model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto") tokenizer = AutoTokenizer.from_pretrained(model_name) text = "say" inputs = tokenizer(text,return_tensors="pt") print(f"inputs:{inputs}") 这里的关键函数是 AutoModelForCausalLM。因果语言建模 (CLM) 是一种语言建模...
# `device_map` cannot be set to `auto` model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True, device_map="sequential", torch_dtype=torch.bfloat16, max_memory=max_memory, attn_implementation="eager") model.generation_config = GenerationConfig.from_pretrained(model_na...
您可以通过使用并设置来量化模型。 fromtransformersimportAutoModelForCausalLM model=AutoModelForCausalLM.from_pretrained(model_id,quantization_config=gptq_config) 请注意,您需要一个GPU来进行模型的量化。我们会将模型放置在CPU中,并在GPU和CPU之间来回移动各个模块以进行量化。 如果您想在使用CPU offload的同时最...
Whenever I set the parameterdevice_map='sequential', only the first gpu device is taken into account. For models that do not fit on the first gpu, the model returns a cuda OOM, as if only running on the first gpu, instead of spilling over to the second gpu. ...
model=transformers.LlamaForCausalLM.from_pretrained("path/to/converted/llama-65B",load_in_8bit=True,device_map="auto") You'll see that only the first two GPUs are filled up. Possibly related to#22377. Expected behavior All 4 GPUs should get parameters. ...
model_name="deepseek-ai/DeepSeek-V2"tokenizer=AutoTokenizer.from_pretrained(model_name,trust_remote_code=True)#`max_memory`should besetbased on your devices max_memory={i:"75GB"foriinrange(8)}#`device_map`cannot besetto`auto`model=AutoModelForCausalLM.from_pretrained(model_name,trust_remote...
model=model, args=training_args, train_dataset=train_tokenized, eval_dataset=validation_tokenized, )# trainingtrainer.train() 过程也非常简单,设置训练参数,使用Trainer对象绑定所有内容,并启动流程。上述超参数都是测试目的,所以如果要得到最好的结果还需要进行超参数的设置,我们使用这些参数是可以运行的。
https://www.modelscope.cn/models/colossalai/grok-1-pytorch/summary 性能优化 结合Colossal-AI在AI大模型系统优化领域的丰富积累,已迅速支持对Grok-1的张量并行。 在单台8H800 80GB服务器上,推理性能相比JAX、HuggingFace的auto device map等方法,推理时延加速近4倍。
fromtransformersimportAutoModelimporttorchdefsetup_optimization():"""优化模型加载配置"""model=AutoModel.from_pretrained("bert-base-chinese",device_map="auto",# 自动设备分配torch_dtype=torch.float16,# 使用半精度浮点数减少内存占用low_cpu_mem_usage=True# 分批加载模型参数)model.eval()# 切换到推理模...
首先,Huggingface的这些transformer Model直接call的时候,接受的标签这个参数是叫"labels"。所以不管你使用Trainer,还是原生pytorch去写,最终模型处理的时候,肯定是使用的名为"labels"的标签参数。 但在Huggingface的datasets中,数据集的标签一般命名为"label"或者"label_ids",那为什么在前两集中,我们没有对标签名进行处理...