The article also compares Falcon-7B with other popular language models like Flan-T5-Large and the BERT family, with Falcon-7B showing better performance. Falcon-7B also performs well when the amount of available labeled data is limited. The authors found that the costs to train Falcon-7B are low and it takes a reasonable amount of time. They also benchmarked Falcon-7B’s throughput via Hugging Face’s text-generation pipeline and found that it performs better in terms of speed for summarization than for classification.
Key takeaways:
- Falcon, a Large Language Model (LLM) developed by researchers at the Technology Innovation Institute in Abu Dhabi, has been assessed for its performance, time to train, costs, and inference. It has received significant attention due to its emphasis on high-quality training data.
- Falcon outperforms other LLMs like Flan-T5-Large and the BERT family in classification and summarization tasks, especially when the amount of available labeled data is limited.
- Fine-tuning Falcon-7B takes a reasonable amount of time, and the associated costs to train are low, making it a cost-effective choice for businesses.
- Falcon-7B performs better in terms of speed for summarization than for classification, but the difference in speed between the two tasks isn't large, making it suitable for both classification and summarization tasks.