Optimizing your LLM in production
Open In Colab
Note: This blog post is also available as a documentation page on Transformers.
Large Language Models (LLMs) such as GPT3/4, Falcon, and LLama are rapidly advancing in their ability to tackle human-centric tasks, establishing themselves as essential tools in modern knowledge-based industries. Deploying these models in real-world tasks remains challenging, however:
To exhibit near-human text understanding and generation capabilities, LLMs currently require to be composed of billions of parameters (see Kaplan et al, Wei et. al). This consequently amplifies the memory demands for inference.
In many real-world tasks, LLMs need to be given extensive contextual information. This necessitates the model's capability to manage very long input sequences during inference.
The crux of these challenges lies in augmenting the computational and memory capabilities of LLMs, especially when handling expansive input sequences.https://github.com/huggingface/blog/blob/main/optimize-llm.md
Open In Colab
Note: This blog post is also available as a documentation page on Transformers.
Large Language Models (LLMs) such as GPT3/4, Falcon, and LLama are rapidly advancing in their ability to tackle human-centric tasks, establishing themselves as essential tools in modern knowledge-based industries. Deploying these models in real-world tasks remains challenging, however:
To exhibit near-human text understanding and generation capabilities, LLMs currently require to be composed of billions of parameters (see Kaplan et al, Wei et. al). This consequently amplifies the memory demands for inference.
In many real-world tasks, LLMs need to be given extensive contextual information. This necessitates the model's capability to manage very long input sequences during inference.
The crux of these challenges lies in augmenting the computational and memory capabilities of LLMs, especially when handling expansive input sequences.https://github.com/huggingface/blog/blob/main/optimize-llm.md