更新后,查阅最新的官方文档,确认quantization_bit属性是否存在,以及如何正确使用。 2. 查阅文档和源代码 如果更新库后问题依旧,查阅最新的官方文档以了解ChatGLMConfig类的正确用法。此外,你也可以尝试直接查看库的源代码,以确认quantization_bit属性是否存在,以及它是否在某些特定条件下才被定义。 3. 检查代码引用 回顾...
quantization_bit可能是一个新版本中引入的属性,或者它可能根本不存在。 检查代码:检查你的代码,确保你没有误用quantization_bit属性。如果你是在尝试进行模型量化,那么可能应该在模型的训练或加载过程中设置这个属性,而不是直接在ChatGLMConfig对象上设置。 更新库:如果你确定quantization_bit是你需要的属性,并且你的Chat...
这个项目微调的时候可以设quantization_bit么?在sf_medchat.sh里怎么设?#41 Open chenxu126 opened this issue Jun 14, 2023· 0 comments Comments chenxu126 commented Jun 14, 2023 No description provided. Sign up for free to join this conversation on GitHub. Already have an account? Sign in ...
difference value to the subsequent difference value, and a sample value determination unit that determines, based on the detailed waveform, sample values for respective sampling times of the flat period by means of a quantization bit rate that is larger than the quantization bit rate of the audio...
目前,既要保证识别效果,同时还要使用 8 bit 量化模型,一种比较完备的做法就是将推理阶段的量化操作迁移到训练阶段,如 Tensorflow 说明文档一章介绍 Fixed Point Quantization。采用 fake 的量化后的浮点来作为 input 和 weight 的替换,同时浮点范围采用了平滑最大最小值的方法,具体可以查看 TensorFlow 的官方代码 Movi...
根据你提供的信息,如果系统环境不是Linux或不支持CUDA设备,则只支持8-bit量化。 如果系统环境是Linux且支持CUDA设备,则可能需要进一步检查以确认是否还支持其他类型的量化。这通常依赖于你所使用的深度学习框架或量化工具。 示例代码(假设使用PyTorch): 如果你在Linux系统上使用PyTorch,并且系统支持CUDA,你可以通过以下...
As far as I know vllm and ray doesn't support 8-bit quantization as of now. I think it's the most viable quantization technique out there and should be implemented for faster inference and reduced memory usage.
Quantization 8bit for yolov4 Assinar Mais ações Kartikeya Principiante 09-01-2020 10:27 PM 3.401 Visualizações Hi, I am trying to convert fp32 yolo model(trained on custom classes) into an int8 low precision quantized model. However upon conversion I am unable to see ...
examples/train_qlora/llama3_lora_sft_gptq.yaml i can not find quantization_bit param (but i see in LLaMA-Factory/examples/extras/fsdp_qlora /llama3_lora_sft.yaml) how can i set param to design 4/8 bit quantization Reminder I have read the README and searched the existing issues....
I tryed to modify your example code to run this model on lowvram card by BNB 4bit or 8bit quantization config. While use bnb 4bit config like below: qnt_config = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.float16, bnb_4bit_...