🚀 LLMForge

LLMForge is a modular LLMOps pipeline originally designed for Reddit-based dataset curation and LoRA fine-tuning on TinyLLaMA-1.1B. It automates ingestion, cleaning, training, and Hugging Face syncing — all backed by Prefect, Modal, and CI/CD best practices.

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✅ What’s New (June 2025)

We’ve added a one-line fine-tuning CLI flow using Modal GPU compute and any Hugging Face dataset.

./llmforge finetune \
  --dataset-repo yourusername/your-dataset \
  --model-repo yourusername/your-model-name \
  --hf-auth ./hf_token.txt \
  --push-to-hub

No setup beyond a HF token file and basic venv install. Full walkthrough and screenshots below 👇

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🧠 Why This Update Matters

This update streamlines LLMForge into a modular LoRA trainer for the OSS community:

• 🔥 No MLOps knowledge needed to fine-tune & push your own model
• 🧼 Old full-pipeline logic is retained but commented for now
• 🔁 Fast launch, easy to build on top of

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⚙️ Key Features

• 🔎 Reddit-based prompt-completion dataset creation
• 🧹 Toxicity filtering using Detoxify
• ⚙️ LoRA fine-tuning on Modal with GPU (T4)
• ☁️ Hugging Face Dataset + Model Hub syncing
• 🔁 Prefect orchestration pipeline (legacy)
• 🚀 New! One-command fine-tuning using unsloth via CLI + Modal
• 🧪 CI setup with pytest and GitHub Actions
• 📧 Email alerts for pipeline (optional)

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🆕 New: Simple Finetune CLI

After cloning the repo:

python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt

Place your Hugging Face token in a file named hf_token.txt.

Then run:

./llmforge finetune \
  --dataset-repo yourusername/your-dataset(whatever dataset repo name you want to give) \
  --model-repo yourusername/your-model-name(same naming conventions like dataset) \
  --hf-auth ./hf_token.txt \
  --push-to-hub

That's it. Modal will handle the GPU job and push the model to your HF.

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🧼 Notes on Code Cleanup

• tinylama and old full-pipeline fine-tuning logic are commented (not deleted).
• The 12-hour looped pipeline is inactive, but logic is retained for future revival.
• modal secret creation is handled dynamically using your HF token.

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🔧 Architecture Overview

Module	Role
src/	Pipeline + finetuning logic
tasks/	Prefect-wrapped tasks for orchestration
tests/	Test coverage
llmforge	CLI to trigger training

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🛠️ Features Implemented

Feature	Status
Reddit Ingestion with PRAW	✅
Toxicity Filtering (Detoxify)	✅
Prompt-Completion Generation	✅
Hugging Face Dataset Push	✅
LoRA Fine-tuning on Modal	✅
Hugging Face Model Push	✅
CLI-based Finetune Trigger	✅
CI + pytest test integration	✅
Email Alerts	✅
Streamlit Inference UI (local)	✅

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📁 Project Structure

.
├── hf_uploader.py
├── inference.py
├── LICENSE
├── llmforge                   # CLI wrapper for launching fine-tune jobs
├── prefect.yaml
├── __pycache__
├── README.md
├── requirements.txt
├── run_pipeline.py
├── src/
│   ├── data_ingestion.py
│   ├── data_processor.py
│   ├── email.py
│   ├── finetune.py
│   ├── __init__.py
│   └── __pycache__
├── tasks/
│   ├── data_ingestion_task.py
│   ├── data_processor_task.py
│   ├── finetune_task.py
│   └── __pycache__
├── tests/
│   ├── __init__.py
│   └── test_pipeline.py
├── venv
└──

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📷 Screenshots

🔄 One-command Modal GPU Job

✅ Model Pushed to Hugging Face

🧪 CI + Prefect Pipelines

🎛️ Streamlit Inference UI

Model and Dataset Pushed to Hugging Face after CLI run

Terminal outputs after I ran CLI command

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🧪 Testing

pytest tests/

✅ All tests pass for ETL logic and CLI triggers

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⚠️ Dev Notes

LLMForge was built on a 4GB GPU laptop:

• Modal handles all remote fine-tuning
• The 12-hr pipeline is currently disabled for cost reasons
• Adapter merging was skipped for memory savings

Despite that:

• It’s cloud-ready and reproducible
• Works on real Reddit + HF datasets
• Fully OSS and tweakable

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🧠 Future Extensions

• Merge adapters for complete model export
• Auto-deploy Streamlit via Modal or Render
• Reactivate scheduled flows (Prefect)
• Add Rouge/BLEU scoring post-finetune
• Support data balancing + multi-source ingestion

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🧑‍💻 Author

Hindol R. Choudhury MLOps • LLM Infra • Applied AI 📫 LinkedIn

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