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💬 The Most Powerful RAG Chatbot

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

Author

Lior Sinclair
November 29, 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

In Today’s Summary:

  • Repo Highlight: Verba

  • Trending Repos: adapters, Video-LLaVA

  • Pytorch Tip: Dask

  • Trending Models: GPT-Baker, tulu-2-dpo-70b

  • Python Tip: functools.partial

Reading time: 4 min 26 sec

HIGHLIGHT
Verba: The Easiest Way to Chat With Your Data

What’s New
Verba, dubbed the "Golden RAGtriever," leverages Weaviate's Generative Search technology to query personal documents. Its interface simplifies Retrieval-Augmented Generation (RAG) use.

The application integrates Large Language Models (LLMs) from OpenAI, Cohere, and HuggingFace. It accommodates various file types, aiding users in exploring and extracting insights from their data.

How it Works
Verba's core functionality lies in its hybrid search mechanism that combines vector and lexical search methods, augmented by a Semantic Cache for quicker query processing. This combination delivers responses that are both precise and contextually enriched.

Features

  • Hybrid Search: Integrated vector and lexical search for precision matching and contextual understanding.

  • Semantic Cache: Speeds up queries by remembering past interactions and proactively suggesting auto-completions.

  • User Interface: Simplifies data import and interaction, enhancing user experience.

  • File Type Compatibility: Automatically chunks and vectorizes a variety of formats like .txt, .md, .pdf, etc., so you can chat with all your data.

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TRENDING REPOS

adapters (☆ 2.1k)
Adapters extends Hugging Face’s transformers library, simplifying the integration, training and usage of adapters and other efficient fine-tuning methods for Transformer-based language models.

Video-LLaVA (☆ 1.3k)
Video-LLaVA unifies visual representations for images and videos into a single feature space, enabling a Large Vision-Language Model (LVLM) to perform visual reasoning on both modalities simultaneously.

Understanding Deep Learning (☆ 3.5k)
Professor Simon Prince has released IPython notebooks for his book "Understanding Deep Learning." These resources, covering 21 chapters, include fundamentals and advanced topics like deep reinforcement learning and ethics, benefiting those in the deep learning field.

intel-extension-for-transformers (☆ 1.2k)
The Intel Extension for Transformers is a toolkit designed to accelerate Transformer-based models on Intel platforms, notably the 4th Intel Xeon Scalable processor. It features model compression, advanced optimizations, and a simple API for building chatbots.

LLaMA-Factory (☆ 7.1k)
The LLaMA Factory offers a framework for fast and efficient fine-tuning of Large Language Models like LLaMA, BLOOM, and others, simplifying the training and evaluation process. LLaMA Board is a one-stop web UI that facilitates getting started with model training and tuning.

Speechmatics has launched Real-Time Translation as part of its all-in-one Speech API.

Their new self-supervised model can bring your product or service to the largest audience possible, without the hassle of multiple different language APIs and lengthy setup times.

PYTORCH TIP
Dask

PyTorch and Dask can be combined for effective handling of large-scale data processing and model training. Dask is a flexible parallel computing library for analytics that scales from a single CPU to thousands of nodes. Dask allows PyTorch to handle much larger datasets that can be loaded and processed in parallel, accelerating data preparation.

When To Use

  • Handling Large Datasets: When datasets are too large to fit into memory and require parallel loading and processing.

  • Distributed Computing: To leverage multiple CPUs or machines for parallel data processing.

Benefits

  • Scalability: Dask allows for scaling data processing workloads across multiple machines in high-performance computing clusters or in the cloud.

  • Efficiency: Faster data processing, especially for large datasets, before feeding into PyTorch models.

  • Simplicity: Dask seamlessly integrates with PyTorch’s DataLoader for efficient batch processing.


import dask.array as da
import torch
from torch.utils.data import (
    TensorDataset, 
    DataLoader
)

# Example of using Dask Array
dask_array = da.random.random(
        (10000, 10), chunks=(1000, 10)
)

# Convert Dask array to NumPy
# and then to a PyTorch Tensor
tensor = torch.tensor(
    dask_array.compute()
)

# Create a TensorDataset and DataLoader
dataset = TensorDataset(tensor)
loader = DataLoader(
    dataset, batch_size=32
)

# Use the DataLoader in your training loop
for batch in loader:
    # Training code here
    pass

TRENDING MODELS/SPACES

UltraFastBERT-1x11-long
UltraFastBERT-1x11-long is a pre-trained BERT variant utilizing fast feedforward networks (FFF) for efficient neuron usage during inference. It achieves 78x speedup for CPU baseline feedforward implementation, and 40x speedup over a batched PyTorch implementation.

tulu-2-dpo-70b
Tulu V2 DPO 70B, part of the Tulu series of language models, is a fine-tuned version of Llama 2, designed to function as an assistant. To date, it is the largest model trained using Direct Preference Optimization (DPO).

GPT-Baker
GPT Baker enables the creation and testing of custom open-source GPTs with an easy-to-use interface.

PYTHON TIP
functools.partial

functools.partial is a utility in Python's functools module that allows you to create a new function with some predefined arguments of an existing function. It's useful when you have a function that you commonly call with the same parameters and want to avoid repetitive code.

When To Use

  • Repetitive Function Calls: When you need to call a function multiple times with the same or similar arguments.

    Callback Functions: When using functions as callbacks in APIs, GUIs, or event handlers that require a specific signature.

Benefits

  • Code Conciseness: Reduces boilerplate by fixing some arguments of functions, leading to shorter and cleaner code.

    Flexibility: Allows customization of function calls without altering the original function's definition.


from functools import partial

def greet(name, message):
    return f"{message}, {name}!"

# Creating a partial function with a fixed message
hello_greet = partial(greet, message="Hello")

# Using the partial function
print(hello_greet(name="Alice"))  
# Outputs: "Hello, Alice!"


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Igor Tica is a contributing writer at AlphaSignal and a research engineer at SmartCat, focusing on computer vision. He's actively seeking partnerships in self-supervised and contrastive learning.

Jacob Marks is an editor at AlphaSignal and ML engineer at Voxel51, is recognized as a leading AI voice on Medium and LinkedIn. Formerly at Google X and Samsung, he holds a Ph.D. in Theoretical Physics from Stanford.

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