🥷 Let Agents Solve Your Tasks

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

Author

Lior Sinclair
October 13, 2023

AlphaSignal

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!

Lior

On Today’s Summary:

  • Repo Highlight: AutoGen

  • Trending Repos: LLaVa, fiftyone, OpenAI Python

  • Pytorch Tip: Asynchronous Data Loading

  • Trending Models: Replit, Zephyr, HotShot

  • Python Tip: Parquet

Reading time: 4min 16 sec

HIGHLIGHT
🥷 Microsoft AutoGen: LLM Agents at Your Service

What’s New?
AutoGen is a framework that enables the development of LLM applications using multiple agents which can converse with each other to solve tasks. Agents are customizable, conversable, and seamlessly allow human participation.

Why Does It Matter
Multiple agents can collaborate to solve complex tasks that are difficult for a single agent. For example, in a customer service application, one agent could handle initial queries while another specializes in troubleshooting. They can pass information between each other, making the process more efficient and effective.

Features

  • Simplified Multi-Agent Development: Enables building LLM applications with minimal effort, by simplifying the orchestration, automation, and optimization of a complex LLM workflow.

  • Flexible Conversation Design: Supports a wide range of conversation patterns concerning autonomy, number of agents, and conversation topology.

  • Advanced Inference API: Provides a drop-in replacement of openai.Completion and openai.ChatCompletion, allowing performance tuning and tuning, caching, and error handling utilities.

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⚙️ TRENDING REPOS

haotian-liu / LLaVA (☆ 8k)
LLaVA is a large multimodal model (LMM) that combines a vision encoder and Vicuna for general-purpose visual and language understanding. It represents an open-source alternative to GPT-4.

voxel51/fiftyone (☆ 5.2k)
The open-source tool for building high-quality datasets and computer vision models

openai / openai-python (☆ 12k)
The library provides easy access to the OpenAI API from applications written in the Python language. The latest major update introduces key enhancements such as improved error handling, optimizations for embeddings and integration of Weights and Biases functionalities.

dvlab-research / LongLoRA (☆ 1k)
LongLoRA is an efficient method to extend the context sizes of large language models (LLMs) without the typical high computational costs. It is best used in use-cases which require long context length in order to solve a domain-specific task.

OpenTalker / SadTalker (☆ 7k)
SadTalker：Learning Realistic 3D Motion Coefficients for Stylized Audio-Driven Single Image Talking Face Animation

• Automate up to 70% of your support queries from your site visitors

• Multilingual chatbots that provide 24/7 round-the-clock support in over 80 languages

• Integrates with over 5000 apps such as Slack, Microsoft Teams, and more

PYTORCH TIP
Asynchronous Data Loading

In deep learning, in situations when dealing with large datasets, loading data can become a bottleneck. To mitigate this, PyTorch provides the DataLoader class that can load data in parallel using torch.multiprocessing. This parallel loading is particularly beneficial when the data loading process is slower than the model training process.

When To Use

  • Large Datasets: When you're dealing with substantial amounts of data that can't be loaded into memory all at once.

  • Complex Data Preprocessing: When your preprocessing steps are computationally intensive and can be parallelized.

Benefits

  • Speed: Data is preloaded in the background, ensuring the GPU doesn't remain idle waiting for the data.

Efficiency: Maximizes the GPU utilization, leading to faster epochs and reduced overall training time.


import torch
from torch.utils.data import DataLoader

# Create an instance of the custom dataset
dataset = CustomDataset(...)
# e.g. you can use CIFAR10 dataset
# dataset = datasets.CIFAR10(...)

# Create a DataLoader with multiple workers
loader = DataLoader(
    dataset,
    batch_size=64,
    shuffle=True,
    # Number of subprocesses
    # to use for data loading
    num_workers=4,
    # Pins memory, useful if using CUDA
    pin_memory=True
)

# Now, when iterating over the loader,
# data is loaded in parallel
for batch in loader:
    # Training loop here

🗳️ TRENDING MODELS

replit-code-v1_5-3b
A code completion model by Replit, utilizing a 3.3B parameter Causal Language Model. It represents an open-source alternative to GitHub Copilot and it's designed for broad use, including commercial applications and fine-tuning.

zephyr-7b-alpha
A series of language models that are trained to act as helpful assistants. Zephyr-7B-α is the first model in the series, and is a fine-tuned version of Mistral-7B.

Hotshot-XL
A text-to-GIF model trained to work alongside Stable Diffusion XL (SDXL). The model allows users to generate GIFs of specific subjects only using images - which is especially important as it’s usually much easier to find suitable images for training compared to videos.

PYTHON TIP
Parquet

When working with large datasets in Python, the format in which you save your data can have a significant impact on performance. Parquet is a columnar storage file format that allows for fast reading and writing of data, making it especially suitable for machine learning workflows.

When To Use

  • Large Datasets: When dealing with datasets that are large in size, and you want to minimize I/O time.

  • Interoperability: When you need compatibility between Python and other languages or tools, e.g., R or big data tools.

  • Memory Efficiency: Store data in a way that retains data type and sparsity, reducing memory overhead.

Benefits

  • Speed: Optimized for performance, enabling rapid I/O operations.

  • Compression: Supports on-the-fly compression, which can significantly reduce file sizes.

  • Schema Retention: Retains the schema, allowing datasets to be self-describing.

Columnar Storage: Enables efficient reading of specific columns without loading the entire dataset.


import pandas as pd

# Sample dataframe
df = pd.DataFrame({
    'A': range(1, 4),
    'B': ['A', 'B', 'C'],
    'C': [1.1, 2.2, 3.3]
})

# Save to Parquet
df.to_parquet('dataframe.parquet')

# Load specific columns from Parquet
columns_to_load = ['A', 'C']
df_subset = pd.read_parquet(
    'dataframe.parquet',
    columns=columns_to_load
)

print(df_subset)

# Load entire Parquet file
df_full = pd.read_parquet(
    'dataframe.parquet'
)

print(df_full)

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