importosos.environ["cuda_visible_devices"]="0,1"fromaccelerateimportinfer_auto_device_map,init_empty_weights,load_checkpoint_and_dispatchfromtransformersimportAutoConfig,AutoModel,AutoModelForCausalLM,AutoTokenizer# import gradio as gr# import torchimporttimetokenizer=AutoTokenizer.from_pretrained(".\\cha...
`from transformers import pipeline import torch model = "databricks/dolly-v2-12b" pipeline = pipeline( "text-generation", model=model, torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto" )` 然而,如果我们尝试在HumanEval基准问题上执行该模型,我们会发现它与LLaMA和Mixtral相比表现...
torch_dtype="auto",device_map="auto")# 这里对大模型角色进行定义sys_content="You are a helpful assistant"# 获取千问 token 实例defsetup_qwen_tokenizer():returnAutoTokenizer.from_pretrained(model_name)# 设置问答输入信息defsetup_model_input(tokenizer,prompt):# 判断硬件...
ignore_patterns=[" .h5", " .ot", "*.mspack"]) model = AutoModelForCausalLM.from_pretrained( model_path, torch_dtype="auto", device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained(model_path) prompt = '你是谁' messages = [ {"role": "system", "content": "You are a help...
map_entry.first, py::reinterpret_steal<py::object>( torch::autograd::functionToPyObject( map_entry.second))); }returnfuncs; }) .def("_send_functions", [](constContextPtr& ctx) {std::map<int64_t, py::object> funcs;for(constauto& map_entry : ctx->sendFunctions()) { ...
generated::initialize_autogenerated_functions(); autoc_module = THPObjectPtr(PyImport_ImportModule("torch._C")); } 用来初始化cpp_function_types表,这个表维护了从cpp类型的函数到python类型的映射: static std::unordered_map<std::type_index, THPObjectPtr> ...
from transformers import AutoModelForSpeechSeq2Seqmodel_id = "openai/whisper-large-v3"quanto_config = QuantoConfig(weights="int8")model = AutoModelForSpeechSeq2Seq.from_pretrained( model_id, torch_dtype=torch.float16, device_map="cuda", quantization_config=quanto_config)你可查阅此 ...
self.normalize = normalize_to_neg_one_to_one if auto_normalize else identityself.unnormalize = unnormalize_to_zero_to_one if auto_normalize else identity @torch.inference_mode()def p_sample(self, x: torch.Tensor, timestamp: int) -> torch.Tensor:b, ...
at::AutoNonVariableTypeMode non_var_type_mode(true); //2\. convert theinputtensor to an NSMutableArrayfordebuggingfloat* floatInput = tensor.data_ptr<float>();if(!floatInput) {returnnil; } NSMutableArray* inputs = [[NSMutableArray alloc] init];for(inti =0; i <3* WIDTH * HEIGHT;...