tlama-core 0.0.1

Core library for training Tlama models.

Tlama Core 🚀

Welcome to Tlama Core, the foundational repository for the Tlama models! This is the heart of our mission to create scalable, efficient, and cutting-edge AI models optimized for training and inference on GPUs. Whether you’re an AI researcher, developer, or GPU enthusiast, this repository is your playground to push the limits of performance, scalability, and innovation.

We believe in community-driven development—together, we can shape the future of AI. Join us to revolutionize machine learning with state-of-the-art optimizations for large-scale, high-performance models.

Why Tlama Core? 🤔

Tlama Core is the foundation for next-gen AI models, designed to enhance Tlama models with unmatched efficiency, power, and scalability. From high-performance computing to deep learning and robotics, we’re building the infrastructure for groundbreaking research and production-ready solutions.

Core Areas of Focus ⚙️

We’re targeting key optimizations to make Tlama models faster and more scalable. Explore our focus areas:

1. Custom CUDA Kernels 🔥: Unlock hardware potential with tailored kernels for attention, matrix ops, and more.
2. Mixed Precision Training 💎: Leverage Tensor Cores to train larger models faster.
3. Distributed & Multi-GPU Support 🌐: Scale effortlessly with optimized multi-GPU training.
4. Memory Optimizations 🧠: Use checkpointing and dynamic allocation for efficient large-scale training.
5. Profiling Tools 🕵️‍♂️: Analyze and optimize performance with precision.
6. Innovative Algorithms 💡: Push beyond PyTorch and cuBLAS with fresh approaches.
7. Compression Techniques 📦: Lightweight models via quantization and pruning.
8. Fine-tuning & Transfer Learning 🔄: Adapt models quickly to new tasks.
9. Reinforcement Learning Support 🎮: Tools for RL experimentation and deployment.
10. Research Support 🔬: Utilities for rapid experimentation and innovation.

Roadmap 📅

Join our journey to build the future of AI! Track our progress and contribute via our Roadmap Project Board and Issues.

Phases	Focus	Key Goals	Status
1	Bases	Repo structure, docs, Migrate Tlama-124M	In Progress
2	Foundations	CUDA kernels, mixed precision, docs	Pending
3	Scalability	Multi-GPU, profiler, Tlama-500M	Pending
4	Innovation/Robotics	Optimized attention, RL, simulation	Pending
5	Consolidation	Tlama-1B, API, hackathon	Pending

How to Set Up Tlama Core 💻

Get started easily on Windows, Linux, or macOS!

Prerequisites

• CUDA Toolkit (GPU support)
• Python 3.8+ (virtual env recommended)
• PyTorch (GPU version preferred)
• NVIDIA Driver (GPU-compatible)
• CMake (for CUDA kernels)

CUDA Toolkit Setup 🛠️

• Windows: Download from NVIDIA and install.
• Linux: Use sudo apt install nvidia-cuda-toolkit or download from NVIDIA.
• macOS: CPU-only (no CUDA support).

Installation Steps 🚀

1. Clone the Repository:

   git clone https://github.com/your-username/tlama-core.git
   cd tlama-core
   

2. Set Up Environment:

   python3 -m venv tlama-env
   source tlama-env/bin/activate  # Linux/macOS
   .\tlama-env\Scripts\activate  # Windows
   

3. Install Dependencies:

   pip install -r requirements.txt
   

4. Install CUDA Kernels:

   python setup.py install
   

5. Verify:

   python -c "import torch; print(torch.cuda.is_available())"  # Should output True
   

Run Tlama 124M Model 🏃‍♂️

Test it out:

from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("eigencore/tlama-124M", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("eigencore/tlama-124M", trust_remote_code=True)

prompt = "Once upon a time in a distant kingdom..."
input_ids = tokenizer.encode(prompt, return_tensors="pt")
output = model.generate(input_ids, max_length=50)
print(tokenizer.decode(output[0], skip_special_tokens=True))

More at Hugging Face.

How to Contribute 🌟

We’re all about collaboration! Here’s how to jump in:

1. Check out the Roadmap Project Board.
2. Pick an Issue to tackle.
3. Follow our Contribution Guidelines.
4. Join the convo on Discord.

New to contributing? Look for good-first-issue tags!

Our Vision 🌍

Our mission: scalable, efficient, high-performance AI models. We’re empowering researchers and developers to train bigger, deploy faster, and innovate freely. Learn more at Eigen Core.