In practice these features alongside int4 weight only quantization allow us toreduce peak memory by ~55%, meaning we can Llama3.1-8B inference with a130k context length with only 18.9 GB of peak memory.More details can be foundhere
Quanto is device agnostic, meaning you can quantize and run your model regardless if you are on CPU/GPU/ MPS (Apple Silicon). Quanto is also torch.compile friendly. You can quantize a model with quanto and call `torch.compile` to the model to compile it for faster generation. This featur...
meaning the most important or impactful rows (determined by sampled inputs and outputs) are processed first. This method aims to place most of the quantization error (inevitably introduced during quantization) on less significant weights. This approach improves...
In language tasks, a few words/tokens in a sentence exhibit more importance than words to understand the overall meaning of a sentence better, leading to different patterns of self-attention applied to different parts of the input. In vision applications, a few regions in the input image may ...
In language tasks, a few words/tokens in a sentence exhibit more importance than words to understand the overall meaning of a sentence better, leading to different patterns of self-attention applied to different parts of the input. In vision applications, a few regions in the input image may ...
axis=1. These settings offer a good balance between quality, vram usage and speed. If you want better results with the same vram usage, switch toaxis=0and use the ATEN backend. If you want to use lower likenbits=2, you should useaxis=0with a low group-size via HQQ+, meaning adding...
(3) High Accuracy: The agent should maintain high translation accuracy, accurately conveying the meaning of the original language and avoiding ambiguity and misunderstandings as much as possible. (4) Contextual Understanding: The agent needs to understand the context of the text or speech and transla...
We've added kv cache quantization and other features in order to enable long context length (and necessarily memory efficient) inference. In practice these features alongside int4 weight only quantization allow us to reduce peak memory by ~55%, meaning we can Llama3.1-8B inference with a 130k...
Quanto is device agnostic, meaning you can quantize and run your model regardless if you are on CPU/GPU/ MPS (Apple Silicon). Quanto is also torch.compile friendly. You can quantize a model with quanto and call `torch.compile` to the model to compile it for faster generation. This featur...
In practice these features alongside int4 weight only quantization allow us to reduce peak memory by ~55%, meaning we can Llama3.1-8B inference with a 130k context length with only 18.9 GB of peak memory. More details can be found here Quantization Aware Training Post-training quantization can...