What is Falcon-7B Instruct? Is it a better alternative to GPT-3?

Artificial Intelligence (AI) has witnessed remarkable advancements in recent years, particularly in the field of Large Language Models (LLMs). GPT-3, developed by OpenAI, has gained significant attention as one of the leading models in this domain. However, a new open-source contender has emerged - Falcon, a family of state-of-the-art language models created by the Technology Innovation Institute (TII) in Abu Dhabi.

In this article, we embark on a journey to explore the potential of Falcon-7B Instruct LLM, one of the impressive variants within the Falcon lineup, as a compelling alternative to GPT-3.

The Falcon Models:

The Falcon family consists of two base models:

1. Falcon-40B, and
2. Falcon-7B.

Falcon-40B, with approximately 90GB of GPU memory requirements, has achieved top rankings on the Open LLM Leaderboard. On the other hand, Falcon-7B, the smaller sibling, only requires around 15GB of GPU memory, making it accessible even on consumer hardware. This makes both models appealing options for various use cases.

Let's talk about the Falcon-7B Instruct Model!

Falcon-7B Instruct: To cater to specific requirements, TII has introduced instruct versions of the Falcon models, including Falcon-7B Instruct and Falcon-40B Instruct. These experimental variants have been fine-tuned on instructions and conversational data, making them particularly suitable for popular assistant-style tasks.

Training Data and Quality: The Falcon models have been trained on an impressive scale, with Falcon-7B processing 1.5 trillion tokens and Falcon-40B handling 1 trillion tokens. These models optimize for inference by leveraging high-quality training data. Notably, the majority of the training data (>80%) is derived from RefinedWeb, a massive web dataset based on CommonCrawl.

TII has focused on scaling and improving the quality of web data, implementing rigorous filtering and large-scale deduplication techniques. While some curated sources, such as conversational data from Reddit, are included in the training, their proportion is significantly lower than that of previous state-of-the-art LLMs like GPT-3 or PaLM. TII has even released a 600 billion token extract of RefinedWeb for the community to utilize in their own language models.

Multiquery Attention: The Falcon models incorporate an interesting feature called multi-query attention. Unlike the traditional multi-head attention scheme, which has separate query, key, and value embeddings per head, multi-query attention shares a single key and value across all attention heads.

This innovation enhances the scalability of inference, significantly reducing memory costs and enabling optimizations like statefulness. The smaller K, V-cache during autoregressive decoding contributes to these benefits.

Falcon models also have multilingual capabilities. It understands English, German, Spanish, and French and has limited capabilities in some European languages such as Dutch, Italian, Romanian, Portuguese, Czech, Polish, and Swedish.

Now, let's see how these models have performed against other top language models. The 40B parameter model currently tops the charts of the Open LLM Leaderboard, while the 7B model is the best in its weight class.

Falcon-7B Instruct vs. GPT-3:

Now let's compare Falcon-7B Instruct with GPT-3, the widely acclaimed LLM developed by OpenAI. While GPT-3 has achieved remarkable performance and versatility, Falcon-7B Instruct offers several advantages.

1. First, Falcon-7B Instruct requires less GPU memory, making it more accessible on consumer hardware. Despite its power, Falcon uses only 75 percent of GPT-3’s training compute.
2. Also, it requires one-fifth of the compute at inference time. This means practitioners and enthusiasts can leverage the power of Falcon-7B Instruct without significant infrastructure investments.
3. Third, Falcon models, including Falcon-7B Instruct, have been fine-tuned on instructions and conversational data, which enhances their performance on assistant-style tasks.
4. While GPT-3's large parameter count and extensive training on internet data give it a wide range of capabilities, Falcon-7B Instruct's focus on instructions and conversational data makes it a more specialized and targeted solution for certain tasks. It excels in scenarios where following instructions and engaging in interactive conversations are crucial.
5. Finally, TII's release of the RefinedWeb extract and the availability of instruct versions provide opportunities for customization and further experimentation.

Conclusion:

In conclusion, Falcon-7B Instruct from the Falcon family of language models emerges as a promising alternative to GPT-3. Developed by the esteemed Technology Innovation Institute, Falcon models offer remarkable performance and accessibility, making them highly appealing for a diverse range of language processing tasks.

Notably, Falcon-7B Instruct shines in tasks that involve following instructions and participating in interactive conversations. Its proficiency in these areas makes it an ideal choice for professionals and enthusiasts seeking a language model for copywriting, summarization, code-writing, classification, sentiment analysis, and more.

Excitingly, Falcon-7B Instruct is now available on Monster API, offering access at a price as low as roughly $0.01 for 1000 tokens! This optimized version of Falcon-7B Instruct is specifically designed to cater to large-scale requests while ensuring that users can enjoy the benefits of powerful text generation at an incredibly affordable rate.

With such a low cost, Falcon-7B Instruct on Monster API opens up a world of possibilities for developers and businesses seeking budget-friendly access to state-of-the-art AI capabilities.

To learn more about the model and its capabilities, refer to the documentation provided on: MonsterAPI Postman Documentation. There, you'll find examples of API requests that you can quickly integrate into your applications to power them up with Falcon-7B Instruct's capabilities!