
+os.environ["DEFAULT_BATCH_SIZE"] = "2"
+os.environ["MAX_NUM_TOKENS"] = "1024"
+
+launch()
diff --git a/packages.txt b/packages.txt
new file mode 100644
index 0000000000000000000000000000000000000000..15f6a51cbff6603c1d8c3d80135eb9c9f469afdb
--- /dev/null
+++ b/packages.txt
@@ -0,0 +1,2 @@
+poppler-utils
+tesseract-ocr
\ No newline at end of file
diff --git a/pdm.lock b/pdm.lock
new file mode 100644
index 0000000000000000000000000000000000000000..ad969f7fb96e6c248cd6614c54e27c4bd119defa
--- /dev/null
+++ b/pdm.lock
@@ -0,0 +1,3158 @@
+# This file is @generated by PDM.
+# It is not intended for manual editing.
+
+[metadata]
+groups = ["default"]
+strategy = ["inherit_metadata"]
+lock_version = "4.5.0"
+content_hash = "sha256:e95140895657d62ad438ff1815ddf1798abbb342ddd2649ae462620b8b3f5350"
+
+[[metadata.targets]]
+requires_python = ">=3.10,<3.13"
+
+[[package]]
+name = "aiofiles"
+version = "23.2.1"
+requires_python = ">=3.7"
+summary = "File support for asyncio."
+groups = ["default"]
+files = [
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+ {file = "aiofiles-23.2.1.tar.gz", hash = "sha256:84ec2218d8419404abcb9f0c02df3f34c6e0a68ed41072acfb1cef5cbc29051a"},
+]
+
+[[package]]
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+version = "2.4.4"
+requires_python = ">=3.8"
+summary = "Happy Eyeballs for asyncio"
+groups = ["default"]
+files = [
+ {file = "aiohappyeyeballs-2.4.4-py3-none-any.whl", hash = "sha256:a980909d50efcd44795c4afeca523296716d50cd756ddca6af8c65b996e27de8"},
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+
+[[package]]
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+version = "3.11.10"
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+summary = "Async http client/server framework (asyncio)"
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+ "aiohappyeyeballs>=2.3.0",
+ "aiosignal>=1.1.2",
+ "async-timeout<6.0,>=4.0; python_version < \"3.11\"",
+ "attrs>=17.3.0",
+ "frozenlist>=1.1.1",
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diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000000000000000000000000000000000000..b9625204b6a9382965bf1bdecfcc73c4be20a7d3
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,40 @@
+[project]
+name = "synthetic-dataset-generator"
+version = "0.2.0"
+description = "Build datasets using natural language"
+authors = [
+ {name = "davidberenstein1957", email = "david.m.berenstein@gmail.com"},
+]
+keywords = [
+ "gradio",
+ "synthetic-data",
+ "huggingface",
+ "argilla",
+ "generative-ai",
+ "ai",
+]
+requires-python = "<3.13,>=3.10"
+readme = "README.md"
+license = {text = "Apache 2"}
+
+dependencies = [
+ "argilla>=2.4.0,<3.0.0",
+ "distilabel[argilla,hf-inference-endpoints,hf-transformers,instructor,llama-cpp,ollama,openai,outlines,vllm,vision]>=1.5.0,<2.00",
+ "gradio[oauth]>=5.4.0,<6.0.0",
+ "gradio-huggingfacehub-search>=0.0.12,<1.0.0",
+ "huggingface-hub>=0.26.0,<0.28.0",
+ "model2vec>=0.2.4,<1.0.0",
+ "nltk>=3.9.1,<4.0.0",
+ "pydantic>=2.10.5,<3.0.0",
+ "sentence-transformers>=3.2.0,<4.0.0",
+ "transformers>=4.44.2,<5.0.0",
+ "unstructured[md,pdf,docx]>=0.16.3,<1.0.0",
+ "setuptools",
+]
+
+[build-system]
+requires = ["pdm-backend"]
+build-backend = "pdm.backend"
+
+[tool.pdm]
+distribution = true
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..05ddf6b0dff6718b255d03f567ac46e3e4edba36
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1 @@
+-e git+https://github.com/argilla-io/synthetic-data-generator.git#egg=synthetic-dataset-generator
\ No newline at end of file
diff --git a/src/synthetic_dataset_generator/__init__.py b/src/synthetic_dataset_generator/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..86d513b8efcd172826806ffa4b6b2ea9ef038036
--- /dev/null
+++ b/src/synthetic_dataset_generator/__init__.py
@@ -0,0 +1,20 @@
+import inspect
+from gradio import TabbedInterface
+
+from synthetic_dataset_generator import ( # noqa
+ _distiset,
+ _inference_endpoints,
+)
+
+def launch(*args, **kwargs):
+ """Launch the synthetic dataset generator.
