train_dataset, shuffle=True, collate_fn=data_collator, batch_size=args.per_device_train_batch_size9 changes: 8 additions & 1 deletion 9 examples/pytorch/question-answering/run_qa_beam_search_no_trainer.py Original file line numberDiff line numberDiff line change ...
def __init__(self, device, local_files_only=False, empty_cache=True, unload_models=True, dtype=torch.bfloat16, out_dir=None, save_interval=None, debug=False, no_safe=False): self.local_files_only = local_files_only self.empty_cache = empty_cache self.unload_models = unload_models ...
I set up Intel CV SDK beta R3 environment under Ubuntu 16.04, and tested according to official guide like https://software.intel.com/en-us/inference-engine-devguide-converting-your-caffe-model. ;However, when I test example commands from it, FP32 model conversion pas...
)[1], "displayName": "Contact us" } ] }; var nav = new SP.UI.Controls.Navigation( "chrome_ctrl_placeholder", options ); nav.setVisible(true); } // Function to retrieve a query string value. // For production purposes you may want to use // a library to handle the query string...
")[1],"displayName":"Account settings"}, {"linkUrl":"Contact.html?"+ document.URL.split("?")[1],"displayName":"Contact us"} ] };varnav =newSP.UI.Controls.Navigation("chrome_ctrl_placeholder", options ); nav.setVisible(true); }// Function to retrieve a query string value.// ...
disable_flashinfer_sampling=True, disable_radix_cache=False, disable_regex_jump_forward=False, disable_cuda_graph=False, disable_disk_cache=False, enable_mixed_chunk=False, enable_torch_compile=False, enable_p2p_check=False, enable_mla=False, attention_reduce_in_fp32=False, efficient_weight_load...
Running on local URL:http://127.0.0.1:7860 To create a public link, setshare=Trueinlaunch(). Folder 100_Abey Ja: 16 images found Folder 100_Abey Ja: 1600 steps max_train_steps = 1600 stop_text_encoder_training = 0 lr_warmup_steps = 160 ...
Hi, I was trying to use FP16 and INT8. I understand this is how you prepare a FP32 model. model = onnx.load("/path/to/model.onnx") engine = backend.prepare(model, device='CUDA:1') input_data = np.random.random(size=(32, 3, 224, 224)).ast...
checkpoint=torch.load(ckpt_path,weights_only=True,map_location=device) checkpoint=torch.load(ckpt_path,weights_only=True) ifuse_ema==True: ema_model=EMA(model,include_online_model=False).to(device) ifuse_ema: ifckpt_type=="safetensors": ...
fp16data := gocv.FP16BlobFromImage(img, 1.0/128.0, image.Pt(299, 299), 128.0, true, false)// load image tensor into graph on NCS stick loadStatus := graph.LoadTensor(fp16Blob.ToBytes()) loadStatus := graph.LoadTensor(fp16data) ...