- Quick Overview
TL;DR
- • Llama 3.1 8B fine‑tuned to speak like Rick Sanchez
- • Unsloth + LoRA for efficient training; BF16/FP16; AdamW 8‑bit
- • Lambda Labs A100; automated lifecycle via Python scripts
- • Ollama deployment with GGUF Q8_0 and custom Modelfile
Core Technologies
- • Base Model: Meta Llama 3.1 8B (4‑bit)
- • Fine‑tuning: Unsloth + LoRA
- • Cloud: Lambda Labs A100 SXM4
- • Deployment: Ollama (local serving)
Project Overview
Rick LLM demonstrates a complete pipeline to fine‑tune Meta Llama 3.1 8B into a character‑style model. It covers dataset creation and cleaning, parameter‑efficient fine‑tuning, cloud GPU orchestration, and local deployment through Ollama.
Project Structure
rick-llm/ ├── src/ │ ├── dataset.py # Dataset creation and preprocessing │ ├── rick_llm/ # Core fine-tuning modules │ │ ├── finetune.py # Main training orchestration │ │ ├── trainer.py # Training configuration and setup │ │ ├── model_utils.py # Model initialization utilities │ │ └── constants.py # Training hyperparameters │ └── lambda/ # Cloud infrastructure management │ ├── commands.py # Lambda Labs API integration │ └── request.json # Instance configuration ├── notebooks/ # Jupyter notebooks for experimentation ├── ollama_files/ # Deployment artifacts │ ├── Modelfile # Ollama model configuration │ └── unsloth.Q8_0.gguf # Quantized model weights └── Makefile # Automation scripts
Implementation Details
1. Dataset Creation Pipeline
- Transcripts from Hugging Face; converted to ShareGPT format
- GPT‑4o‑mini removes stage directions and keeps dialogue
- Instruction pairs with system prompts defining Rick's persona
- Cleaned dataset published to Hugging Face Hub
2. Model Fine‑tuning Process
- Base: unsloth/Meta-Llama-3.1-8B-bnb-4bit; seq len 2048
- LoRA: r=32, alpha=64, dropout=0; target all attention/MLP
- Training: lr=2e‑4 linear; bs=4; grad‑acc=4; epochs=5
- Optimizer: AdamW 8‑bit; mixed precision BF16/FP16
3. Cloud Infrastructure Management
- Lambda Labs gpu_1x_a100_sxm4 (A100 80GB) instances
- Python scripts automate instance lifecycle and SSH keys
- rsync for fast code/data synchronization
4. Model Deployment
- Export to GGUF; Q8_0 quantization for perf/size balance
- Custom Modelfile with Rick's system prompt
- Ollama local serving for an interactive chat interface
Key Features
Educational Value
- End‑to‑end pipeline; modern frameworks and optimization
- Cloud GPU usage and cost management
- Comprehensive README and docs
Technical Innovations
- Unsloth optimizations reduce time and memory
- LoRA keeps additional parameters minimal
- AI‑assisted cleaning ensures data quality
- Production‑ready quantization and deployment
Automation
- Makefile commands streamline workflow
- Programmatic cloud resource management
- Robust error handling and logging
Use Cases and Applications
Educational
- LLM fine‑tuning example, cloud compute, data engineering
Entertainment
- Rick‑style chatbot; creative content; fan engagement
Technical Demonstration
- Optimization techniques, deployment patterns, cost strategies
Project Impact
This project demonstrates advanced understanding of modern AI agent frameworks, production AI observability and monitoring, LLM evaluation and performance optimization, microservices architecture, async Python development, API design and integration, container orchestration, and real-world AI application deployment. The codebase reflects production-ready practices including proper error handling, logging, configuration management, and scalable architecture design.