+ Based on the `TabbedInterface` from Gradio.
+ Parameters: https://www.gradio.app/docs/gradio/tabbedinterface
+ """
+ from synthetic_dataset_generator.app import demo
+ return demo.launch(*args, server_name="0.0.0.0", **kwargs)
+
+
+launch.__doc__ = TabbedInterface.launch.__doc__
+launch.__signature__ = inspect.signature(TabbedInterface.launch)
+launch.__annotations__ = TabbedInterface.launch.__annotations__
diff --git a/src/synthetic_dataset_generator/__main__.py b/src/synthetic_dataset_generator/__main__.py
new file mode 100644
index 0000000000000000000000000000000000000000..8c9ea6bbfe136925cecc3d811ccfb03d524df690
--- /dev/null
+++ b/src/synthetic_dataset_generator/__main__.py
@@ -0,0 +1,4 @@
+if __name__ == "__main__":
+ from synthetic_dataset_generator import launch
+
+ launch()
diff --git a/src/synthetic_dataset_generator/_distiset.py b/src/synthetic_dataset_generator/_distiset.py
new file mode 100644
index 0000000000000000000000000000000000000000..7bf05222762180939af3eb8076264894f7357b7d
--- /dev/null
+++ b/src/synthetic_dataset_generator/_distiset.py
@@ -0,0 +1,148 @@
+from typing import Optional
+
+import distilabel
+import distilabel.distiset
+import gradio as gr
+from distilabel.utils.card.dataset_card import (
+ DistilabelDatasetCard,
+ size_categories_parser,
+)
+from huggingface_hub import DatasetCardData, HfApi
+
+
+class CustomDistisetWithAdditionalTag(distilabel.distiset.Distiset):
+ def _generate_card(
+ self,
+ repo_id: str,
+ token: str,
+ include_script: bool = False,
+ filename_py: Optional[str] = None,
+ ) -> None:
+ """Generates a dataset card and pushes it to the Hugging Face Hub, and
+ if the `pipeline.yaml` path is available in the `Distiset`, uploads that
+ to the same repository.
+
+ Args:
+ repo_id: The ID of the repository to push to, from the `push_to_hub` method.
+ token: The token to authenticate with the Hugging Face Hub, from the `push_to_hub` method.
+ include_script: Whether to upload the script to the hugging face repository.
+ filename_py: The name of the script. If `include_script` is True, the script will
+ be uploaded to the repository using this name, otherwise it won't be used.
+ """
+ card = self._get_card(
+ repo_id=repo_id,
+ token=token,
+ include_script=include_script,
+ filename_py=filename_py,
+ )
+
+ card.push_to_hub(
+ repo_id,
+ repo_type="dataset",
+ token=token,
+ )
+ if self.pipeline_path:
+ # If the pipeline.yaml is available, upload it to the Hugging Face Hub as well.
+ HfApi().upload_file(
+ path_or_fileobj=self.pipeline_path,
+ path_in_repo=distilabel.distiset.PIPELINE_CONFIG_FILENAME,
+ repo_id=repo_id,
+ repo_type="dataset",
+ token=token,
+ )
+
+ def _get_card(
+ self,
+ repo_id: str,
+ token: Optional[str] = None,
+ include_script: bool = False,
+ filename_py: Optional[str] = None,
+ ) -> DistilabelDatasetCard:
+ """Generates the dataset card for the `Distiset`.
