Unite.AI

AUGUST 6, 2024

These new use cases necessitate multiple, often dependent, LLM generation calls, indicating a trend of using multi-call structures to complete complex tasks. State-of-the-art inference engines, optimized to reduce latency and improve throughput, lack direct knowledge of the workload, resulting in significant inefficiencies.

LLM 130

LLM Inference Engine Auto-complete Python 130

AWS Machine Learning Blog

APRIL 20, 2023

With advancements in hardware design, a wide range of CPU- and GPU-based infrastructures are available to help you speed up inference performance. Analyze the default and advanced Inference Recommender job results, which include ML instance type recommendation latency, performance, and cost metrics. sm_client = boto3.client("sagemaker",

ML 103

ML Auto-classification Python Auto-complete 103

Mlearning.ai

JULY 9, 2023

launch() This Python script uses a HuggingFace Transformers library to load the tiiuae/falcon-7b-instruct model. launch() This Python script uses a HuggingFace Transformers library to load the tiiuae/falcon-7b-instruct model. We leverage the python bindings for LLaMA.cpp to load the model.

Large Language Models 52

Large Language Models LLM Python Auto-complete 52