Text Generation using Falcon 7B

Let us see how we can use Falcon 7B for text generation.

1. Install necessary libraries

We are installing the accelerate package as it provides a collection of utilities and wrappers for accelerating Python applications, particularly those related to scientific computing, numerical simulations, and machine learning. The package leverages hardware acceleration features such as SIMD (Single Instruction, Multiple Data), multi-threading, and GPU acceleration to improve the performance of numerical computations.

!pip install accelerate

2. Import the libraries

For text generation, we will require pandas for data manipulation and analysis, pytorch and transformers module for automatic model configuration, auto-loading of pre-trained models and tokenization.

3. Initiate the Model

This code initializes a text generation pipeline using the “tiiuae/falcon-7b-instruct” model from the Hugging Face transformers library. It loads the tokenizer for the model and creates the pipeline with parameters such as using bfloat16 data type, trusting remote code, and automatically mapping computation to available devices. This pipeline is then ready for text generation tasks based on input prompts.

3. Use the model for text generation

This code snippet generates text sequences using the previously initialized text generation pipeline (generator). It prompts the model with the input text “What is the purpose of life” and specifies parameters such as maximum sequence length (max_length), whether to sample from the model (do_sample=True), the number of top-k candidates to sample from (top_k=10), the number of sequences to generate (num_return_sequences=1), and the end-of-sequence token ID from the tokenizer. The generated text sequences are stored in the text_sequences variable, and then each generated sequence is printed using a loop, displaying the generated text with the label “Result”. This allows for the quick generation and display of text based on the input prompt.

Output

The purpose of life is a highly debated topic, with different individuals or cultures providing unique perspectives on its meaning. From a philosophical standpoint, it is often perceived as a subjective question as it depends on an individual's understanding and beliefs. However, some argue that the purpose of life is to find meaning and fulfillment, cultivate relationships, make a positive impact on the world, or simply to live a content and happy life. Ultimately, it is up to each individual to provide their own answer and purpose.

Falcon LLM is a large language model that is engineered to comprehend and generate human like text, showcasing remarkable improvements in natural language and generation capabilities. This article covers the fundamentals of Falcon LLM and demonstrates how can we perform text generation using Falcon LLM.

Falcon LLM aims to set new benchmarks in AI’s ability to interact, reason, and assist in a variety of complex tasks, promising transformative impacts across industries and research domains.

Large Language Model (LLM) is a very huge model (in terms of parameter) that are generally based on the transformer architecture (a special type of neural network capable of parallel processing through self-attention mechanism) that are trained on massive amounts of text data which help them to understand and generate text like humans do. Some examples of the famous LLM are GPT-3, Google BART, PaLM. Though the LLM models like GPT-3, Google BART, and PaLM are available to the public for inference, how they have been trained is not documented in detail. Traditionally the open-source LLM model has always lagged behind these private/commercial LLM models in terms of performance and size. The lack of detailed documentation about the training process of successful large-scale models limits the research and progress of open-source models.

Let us get an understanding of the key components of the Falcon Model.