Falcon – A guide to finetune and inference

Falcon, an open-source autoregressive decoder-only language model from the Technology Innovation Institute, is available in 7-billion- and 40-billion-parameter versions and has been integrated into Lit-GPT/Lit-Parrot for fine-tuning, quantization, and inference. The walkthrough uses Falcon-7B and the 52,000-example Alpaca instruction dataset, outlining a workflow to download and convert model weights, prepare and tokenize data into training and test sets, and fine-tune the model with AdapterV2 parameter-efficient training. Fine-tuning can run on a single GPU with at least 14 GB of memory, taking roughly three hours on one GPU or about 30 minutes across eight A100 GPUs, while the larger Falcon-40B model was originally trained on 384 AWS GPUs over two months. For deployment, supplied generation scripts support int8 and GPTQ int4 quantization, enabling Falcon-7B inference below 10 GB or approximately 4.5 GB of GPU memory respectively, although int4 may trade speed for lower memory use; the material also points readers to further resources on custom LLM training, mixed precision, LoRA, and other efficient fine-tuning methods.

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