#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 ...
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...
The quantized model can be saved usingsave_pretrained: qmodel.save_pretrained('./Llama-3.1-8B-quantized') It can later be reloaded usingfrom_pretrained: fromoptimum.quantoimportQuantizedModelForCausalLMqmodel=QuantizedModelForCausalLM.from_pretrained('Llama-3.1-8B-quantized') ...
qmodel.save_pretrained('./Llama-3.1-8B-quantized') It can later be reloaded usingfrom_pretrained: fromoptimum.quantoimportQuantizedModelForCausalLM qmodel = QuantizedModelForCausalLM.from_pretrained('Llama-3.1-8B-quantized') You can see more details andexamplesin theQuantorepository. ...
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....
quanto import freeze freeze(model) 5. Serialize quantized model Quantized models weights can be serialized to a state_dict, and saved to a file. Both pickle and safetensors (recommended) are supported. from safetensors.torch import save_file save_file(model.state_dict(), 'model...
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") ...
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/ \ ...
("人工智能正在",max_length=50)print(f"生成文本:{text}")# 3. 命名实体识别ner=pipeline("ner",model="bert-base-chinese")entities=ner("华为总部位于深圳")print(f"识别实体:{entities}")# 4. 问答系统qa=pipeline("question-answering",model="bert-base-chinese")context="北京是中国的首都,上海是...