Key Features of Falcon LLM
- Falcon models are causal decoders based on the transformer‘s decoder architecture, trained on a diverse, high-quality dataset collected from web data.
- All Falcon models are released under the Apache 2.0 license, making them freely accessible for both research and commercial use. Falcon models demonstrate comparable performance to recent state-of-the-art models like GPT-4 and LLaMA 2 on tasks such as text generation, translation, question answering, and code generation. The Falcon-180B model achieves near-PaLM-2-Large performance at a reduced pretraining and inference cost, placing it among the top language models globally.
- Falcon models have limited multilingual capabilities as they are trained primarily on English and datasets related to European languages such as German, Spanish, and French.
- The Falcon team claims that their models require less memory compared to other models of similar sizes, making them more accessible.
- Falcon-180B, the largest model, has been trained on over 3.5 trillion tokens of text, representing the largest openly documented pretraining run.
Falcon LLM: Comprehensive Guide
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.
Table of Content
- What is Falcon LLM?
- Key Features of Falcon LLM
- Design Philosophy of Falcon LLM
- Key Model components of Falcon LLM
- Limitation
- Text Generation using Falcon 7B
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.
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