This approach can significantly reduce the computational requirements and memory footprint compared to traditional full model fine-tuning. SELECT pgml.tune( 'fingpt-llama2-7b-chat', task => 'conversation', relation_name => 'pgml.fingpt_sentiment_train_view', model_name => 'meta-llama/Llama...
Benchmark: the statistics about inference speed and memory footprint. Introduction After months of efforts, we are pleased to announce the evolution from Qwen1.5 to Qwen2. This time, we bring to you: Pretrained and instruction-tuned models of 5 sizes, including Qwen2-0.5B, Qwen2-1.5B, Qwe...