+
+ Note:
+ If `repo_id` and `token` are provided, it will extract the metadata from the README.md file
+ on the hub.
+
+ Args:
+ repo_id: Name of the repository to push to, or the path for the distiset if saved to disk.
+ token: The token to authenticate with the Hugging Face Hub.
+ We assume that if it's provided, the dataset will be in the Hugging Face Hub,
+ so the README metadata will be extracted from there.
+ include_script: Whether to upload the script to the hugging face repository.
+ filename_py: The name of the script. If `include_script` is True, the script will
+ be uploaded to the repository using this name, otherwise it won't be used.
+
+ Returns:
+ The dataset card for the `Distiset`.
+ """
+ sample_records = {}
+ for name, dataset in self.items():
+ sample_records[name] = (
+ dataset[0] if not isinstance(dataset, dict) else dataset["train"][0]
+ )
+
+ columns = self["default"].column_names
+ columns = self["default"].column_names
+
+ if ("label" in columns and "text" in columns) or (
+ "labels" in columns and "text" in columns
+ ):
+ task_categories = ["text-classification"]
+ elif ("prompt" in columns and "completion" in columns) or (
+ "messages" in columns
+ ):
+ task_categories: list[str] = [
+ "text-generation",
+ "text2text-generation",
+ "question-answering",
+ ]
+ elif "context" in columns and "question" in columns and "response" in columns:
+ task_categories: list[str] = [
+ "text-generation",
+ "text2text-generation",
+ "text-retrieval",
+ "question-answering"
+ ]
+ if (
+ "positive_retrieval" in columns and "negative_retrieval" in columns
+ ) or ("positive_reranking" in columns and "negative_reranking" in columns):
+ task_categories.append("sentence-similarity")
+ else:
+ task_categories: list[str] = []
+ gr.Info(
+ f"No task categories found for dataset with columns: {columns}. "
+ "Please notify the distilabel team if you think this is an error."
+ )
+
+ readme_metadata = {}
+ if repo_id and token:
+ readme_metadata = self._extract_readme_metadata(repo_id, token)
+
+ metadata = {
+ **readme_metadata,
+ "size_categories": size_categories_parser(
+ max(len(dataset) for dataset in self.values())
+ ),
+ "task_categories": task_categories,
+ "tags": [
+ "synthetic",
+ "distilabel",
+ "rlaif",
+ "datacraft",
+ ],
+ }
+
+ card = DistilabelDatasetCard.from_template(
+ card_data=DatasetCardData(**metadata),
+ repo_id=repo_id,
+ sample_records=sample_records,
+ include_script=include_script,
+ filename_py=filename_py,
+ references=self.citations,
+ )
+
+ return card
+
+
+distilabel.distiset.Distiset = CustomDistisetWithAdditionalTag
diff --git a/src/synthetic_dataset_generator/_inference_endpoints.py b/src/synthetic_dataset_generator/_inference_endpoints.py
new file mode 100644
index 0000000000000000000000000000000000000000..830ecbc42e52af277d203a7b70391c750a9b2d1b
--- /dev/null
+++ b/src/synthetic_dataset_generator/_inference_endpoints.py
@@ -0,0 +1,58 @@
+import warnings
+
+import distilabel
+import distilabel.distiset
+from distilabel.models import InferenceEndpointsLLM
+from pydantic import (
+ ValidationError,
+ model_validator,
+)
+
+
+class CustomInferenceEndpointsLLM(InferenceEndpointsLLM):
+ @model_validator(mode="after") # type: ignore
+ def only_one_of_model_id_endpoint_name_or_base_url_provided(
+ self,
+ ) -> "InferenceEndpointsLLM":
+ """Validates that only one of `model_id` or `endpoint_name` is provided; and if `base_url` is also
+ provided, a warning will be shown informing the user that the provided `base_url` will be ignored in
+ favour of the dynamically calculated one.."""
