bnb_4bit_compute_dtype=compute_dtype)# Load model and tokenizermodel = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=bnb_config, use_cache = False, device_map=device_map)model.config.pretraining_tp = 1# Load the tokenizertokenizer = AutoTokenizer.from_pretrained(model_id...
config = AutoConfig.from_pretrained(modelName) tokenizer = AutoTokenizer.from_pretrained(modelName) # 初始化模型 model = AutoModelForCausalLM.from_pretrained(modelName, config=config) # 由于Kaggle GPU的VRAM有限,我们将使用BitsAndBytes进行4位量化 bnbConfig = AutoConfig.from_pretrained(modelName) # ...
model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=bnb_config, use_cache = False, device_map=device_map) model.config.pretraining_tp = 1 # Load the tokenizer tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True) tokenizer.pad_token = tokenizer.eos...
完整的代码如下：from transformers import AutoTokenizer, AutoModelForCausalLMimport requeststokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")def generate_text(prompt, model, tokenizer, length=500, temperature=0.7)...
model=AutoModelForCausalLM.from_pretrained(model_id,quantization_config=bnb_config,use_cache=False,device_map=device_map)model.config.pretraining_tp=1# Load the tokenizer tokenizer=AutoTokenizer.from_pretrained(model_id,trust_remote_code=True)tokenizer.pad_token=tokenizer.eos_token tokenizer.padding_s...
# Load model and tokenizer model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=bnb_config, use_cache = False, device_map=device_map) model.config.pretraining_tp = 1 # Load the tokenizer tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True) tokenize...
model = AutoModelForCausalLM.from_pretrained(modelName,device_map="auto",quantization_config=bnbConfig 创建一个简单的提示模板,包括系统、用户和AI。我们要求模型生成Python代码来显示星号模式。 在新的笔记本中,我们首先修改标题,然后将加速器更改为GPT T4 x2。接下来,我们将按照步骤安装并更新所需的Python包,...
model = AutoModelForCausalLM.from_pretrained(model_id,quantization_config=bnb_config, use_cache =False,device_map=device_map) model.config.pretraining_tp = 1 # Load the tokenizer tokenizer = AutoTokenizer.from_pretrained(model_id,trust_remote_code=True) ...
tokenizer = AutoTokenizer.from_pretrained(modelName) # 初始化模型 model = AutoModelForCausalLM.from_pretrained(modelName, config=config) # 由于Kaggle GPU的VRAM有限,我们将使用BitsAndBytes进行4位量化 bnbConfig = AutoConfig.from_pretrained(modelName) ...
OVModelForCausalLM通常用于加载经过 OpenVINO 优化的模型。确保你的模型是经过 OpenVINO 优化的格式（例如...