I use the following code to load the saved model: config = T5Config.from_pretrained( model_name_or_path, cache_dir=model_args.cache_dir, revision=model_args.model_revision, use_auth_token=True if model_args.use_auth_token else None, ) config.train_task_adapters = ada...
I'm trying to save themicrosoft/table-transformer-structure-recognitionHuggingface model (and potentially its image processor) to my local disk in Python 3.10. The goal is to load the model inside a Docker container later on without having to pull the model weights and configs from Huggi...
I'm having the same issue, i've fine tuned a Llama 7b model using peft, and got satisfying results in inference, but when i try to use SFTTrainer.save_model, and load the model from the saved files using LlamaForCausalLM.from_pretrained, the inference result seem to just be of the ...
第一步load进来的model的细节: model init BloomForCausalLM((transformer):BloomModel((word_embeddings):Embedding(250880,1024)(word_embeddings_layernorm):LayerNorm((1024,),eps=1e-05,elementwise_affine=True)(h):ModuleList((0):BloomBlock((input_layernorm):LayerNorm((1024,),eps=1e-05,elementwise...
如上面表格中的example所示,只需要在load_dataset()函数中指定加载数据的类型(csv或tsv表格数据、text文本数据、json或json lines格式数据已经pandas保存的pickle文件)以及设置参数data_files来指定一个或多个文件即可。下面我们使用上面的方法加载本地文件,后面会介绍加载远程数据。
model_checkpoint = "distilbert-base-uncased" # use_fast: Whether or not to try to load the fast version of the tokenizer. # Most of the tokenizers are available in two flavors: a full python # implementation and a “Fast” implementation based on the Rust library Tokenizers. ...
cache_dir='/home/{username}/huggingface')# Set `torch_dtype=torch.float16` to load model in ...
· load_best_model_at_end 表示在测试集上计算使用性能最好的模型(用 metric_for_best_model 指定)的模型。 · report_to 将所有训练和验证的数据报告给 TensorBoard。 args = TrainingArguments( # output_dir: directory where the model checkpoints will be saved. ...
# Uncomment to instead load the model I trained earlier: # model = butterfly_pipeline.unet 步骤6:生成图像 我们怎么从这个模型中得到图像呢? 方法1:建立一个管道: from diffusersimportDDPMPipeline image_pipe = DDPMPipeline(unet=model, scheduler=noise_scheduler) ...
I would want the model to load and the API to start listening on the designated port.dawnik17 changed the title Error: Loading Locally Saved Model Error While Loading Locally Saved Model Nov 24, 2023 odellus mentioned this issue Nov 24, 2023 Please add support for neural-chat-7b-v3-1...