same error when I load model on multiple gpus eg. 4,which set bu CUDA_VISIBLE_DEVICES=0,1,2,3. but when I load model only in 1 gpu, It can generate result succesfully. my code: ` tokenizer = LlamaTokenizer.from_pretrained(hf_model_path) model = LlamaForCausalLM.from_pretrained( hf...
Here is my load model code... ` model = AutoModelForCausalLM.from_pretrained( script_args.model_name_or_path, low_cpu_mem_usage=True, torch_dtype=torch.float16, load_in_4bit=True, device_map="auto", trust_remote_code=True) model_ref = A...
model ="mistralai/Mixtral-8x7B-Instruct-v0.1" tokenizer = AutoTokenizer.from_pretrained(model) pipeline = transformers.pipeline( "text-generation", model=model, model_kwargs={"torch_dtype": torch.float16,"load_in_4bit":True}, ) messages = [{"role":"user","content":"Explain what a Mi...
optimum-cli inc quantize --model distilbert-base-cased-distilled-squad --output ./quantized_distilbert To load a model quantized with Intel Neural Compressor, hosted locally or on the 🤗 hub, you can do as follows : fromoptimum.intelimportINCModelForSequenceClassification model_id ="Intel/dist...
AMD GPU: Enables running the DeepSeek-V3 model on AMD GPUs via SGLang in both BF16 and FP8 modes. Huawei Ascend NPU: Supports running DeepSeek-V3 on Huawei Ascend devices. Since FP8 training is natively adopted in our framework, we only provide FP8 weights. If you require BF16 weights ...
on the down-stream task tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') for model_class in BERT_MODEL_CLASSES: # Load pretrained model/tokenizer model = model_class.from_pretrained('bert-base-uncased') # Models can return full list of hidden-states & attentions weights at each...
load(filename)) ⚠️:如果你是使用prepare()方法之后的话,那么模型加载权重的时候是需要用accelerator.unwrap_model方法的。其他情况下,问题不大。 保存或加载训练的整个状态 这里说的整个训练过程状态是指保存/加载 训练模型过程中的model、optimizer、random generators和LR schedulers。详情参考文档吧,一般情况...
StoppingCriteriaList,TextIteratorStreamerfromthreadingimportThreadimportosfromhuggingface_hubimportInferenceClientimportgradioasgrimportrandomimporttimefromTTS.apiimportTTSimporttorchfromtransformersimportAutoModelForSpeechSeq2Seq,AutoProcessor,pipelinefromdatasetsimportload_datasetimportscipy.io.wavfileaswavfileimportnumpy...
model_id, load_in_8bit=True, device_map='auto', cache_dir='model_cache') # Define LoRA Config lora_config = LoraConfig( r=16, lora_alpha=32, lora_dropout=0.05, bias="none", task_type=TaskType.SEQ_2_SEQ_LM ) # prepare int-8 model for training ...
model_name = "meta-llama/Llama-2-7b-hf" tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True) tokenizer.pad_token = tokenizer.eos_token bnb_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type="nf4", ...