xdfnetcommentedMay 15, 2024 What is the issue? D:\llama.cpp>ollama create eduaigc -f modelfile transferring model data using existing layer sha256:28ce318a0cda9dac3b5561c944c16c7e966b07890bed5bb12e122646bc8d71c4 creating new layer sha256:58353639a7c4b7529da8c5c8a63e81c426f206bab10...
llama_model_loader: - kv 20: general.quantization_version u32 = 2 llama_model_loader: - type f32: 121 tensors llama_model_loader: - type f16: 170 tensors time=2024-05-20T16:44:58.427+08:00 level=INFO source=server.go:540 msg="waiting for server to become available" status="llm ...
This paper studies the problem of adaptive control for planar nonlinear systems with input quantization. The Nussbaum-type function is applied to handle the unknown control directions. By virtue of dynamic gain technology, an adaptive controller is designed to ensure that all signals of the system ...
Considering the effect of quantization and ETS, the closed-loop system was transformed into a time-delay singular system. The co-design of the quantized state feedback controller and the ETS was presented. Nevertheless, to the best of authors’ knowledge, no report is addressed on the problem ...
there is less literature about the application of the event-triggered ASMC on the consensus control of MASs. It is worth mentioning that an event-triggered ASMC algorithm is proposed by Li39for a class of Takagi-Sugen fuzzy systems with actuator faults and signal quantization. This indicates th...
Abstract This paper addresses the joint state and fault estimation problem for a class of discrete-time networked systems with unknown measurement delays. A novel augmented observer is developed to simultaneously estimate system states, fault signals and the perturbed term caused by measurement delays. ...
Deep compression: compressing deep neural networks with pruning, trained quantization and Huffman coding preprint at arXiv (2015), 10.48550/arXiv.1510.00149 Google Scholar He et al., 2020 K. He, Y. Liu, M. Wang, G. Chen, Y. Jiang, J. Yu, C. Wan, D. Qi, M. Xiao, W.R. Leow,...
ADAPTIVE-LIKEVIBRATIONCONTROLINMECHANICALSYSTEMSWITHUNKNOWNPARAMENTERSANDSIGNALSF.Beltrán-CarbajalandG.Silva-..
quantization.utils import general_compress from bitblas.cache import global_operator_cache global_operator_cache.clear() linear_bitblas = BitBLASLinear( in_features, out_features, bias=bias, A_dtype="float16", W_dtype=W_dtype, accum_dtype="float16", out_dtype="float16", group_size=group_...
I seem to get the same problem whether I use a 32-bit, 16-bit, or 4-bit model. Does anything look amiss in the steps that I've performed or the logs which are generated from conversion/quantization? Any help at all would be appreciated!