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
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...
Dr. Robert Kübler August 20, 2024 13 min read Hands-on Time Series Anomaly Detection using Autoencoders, with Python Data Science Here’s how to use Autoencoders to detect signals with anomalies in a few lines of… Piero Paialunga ...
Dettmers et al.[131]note that the outliers in activation matrices in a few layers break the quantization of LLMs. They quantize outliers to FP16 and other activations to 8 bits to resolve this issue, thereby improving accuracy but bringing challenges in the implementation. The GOBO technique[1...
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 ...
what's meaning of the debug info? The debug info is the min/max of the max of input. Please refer the code. neural-compressor/neural_compressor/adaptor/torch_utils/waq/utils.py Lines 286 to 291 in 24419c9 def cal_scale(input_max_abs, weights, alpha, weight_max_lb=1e-5): we...
(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...
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...
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...