I used the BEST Open Source LLM to build a GPT WebApp (Falcon-40B Instruct)

Key Concepts

Falcon 40B Instruct, open-source LLM, Hugging Face LLM Leaderboard, Apache 2.0 license, PyTorch, CUDA, Langchain, Hugging Face Pipeline, Transformers, AutoTokenizer, AutoModelForCausalLM, GPU, RunPod, Radio, Streamlit, Q&A, Few-shot sentiment analysis, Chain of Thought prompting, Dolly, Falcon 7B, NordPass Business.

Falcon 40B Instruct: A Deep Dive and Comparison

Introduction

The video focuses on Falcon 40B Instruct, an open-source large language model (LLM), and evaluates its performance against smaller models like Dolly (3B parameters) and Falcon 7B. The video aims to demonstrate how to use Falcon 40B and assess its capabilities in various tasks.

Installation and Setup

1. Dependencies: The video outlines the necessary dependencies for running Falcon 40B, including:
   • PyTorch (installed with CUDA 11.7 for GPU acceleration)
   • Langchain
   • Accelerate
   • Transformers
   • Bits and Bytes
2. Importing Libraries: Specific classes are imported from Langchain (HuggingFacePipeline, PromptTemplate, LLMChain) and Transformers (AutoTokenizer, AutoModelForCausalLM).
3. GPU Requirements: Running Falcon 40B effectively requires substantial GPU resources. The presenter used two A100 80GB GPUs on RunPod, costing approximately $3.38 per hour, to avoid out-of-memory errors.
4. Loading the Model:
   • The model is loaded using AutoModelForCausalLM.from_pretrained from the Transformers library.
   • Key parameters include model_id (tiiuae/falcon-40b-instruct), device_map (specifying GPU usage), torch_dtype, and offload_folder (for managing memory).
   • The model is set to inference mode using model.eval().
5. Transformers Pipeline: The loaded model and tokenizer are passed to the Transformers pipeline, configuring parameters like top_p, top_k, num_return_sequences, and max_length.

Langchain Integration

1. Prompt Template: A basic prompt template is created with an input variable.
2. Hugging Face Pipeline in Langchain: The Transformers pipeline is integrated into Langchain using the HuggingFacePipeline class.
3. LLM Chain: An LLM chain is constructed by combining the Hugging Face Pipeline LLM and the prompt template.

Building a User Interface with Radio

1. Radio Library: The video uses Radio to create a user interface within a Jupyter Notebook.
2. Generate Function: A generate function is defined to take a prompt as input, pass it to the LLM chain, and return the response.
3. Radio Interface: A Radio interface is built using gr.Interface, specifying the function (fn), input type (text), output type (text), title, description, and style (e.g., "Boxy Violet").
4. Launching the App: The app is launched using the launch method, with share=True to generate a shareable URL.

Model Evaluation: Q&A, Sentiment Analysis, and Chain of Thought

The video evaluates Falcon 40B against Dolly (3B) and Falcon 7B across three tasks:

1. Q&A (Mr. Beast)

• Dolly: Provided a lackluster response, misidentifying Mr. Beast as a criminal.
• Falcon 7B: Went "off the deep end," attributing incorrect information to Mr. Beast.
• Falcon 40B: Gave a comprehensive and accurate description of Mr. Beast's rise to fame, mentioning his YouTube channel and philanthropic activities. It also demonstrated some multilingual capabilities by responding to a French translation of the question.

2. Sentiment Analysis

• Dolly: Correctly identified the sentiment of a tricky sentence as positive.
• Falcon 7B: Correctly identified the sentiment as positive.
• Falcon 40B: Returned "mixed" sentiment, which was deemed partially accurate but not ideal.

3. Chain of Thought Prompting (Potato Problem)

• Dolly: Initiated the correct Chain of Thought but failed in the math, returning 6.86 instead of 6.
• Falcon 7B: Correctly formulated the equation (7 - 1 = 6) but added an extra step, resulting in an incorrect answer of 2.
• Falcon 40B: Successfully solved the problem, demonstrating accurate Chain of Thought reasoning and correct math (7 - 1 = 6).

NordPass Business Sponsorship

The video includes a sponsorship segment for NordPass Business, highlighting its features for managing and securing business passwords, including generating strong passwords and securely sharing team passwords. A three-month free trial is offered via a specific URL.

Conclusion

Falcon 40B Instruct demonstrates superior performance compared to smaller models like Dolly and Falcon 7B, particularly in Q&A and Chain of Thought reasoning. While sentiment analysis was less accurate, the model's overall capabilities, especially considering its open-source nature, are impressive. The video successfully demonstrates how to set up and use Falcon 40B, including integrating it with Langchain and building a user interface with Radio.