#using low_cpu_mem_usage since model is quantized model = AutoModelForCausalLM.from_pretrained(base_model_path,quantization_config=bnb_config,low_cpu_mem_usage=True) 测试Gemma 2B基础模型的输出 # just to test the base model response text = "Instruction: Can you explain contrastive learning in ...
Also, if anyone knows a better way to save a 4bit quantized model that would be great. Currently save_pretrained() does not seem to work with 4bit quantized models. pytorch huggingface-transformers huggingface large-language-model Share Improve this question Follow asked Nov 7, 2023 at 23:...
保存为ONNX后,可以继续通过ORTModelForXXX来加载模型,然后使用pipeline来运行任务。 fromoptimum.onnxruntimeimportORTModelForSequenceClassificationfromtransformersimportpipeline,AutoTokenizer model=ORTModelForSequenceClassification.from_pretrained(save_directory,file_name="model_quantized.onnx")tokenizer=AutoTokenizer.f...
quantized_model.save_pretrained("opt-125m-gptq")tokenizer.save_pretrained("opt-125m-gptq") 如果您已使用device_map量化了您的模型,在保存之前,请确保将整个模型移动到您的GPU之一或cpu上。 quantized_model.to("cpu")quantized_model.save_pretrained("opt-125m-gptq") 从🤗 Hub加载量化模型 您可以使...
Note: the model quantized weights will be frozen. If you want to keep them unfrozen to train them you need to useoptimum.quanto.quantizedirectly. The quantized model can be saved usingsave_pretrained: qmodel.save_pretrained('./Llama-3-8B-quantized') ...
上图中的model.onnx是pytorch转为onnx模型文件,model_quantized.onnx是量化后的模型文件。 对比下transformers模型和onnx量化后模型运行速度对比。代码如下: save_directory="tmp/onnx/"model_checkpoint="../../../pretrained_weights/distilbert-base-uncased-finetuned-sst-2-english"fromtransformersimportAutoMo...
tokenizer.save_pretrained(output_path) 本例中,我们使用qasper数据集的一个子集作为校准集。 第2 步: 加载模型,运行推理 仅需运行以下命令,即可加载量化模型: fromoptimum.intelimportIPEXModel model = IPEXModel.from_pretrained("Intel/bge-small-en-v1.5-rag-int8-static") ...
younesbelkadacommentedAug 17, 2023 👀3nlpcat, majid999, and kbulutozler reacted with eyes emoji 👀 nathan-azmentioned this issueAug 28, 2023 Is there any way to save models trained with 4 bit quantization?TimDettmers/bitsandbytes#738 Closed...
qmodel = QuantizedPixArtTransformer2DModel.quantize(model, weights=qfloat8) qmodel.save_pretrained("pixart-sigma-fp8") 此代码生成的 checkpoint 大小为587MB,而不是原本的 2.44GB。然后我们可以加载它: fromoptimum.quantoimportQuantizedPixArtTransformer2DModel ...
--model-type GPT \ --loader llama2_hf \ --saver megatron \ --target-tensor-parallel-size 1 \ --target-pipeline-parallel-size 2 \ --load-dir ./model_from_hf/llama-2-7b-hf/ \ --save-dir ./model_weights/llama-2-7b-hf-v0.1-tp8-pp1/ \ ...