Describe the issue model_path = "liuhaotian/llava-v1.5-7b" tokenizer, model, image_processor, context_len = load_pretrained_model( model_path=model_path, model_base=None, model_name=get_model_name_from_path(model_path), offload_folder="/...
老娘真的要吐血啦, pytorch版本load pretrained model不兼容和matlab is unable to call pytorch0.4。 至于为什么matlab不能call pytorch0.4, 网上有很多猜测,比如matlablibc++版本太老,线程的原因,反正都不靠谱,只能回退。 测试别人的网络,要求pytorch0.4以上,然后我就更新啦, 并且在0.4的版本下训练了自己的网络,然后...
5 Load a single image in a pretrained pytorch net 6 How to load and use a pretained PyTorch InceptionV3 model to classify an image 1 PyTorch transfer learning with pre-trained ImageNet model 0 Load Pre-trained model in Pytorch 0 How to load pretrained googlenet model in pyto...
PreTrainedModel.from_pretrained:加载模型结构和模型参数 load_checkpoint 从checkpoint 中加载模型 parameter,而不加载模型结构
How can I load back the model so that I can use it in other places, like cnn visualization? This is how I am loading the model now: torch.load('model_best.pth.tar') But when I do this, I get this error: AttributeError: 'dict' object has no attribute 'eval' Wh...
INFO:test2:Loaded model weights INFO:test2:loaded pretrained pretrained/f0G40k.pth Process Process-1: Traceback (most recent call last): File "/opt/app/tts/VoiceConversionWebUI/extract/RVC0813Nvidia/train_nsf_sim_cache_sid_load_pretrain.py", line 183, in run ...
# Load tokenizer and model tokenizer = LlamaTokenizer.from_pretrained(path_to_llama2) config = LlamaConfig.from_pretrained(path_to_llama2) config.output_hidden_states = True config.output_attentions = True config.use_cache = True model = LlamaForCausalLM.from_pretrained(path_to_llama2, config...
model = AutoModel.from_pretrained(r'/Users/maxiong/Workpace/Code/transformers/pre_model/bert_model.ckpt.index', from_tf=True, config=config) when I used TFAutoModel to load a model, there is like this model = TFAutoModel.from_pretrained(r'/Users/maxiong/Workpace/Code/transformers/pre_...
quantization_config = BitsAndBytesConfig(load_in_8bit=True, llm_int8_enable_fp32_cpu_offload=True) AutoModelForCausalLM.from_pretrained(path, device_map='auto', quantization_config=quantization_config) If the model does not fit into VRAM, it reports: ...
I want to load a huggingface pretrained transformer model directly to GPU (not enough CPU space) e.g. loading BERT from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("bert-base-uncased") would be loaded to CPU until executing model.to('cuda')...