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🎹 Is This The Open-Source GitHub Copilot?

Your weekly technical digest of top projects, repos, tips and tricks to stay ahead of the curve.


Hey ,

Welcome to this week's edition of AlphaSignal the newsletter for AI professionals.

Whether you are a researcher, engineer, developer, or data scientist, our summaries ensure you're always up-to-date with the latest breakthroughs in AI.

Let's get into it!


On Today’s Summary:

  • Repo Highlight: Tabby

  • Trending Repos ProPainter, Ray, NExT-GPT

  • Pytorch Tip: Model Pruning

  • Trending Models: phi-1_5, wuerstchen, QRcode Monster

  • Python Tip: Dynamic Importing

Reading time: 3min 40 sec

🎹 Tabby: The Open-Source GitHub Copilot (10.5k)

What’s New?
Tabby is an open-source, self-hosted AI coding assistant, positioning itself as a practical alternative to GitHub Copilot. It allows teams to effortlessly leverage any Coding LLM, ensuring flexibility and compatibility in code completion tasks.

Core Features

  • Compatibility: Integrates seamlessly with major Coding LLMs like CodeLlama, StarCoder, and CodeGen, accommodating both smaller models (under 400M parameters) for CPU and larger ones (1B parameters and above) for GPU inference.

  • Efficiency: Powered by Rust, Tabby is able to deliver fast coding experiences and achieves code completion in under a second with adaptive caching.

  • Optimized Stack: Optimizes the entire stack, from IDE extensions to model serving. This enables fast code completions and effective prompts in real-world applications.

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ray-project / ray (☆ 27.8k)
Ray is a unified framework designed to scale AI and Python applications. It includes a core distributed runtime and specialized AI libraries for tasks like data handling, training, and hyperparameter tuning. Supports seamless scaling from a laptop to a cluster.

NExT-GPT / NExT-GPT (☆ 1.6k)
NExT-GPT offers an end-to-end multimodal model capable of processing and generating text, images, video, and audio. It supports any-to-any input-output combinations, built on top of existing LLM and diffusion models. Code released for customization.

aiwaves-cn/agents (☆ 3.2k)
"Agents" is an open-source framework for creating autonomous language agents. It supports features like long-term memory, tool usage, web navigation, and fine-grained control via SOPs. Highly customizable.

InternLM/InternLM (☆ 3.2k)
InternLM is an open-source, efficient framework for training large-scale language models. It supports training on thousands of GPUs with high efficiency and offers pre-trained models. Great for both research and production.

sczhou / ProPainter (☆ 1.6k)
ProPainter is a video inpainting framework. It offers memory-efficient inference options and supports object removal and video completion tasks. Pretrained models and training scripts are available for easy customization.

Model Pruning

Model Pruning is a technique for eliminating unnecessary weight parameters to reduce model size while maintaining accuracy. It's important for deploying in resource-limited environments like mobile devices.

When to Use
- Deployment: Good for environments with limited computational resources.
- Efficiency: Useful for achieving similar performance with a smaller model.

- Size: Significantly reduces the model's size.
- Speed: Fewer parameters lead to faster inference.
- Options: PyTorch offers different pruning methods.

The example uses l1_unstructured pruning to remove parameters with the smallest absolute values for efficient optimization.

import torch
import torch.nn as nn
import torch.nn.utils.prune as prune

# Create model
model = nn.Sequential(
nn.Linear(10, 10),
nn.Linear(10, 5))

# Pick layer to prune
layer = model[0]

# Prune 30% of connections
prune.l1_unstructured(layer, "weight", 0.3)

# Finalize pruning
prune.remove(layer, 'weight')


Phi-1.5 is a 1.3-billion-parameter Transformer language model trained on a primarily synthetic dataset. The model achieves near SOTA performance in tasks like common sense reasoning and language understanding, particularly among models with less than 10B parameters.

Würstchen is a diffusion model that operates in a highly compressed image latent space, achieving unprecedented 42x spatial compression. This is significant because it dramatically reduces the computational costs for both training and inference, enabling faster and cheaper model deployment.

Controlnet QR Code Monster v2 is designed to generate QR codes that are both creative and scannable. The model's performance can be fine-tuned using various parameters and prompts, allowing users to balance between creativity and readability.

Dynamic Importing

The importlib library in Python allows you to dynamically import modules and access their attributes, which can be useful in scenarios where the modules to be used are determined at runtime.

When to Use
- Plugin Architecture: Load plugins based on user configuration.
- Dynamic Model Selection: Pick different ML algorithms conditionally.
- Configuration-Driven: Modify app behavior via external settings.

- Flexibility: Swap modules dynamically as conditions change.
- Modularity: Facilitates easier maintenance and unit testing.
- Extensibility: Integrate new features without modifying existing code.

import importlib

# Dynamically import the 'math' module
math_module = importlib.import_module('math')

# Use the 'sqrt' function from the imported 'math' module
result = math_module.sqrt(16)
print(result) # Output will be 4.0

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