Crushing LLaMA, 'Falcon' is completely open source! 40 billion parameters, trillions of token training, dominating Hugging Face

In the era of large models, what is most important?

The answer LeCun once gave is: open source.

When the code for Meta’s LLaMA was leaked on GitHub, developers around the world had access to it. An LLM that reaches GPT level.

Next, various LLMs give various angles to the open source of AI models.

LLaMA paved the way and set the stage for models such as Stanford's Alpac and Vicuna, making them leaders in open source.

At this moment, the Falcon "Falcon" broke out of the siege again.

Falcon Falcon

"Falcon" was developed by the Technology Innovation Institute (TII) in Abu Dhabi, United Arab Emirates. In terms of performance, Falcon performs better than LLaMA good.

Currently, "Falcon" has three versions-1B, 7B and 40B.

TII stated that Falcon is the most powerful open source language model to date. Its largest version, Falcon 40B, has 40 billion parameters, which is still a bit smaller in scale than LLaMA, which has 65 billion parameters.

Although the scale is small, the performance is high.

Faisal Al Bannai, Secretary General of the Advanced Technology Research Council (ATRC), believes that the release of “Falcon” will break the way to obtain LLM and allow researchers and entrepreneurs to propose the best solutions. Most innovative use cases.

The two versions of FalconLM, Falcon 40B Instruct and Falcon 40B, rank in the top two on the Hugging Face OpenLLM rankings, while Meta's LLaMA is ranked third.

It is worth mentioning that Hugging Face passed four current benchmarks for comparing manifolds-AI2 Reasoning Challenge , HellaSwag, MMLU and TruthfulQA are used to evaluate these models.

Although the "Falcon" paper has not yet been publicly released, Falcon 40B has been extensively trained on a carefully screened 1 trillion token network data set.

Researchers revealed that “Falcon” attaches great importance to the importance of achieving high performance on large-scale data during the training process.

What we all know is that LLM is very sensitive to the quality of training data, which is why researchers spend a lot of effort building one that can perform efficient processing on tens of thousands of CPU cores data pipeline.

The purpose is to extract high-quality content from the Internet based on filtering and deduplication.

Currently, TII has released a refined network data set, which is a carefully filtered and deduplicated data set. Practice has proved that it is very effective.

The model trained using only this data set can be on par with other LLMs, or even surpass them in performance. This demonstrates the excellent quality and influence of "Falcon".

In addition, the Falcon model also has multi-language capabilities.

It understands English, German, Spanish and French, and some small European languages ​​such as Dutch, Italian, Romanian, Portuguese, Czech, Polish and Swedish I also know a lot about it.

Falcon 40B is the second truly open source model after the release of the H2O.ai model. However, since H2O.ai has not been benchmarked against other models in this ranking, these two models have not yet entered the ring.

Looking back at LLaMA, although its code is available on GitHub, its weights have never been open source.

This means that the commercial use of this model is subject to certain restrictions.

Moreover, all versions of LLaMA depend on the original LLaMA license, which makes LLaMA unsuitable for small-scale commercial applications.

At this point, “Falcon” comes out on top again.

The only free commercial model!

Falcon is currently the only open source model that can be used commercially for free.

In the early days, TII required that if Falcon is used for commercial purposes and generates more than $1 million in attributable income, a 10% "use tax" will be charged.

But it didn’t take long for the wealthy Middle Eastern tycoons to lift this restriction.

At least so far, all commercial use and fine-tuning of Falcon will be free of charge.

The wealthy people said that they do not need to make money through this model for the time being.

Moreover, TII is also soliciting commercialization plans from around the world.

For potential scientific research and commercialization solutions, they will also provide more "training computing power support" or provide further commercialization opportunities.

##Project submission email: Submissions.falconllm@tii.ae

This is simply saying: as long as the project is good, the model is free! Enough computing power! If you don’t have enough money, we can still collect it for you!

For start-ups, this is simply a "one-stop solution for AI large model entrepreneurship" from the Middle East tycoon.

High-Quality Training Data

According to the development team, an important aspect of FalconLM’s competitive advantage is the selection of training data.

The research team developed a process to extract high-quality data from public crawled datasets and remove duplicate data.

After thorough cleaning of redundant and duplicate content, 5 trillion tokens were retained—enough to train powerful language models.

The 40B Falcon LM uses 1 trillion tokens for training, and the 7B version of the model uses 1.5 trillion tokens for training.

(The research team aims to filter out only the highest quality raw data from the Common Crawl using the RefinedWeb dataset)

More controllable training costs

TII said that compared with GPT-3, Falcon achieved the A significant performance improvement.

##And only when inferring It only takes 20% of the calculation time.

Falcon’s training cost is only equivalent to 40% of Chinchilla’s and 80% of PaLM-62B’s.

Successfully achieved efficient utilization of computing resources.

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