Learning to Quantize Deep Networks by Optimizing Quantization Intervals with Task Loss,程序员大本营,技术文章内容聚合第一站。
A typical edge detection algorithm called Prewitt was used for tumor segmentation task, based on the output of the tumor localization. Overall performance of the proposed tumor segmentation architecture, was analyzed using objective quality parameters including Accuracy, Boundary Displacement Error (BDE),...
文中提出了一种Adaptive Loss-aware Quantization(ALQ)的模块,通过直接优化量化所带来的error来实现无损bit化,同时还引入了pruning的操作,来裁剪部分不重要的权重。ALQ无需梯度近似,同时也能够量化首尾两层。最后结果平均bit数低于1bit,但精度比较可观。 Distance-aware Quantization. paper ** 本文首先提出一种思路:...
tokenizer = AutoTokenizer.from_pretrained(checkpoint) model = AutoModelForCausalLM.from_pretrained(checkpoint, quantization_config=bnb_config, device_map="cuda") inputs = tokenizer(prompt, return_tensors="pt").to('cuda') outputs = model.generate(**inputs, do_sample=True, max_new_tokens=150)...
1 task done tonyawopened this issueJan 3, 2024· 2 comments Copy link tonyawcommentedJan 3, 2024 Reminder I have read the README and searched the existing issues. Reproduction 我使用下面的参数做inference: {"stage":"sft","model_name_or_path":"/workspace/model/deepseek-coder-33b-instruct"...
--task ceval --split validation --lang zh --n_shot 5 --batch_size 1\ Expected behavior 我计划是测试下deepseek-moe-16b-base在ceval上的效果,然后发现在evaluate的时候,加上--quantization_bit 4 的效果反而更好,template 尝试切换成 deepseek 也是一样的结果。
19. The system of claim 18, wherein the first of the plurality of vector instructions is associated with a first task and the first of one or more other instructions is associated with a second task. 20. The system of claim 1, wherein the at least one respective operand comprises at le...
In reality, existing quantization techniques fail to replicate their success on lightweight architectures such as MobileNet. To this end, we present a novel fully differentiable non-uniform quantizer that can be seamlessly mapped onto efficient ternary-based dot product engines. We conduct comprehensive...
Cisse et al. [131] 提出了针对于task losses的对抗样本,可以针对语音样本生成对抗样本,也可以迁移到人体姿势识别应用上。 ATN Baluja and Fischer [42] 训练了一种生成对抗样本的前向网络,名为对抗转化网络(ATN),主要有两部分的loss,一部分loss使得原图和对抗图尽可能相似,另外一部分则使得对抗样本识别错误。
Annotating natural language data is a time-consuming and labor-intensive task, requiring human expertise and effort. The lack of sufficient annotated data can limit the performance and generalization capabilities of deep NLP models. Interpretability is another issue in deep learning-based NLP systems. ...