import tiktoken def num_tokens_from_string(string: str, model_name: str) -> int: try: encoding = tiktoken.encoding_for_model(model_name) except KeyError as e: raise KeyError(f"Error: No encoding available for the model '{model_name}'. Please check the model name and try again.") n...
通过使用模型编译器(model compiler),可以进一步优化计算图。 无论如何,你又一次用灵活性(flexibility)换取了更少的开销(overhead),因为跟踪/编译(tracing/compilation)要求张量大小、类型(tensors sizes, types)等参数是静态的,因此在程序运行时期(runtime)中需要保持不变。控制流结构,如if-else,通常也会在此过程...
通过使用模型编译器(model compiler),可以进一步优化计算图。 无论如何,你又一次用灵活性(flexibility)换取了更少的开销(overhead),因为跟踪/编译(tracing/compilation)要求张量大小、类型(tensors sizes, types)等参数是静态的,因此在程序运行时期(runtime)中需要保持不变。控制流结构,如if-else,通常也会在此过程...
meta:modelftype = mostly Q4_0llm_load_print_meta:modelsize=13.02Bllm_load_print_meta: general.name = LLaMA v2llm_load_print_meta: BOS token =1'<s>'llm_load_print_meta: EOS token =2'</s>'llm_load_print_meta: UNK token =0'<unk>'llm_load_print_meta: LF token =13'<0x0A>'ll...
通过使用模型编译器(model compiler),可以进一步优化计算图。 无论如何,你又一次用灵活性(flexibility)换取了更少的开销(overhead),因为跟踪/编译(tracing/compilation)要求张量大小、类型(tensors sizes, types)等参数是静态的,因此在程序运行时期(runtime)中需要保持不变。控制流结构,如if-else,通常也会在此过程...
- model (str, optional): The model to use for generating summaries. Defaults to 'gpt-3.5-turbo'. - additional_instructions (Optional[str], optional): Additional instructions to provide to the model for customizing summaries. - minimum_chunk_size (Optional[int], optional): The minimum size ...
# >>> Model: replit/replit-code-v1-3b - Temperature = 0.2 # >>> Prompt: """ double_sum_to_value takes a list of integers as an input. It returns True if there are two distinct elements in the list that \\ sum to a value given in input, and False otherwise. ...
1. 模型状态 (model states):模型参数 (fp16) 、模型梯度 (fp16) 和Adam状态 (fp32 的模型参数备份,的 momentum 和 fp32 的 variance) 。假设模型参数量,则共需要字节存储,可以看到,Adam 状态占比。 2. 剩余状态 (residual states) :除了模型状态之外的显存占用,包括激活值 (activation) 、各种临时缓冲区...
Continuous model evaluation is critical to prevent propagation of bias or harmful content. By implementing a robust monitoring and evaluation framework, model consumers can proactively identify and address regression in LLMs, ensuring that these models maintain their ...
model_inputs=tokenizer(inputs) labels=tokenizer(targets)#依次处理每个样本foriinrange(batch_size): sample_input_ids=model_inputs["input_ids"][i] label_input_ids=labels["input_ids"][i]+[tokenizer.pad_token_id]#将输入文本(model_inputs)与标签(labels)“对齐”(设置成一样),然后将标签中对应...