### Task: {instruction} ### Input: {input} ### Response: """ # Tokenize the input input_ids = tokenizer(prompt, return_tensors="pt", truncation=True).input_ids.cuda() # Run the model to infere an output outputs = model.generate(input_ids=input_ids, max_new_tokens=100, do_samp...
device="cuda:2"# the device to load the model onto tokenizer=AutoTokenizer.from_pretrained(model_dir,trust_remote_code=True)prompt="介绍一下大语言模型"messages=[{"role":"system","content":"你是一个智能助理."},{"role":"user","content":prompt}]text=tokenizer.apply_chat_template(messages...
每一步decode前都要运行。 outputs = self(**model_inputs, return_dict=True)进行LLama模型推理 input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1) 将新得到的token拼接到原来的句子上,然后往复循环,直到decode停止 LlamaForCausalLM是LLama进行自回归解码的类,其属性如下 self.model =L...
### Instruction:Use the Task below and the Input given to write the Response, which is a programming code that can solve the following Task:### Task:Develop a Python program that prints "Hello, World!" whenever it is run.### Input:### Response:#Python program to print "Hello World!...
Theinstance_countparameter in the config.pbtxt file specifies the number of instances of the model to run. Ideally, this should be set to match the maximum batch size supported by the TRT engine, as this allows for concurrent request execution and reduces performance bottlenecks. However, it ...
函数名:build_model_input 函数传入参数: model_tokenizer:模型tokenizer,由平台从上传的模型中加载; messages:用户调用会话模型服务时传入的会话信息,当为会话模式时生效; kwargs:其他参数,目前未使用。当未来功能升级时,做向前兼容使用。 函数输出结果: token_ids: 转换后的一维token id数组,将用于喂入模型 样例...
For this section, let’s assume that we have four GPUs and the CUDA device ids are 0, 1, 2, and 3. We will be launching two instances of the T5-small model with tensor parallelism 2 (TP=2). The first instance will run on GPUs 0 and 1 and the second instanc...
这个错误可能是由于模型和GPU之间的数据类型不匹配导致的。你可以尝试将模型的输入数据转换为与GPU相同的...
input_ids.to("cuda") outputs = model.generate(input_ids) print(tokenizer.decode(outputs[0]))` I am able to see the model by running below code on sagemaker, so i am sure the path is correct. `s3 = boto3.client('s3') List all objects in the model folder from S3 ...
_inputs=tokenizer([input_ids],return_tensors="pt").to('cuda')generated_ids=model.generate(model_inputs.input_ids,max_new_tokens=512)generated_ids=[output_ids[len(input_ids):]forinput_ids,output_idsinzip(model_inputs.input_ids,generated_ids)]response=tokenizer.batch_decode(generated_ids,...