tokenizer = AutoTokenizer.from_pretrained( model_path_image_interpretation_1, trust_remote_code=True ) # pipe = AutoModelForCausalLM.from_pretrained( # model_path_image_interpretation_1, # device_map=""auto"", #
SamplingParams# 加载Qwen1.5-4B-Chat模型的tokenizer tokenizer =AutoTokenizer.from_pretrained("Qwen/Qw...
from modelscope import AutoModel, AutoTokenizer from decord import VideoReader, cpu # pip install decord params={} model = AutoModel.from_pretrained('OpenBMB/MiniCPM-V-2_6-int4', trust_remote_code=True) # sdpa or flash_attention_2, no eager model = model.eval() tokenizer = AutoTokenize...
{"conversations": [{"from": "human", "value": "问题"}, {"from": "gpt", "value": "回答"}]} 1. 2. 数据预处理 数据清洗:使用工具(如BleachClean)去除噪声和无效数据。 分词对齐:通过ModelScope内置Tokenizer处理文本: from modelscope import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained...
基于llama3 进行测试,tokenizer 结果一致, 模型运行结果有区别,transformers输出的长度是 963, modelscope 输出的长度是 900 代码: import os import sys import transformers import torch from modelscope import AutoTokenizer, AutoModelForCausalLM from transformers import AutoTokenizer as HFAutoTokenizer from trans...
fromflaskimportFlask, requestfromtransformersimportAutoTokenizer, AutoModelForCausalLM model_dir ='/usr/src/app/gemma-2b-it'app = Flask(__name__) tokenizer = AutoTokenizer.from_pretrained(model_dir) model = AutoModelForCausalLM.from_pretrained(model_dir, device_map="auto")@app.route('/invoke...
frommodelscopeimportAutoModel,AutoTokenizer# 下载模型和分词器model=AutoModel.from_pretrained('modelscope/xxx-model')tokenizer=AutoTokenizer.from_pretrained('modelscope/xxx-tokenizer')# 输入数据text="Hello, ModelScope!"inputs=tokenizer(text,return_tensors="pt")# 进行推理outputs=model(**inputs)print...
tokenizer = AutoTokenizer.from_pretrained('./qwen/Qwen1.5-7B-Chat/', use_fast=False, trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained('./qwen/Qwen1.5-7B-Chat/', device_map="auto",torch_dtype=torch.bfloat16) model.enable_input_require_grads() # 开启梯度检查点时,要...
4 tokenizer = AutoTokenizer.from_pretrained(model_id) ---> 6 model = AutoModelForCausalLM.from_pretrained(model_id, device_map='auto') File /opt/conda/lib/python3.8/site-packages/modelscope/utils/hf_util.py:111, in get_wrapped_class..ClassWrapper.from_pretrained(cls, pretrained_model_name...
revision='v1.0.0') tokenizer = AutoTokenizer.from_pretrained( 'AI-ModelScope/bert-base-uncased', revision='v1.0.0') lora_config = LoRAConfig(target_modules=['query', 'key', 'value']) model = Swift.from_pretrained(model, model_id='./outputs/checkpoint-21') print(model(**tokenizer('...