+
+ if self.base_url and (self.model_id or self.endpoint_name):
+ warnings.warn( # type: ignore
+ f"Since the `base_url={self.base_url}` is available and either one of `model_id`"
+ " or `endpoint_name` is also provided, the `base_url` will either be ignored"
+ " or overwritten with the one generated from either of those args, for serverless"
+ " or dedicated inference endpoints, respectively."
+ )
+
+ if self.use_magpie_template and self.tokenizer_id is None:
+ raise ValueError(
+ "`use_magpie_template` cannot be `True` if `tokenizer_id` is `None`. Please,"
+ " set a `tokenizer_id` and try again."
+ )
+
+ if (
+ self.model_id
+ and self.tokenizer_id is None
+ and self.structured_output is not None
+ ):
+ self.tokenizer_id = self.model_id
+
+ if self.base_url and not (self.model_id or self.endpoint_name):
+ return self
+
+ if self.model_id and not self.endpoint_name:
+ return self
+
+ if self.endpoint_name and not self.model_id:
+ return self
+
+ raise ValidationError(
+ f"Only one of `model_id` or `endpoint_name` must be provided. If `base_url` is"
+ f" provided too, it will be overwritten instead. Found `model_id`={self.model_id},"
+ f" `endpoint_name`={self.endpoint_name}, and `base_url`={self.base_url}."
+ )
+
+
+distilabel.models.llms.InferenceEndpointsLLM = CustomInferenceEndpointsLLM
diff --git a/src/synthetic_dataset_generator/_tabbedinterface.py b/src/synthetic_dataset_generator/_tabbedinterface.py
new file mode 100644
index 0000000000000000000000000000000000000000..e334019794ea05e05fa922c88ea0ca81bbe3ada3
--- /dev/null
+++ b/src/synthetic_dataset_generator/_tabbedinterface.py
@@ -0,0 +1,69 @@
+"""
+This file defines two useful high-level abstractions to build Gradio apps: Interface and TabbedInterface.
+"""
+
+from __future__ import annotations
+
+from collections.abc import Sequence
+
+import gradio as gr
+from gradio.blocks import Blocks
+from gradio.layouts import Tab, Tabs
+from gradio.themes import ThemeClass as Theme
+from gradio_client.documentation import document
+
+
+@document()
+class TabbedInterface(Blocks):
+ """
+ A TabbedInterface is created by providing a list of Interfaces or Blocks, each of which gets
+ rendered in a separate tab. Only the components from the Interface/Blocks will be rendered in the tab.
+ Certain high-level attributes of the Blocks (e.g. custom `css`, `js`, and `head` attributes) will not be loaded.
+
+ Demos: tabbed_interface_lite
+ """
+
+ def __init__(
+ self,
+ interface_list: Sequence[Blocks],
+ tab_names: list[str] | None = None,
+ title: str | None = None,
+ theme: Theme | str | None = None,
+ analytics_enabled: bool | None = None,
+ css: str | None = None,
+ js: str | None = None,
+ head: str | None = None,
+ ):
+ """
+ Parameters:
+ interface_list: A list of Interfaces (or Blocks) to be rendered in the tabs.
+ tab_names: A list of tab names. If None, the tab names will be "Tab 1", "Tab 2", etc.
+ title: The tab title to display when this demo is opened in a browser window.
+ theme: A Theme object or a string representing a theme. If a string, will look for a built-in theme with that name (e.g. "soft" or "default"), or will attempt to load a theme from the Hugging Face Hub (e.g. "gradio/monochrome"). If None, will use the Default theme.
+ analytics_enabled: Whether to allow basic telemetry. If None, will use GRADIO_ANALYTICS_ENABLED environment variable or default to True.
+ css: Custom css as a string or path to a css file. This css will be included in the demo webpage.
+ js: Custom js as a string or path to a js file. The custom js should in the form of a single js function. This function will automatically be executed when the page loads. For more flexibility, use the head parameter to insert js inside
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