git clone https://github.com/SqueezeAILab/SqueezeLLM cd SqueezeLLM pip install -e . cd squeezellm python setup_cuda.py install From-scratch Quantization To quantize your own models, follow the procedure in thislink. Currently, we supportLLaMA7B, 13B, 30B and 65B,LLaMA-27B and 13B, instruct...
To Reproduce mapping = ME.utils.sparse_quantize(coords, return_index=True, device=['cuda:1']) Desktop (please complete the following information): OS: Ubuntu 18.04 Python version: 3.8.10 Pytorch version: 1.8.1 CUDA version: 10.2 Minkowski Engine version: 0.5.4chris...
Prune and Quantize YOLOv5 for a 12x Increase in Performance and a 12x Decrease in Model Files Neural Magic improves YOLOv5 model performance on CPUs by using state-of-the-art pruning and quantization techniques combined with theDeepSparse Engine. In this blog post, we'll cover our general m...
然后我们就可以得到一个quantize的模型: 在这里面,我们展示的是一个实例分割模型,这里面包含了非常多的复杂操作,例如各种shape的组合,以及各种concat,各种interpolate, 其中很多算子是没有办法去量化的,至少很多前推引擎并不支持。 但是我们不管那么多,一顿梭哈,无脑梭哈。 然后我们就可以得到这么一个int8的模型: 模型...
clone() q[mask1[:, i]] = 0 if hasattr(self, 'quantizer'): q = quantize(q.unsqueeze(1), self.quantizer.scale, self.quantizer.zero, self.quantizer.maxq).flatten() Q1[:, i] = q Losses1[:, i] = (w - q) ** 2 / d ** 2 # (w - q) / d它的shape其实是一维的,就是...
For a new given fingerprint images, represent its patches according to the dictionary by computing l0-minimization and then quantize and encode the representation. In this paper, we consider the effect of various factors on compression results. Three groups of fingerprint images are tested. The ...
1.1Prior work We now briefly review some of the algorithms that have resulted from the above assumptions. One way to exploit the low rank assumption is to find a matrix whose rank is the smallest among all matrices which agree with the observed ratings at the known entries of the matrix. ...
To use the Minkowski Engine, you first would need to import the engine. Then, you would need to define the network. If the data you have is not quantized, you would need to voxelize or quantize the (spatial) data into a sparse tensor. Fortunately, the Minkowski Engine provides the quant...
apply(compress_quantized_weights) output_dir = "./ex_llama1.1b_w4a16_packed_quantize" compressor = ModelCompressor(quantization_config=config) compressed_state_dict = compressor.compress(model) model.save_pretrained(output_dir, state_dict=compressed_state_dict) For more in-depth tutorial on ...
However, in scenarios where pre-splitting is inappropriate, the system has to quantize the input vector as a whole. Such scenarios lead to Type V SVQ. In contrast to Type I SVQ, Type V SVQ performs post-splitting of an input vector, which breaks the input vector into several separate ...