这个问题已经过时了，因为它已经开放了60天，没有任何活动。当前的问题在60天内没有活动，被标记为过时...
LM Format Enforcer requires a python API to process the output logits of the language model. This means that until the APIs are extended, it can not be used with OpenAI ChatGPT and similar API based solutions. Regular expression syntax is not 100% supported. See interegular for more details...
Think step by step:<|im_end|>\n<|im_start|>assistant\n'] Starting from v4.46, the `logits` model output will have the same type as the model (except at train time, where it will always be FP32) tensor([[ 1249, 8253, 279, 1372, 315, 11067, 315, 264, 5792, 29372, 2661, 4...
这个问题已经过时了，因为它已经开放了60天，没有任何活动。当前的问题在60天内没有活动，被标记为过时...
base_model_runner.execute_model,在compute_logits和sampler之间调用回调函数。 record_step_event,用两个step_cuda_events轮流接收模型执行消息。下次调用时会阻塞。 driver_worker cached_outputs异步加入output结果。 driver_worker model_output.maybe_pythonize. ...
You need to apply the sigmoid function to convert these logits into probabilities. Threshold the mask probabilities: Apply a threshold (e.g., 0.5) to the sigmoid outputs to convert probabilities into a binary mask. Resize the masks: If the masks are at a different resolution than the ...
I'm able to deploy fine-tuned "bert-base-uncased" model on Triton inference server using TensorRT, while inference I am getting a NaN logits values. Converted the onnx model to tensorrt using command below. trtexec --onnx=model.onnx --saveEngine=model.plan --minShapes=input_ids:1x1,atte...
"module_id"]}}}\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{"name": <function-name>, "arguments": <args-json-object>}\n</tool_call><|im_end|>\n<|im_start|>user\nHi!
"from transformers.generation.logits_process import (\n", " EncoderNoRepeatNGramLogitsProcessor,\n", " EncoderRepetitionPenaltyLogitsProcessor,\n", " EpsilonLogitsWarper,\n", " EtaLogitsWarper,\n", " ExponentialDecayLengthPenalty,\n", " ForcedBOSTokenLogitsProcessor,\n", " ForcedEOSTokenLogits...
The values you're seeing in the output tensor are raw logits, not probabilities. They do not sum to 1 because they have not been passed through a softmax function. In YOLOv5, during inference, these logits are typically passed through a sigmoid function to convert them to objectness scores...