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...
Conclusion In this article, we introduced the GPTQ algorithm, a state-of-the-art quantization technique to run LLMs on consumer-grade hardware. We showed how it addresses the layer-wise compression problem, based on an improved OBS technique with arbitrary order insight, lazy batch updates, and...
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...
waht's meaning of the debug info? i see smooth do nn.linear and nn.conv, so for llama2, lm_head is smoothed, right? and after smooth, len(sq.absorb_to_layer) == 65 instead of 1 ,why need to do assert len(sq.absorb_to_layer) == 1 ? by the way, does the code can run ...
conda create -n keras-llm-robot python==3.11.5 Clone the repository: git clone https://github.com/smalltong02/keras-llm-robot.git cd keras-llm-robot Activate the virtual environment: conda activate keras-llm-robot If you have an NVIDIA GPU, Please install the CUDA Toolkit from (https:...
You can use it on any model (LLMs, Vision, etc.). The dequantization step is a linear operation, this means that HQQ is compatbile with various optimized CUDA/Triton kernels. HQQ is compatible with peft training. We try to make HQQ fully compatible `torch.compile` for faster inference...
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...