下面,我们就直接使用Tokenizer来进行分词: 代码语言:javascript 代码运行次数:0 运行 AI代码解释 from transformersimportBertTokenizer # 或者 AutoTokenizer tokenizer=BertTokenizer.from_pretrained("bert-base-cased") 代码语言:javascript 代码运行次数:0 运行 AI代码解释 s='today is a good day to learn transform...
"prompt=f'Question: {text.strip()}\n\nAnswer:'inputs=tokenizer(prompt,return_tensors="pt").to(0)output=model.generate(inputs["input_ids"],max_new_tokens=40)print(tokenizer.decode(output[0].tolist(),skip_special_tokens=True)) 输出: 代码语言:javascript 代码运行次数:0 运行 AI代码解释 ...
tokenizers replace lazy_static with stabilized std::sync::LazyLock in 1.80 (#1739) Mar 19, 2025 .gitignore Rvert main hiccup. May 16, 2023 CITATION.cff 0.13.4.rc1 (#1319) Aug 14, 2023 LICENSE Create LICENSE Jan 5, 2020 README.md ...
huggingface tokenizer本地化 以t5-base为例 本地存储: fromtransformersimportAutoTokenizer tokenizer= AutoTokenizer.from_pretrained('t5-base') tokenizer.save_pretrained('your_path') 本地加载: tokenizer = AutoTokenizer.from_pretrained('your_path')...
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 Mixture of Experts is in less than 100 words."}...
分词器文件: tokenizer.json、vocab.txt(根据模型类型可能不同) 特殊文件: special_tokens_map.json, tokenizer_config.json 将下载的文件拷贝到 model_folder 目录中 , 在 SentenceTransformer 构造函数中 , 指定本地目录 ; from sentence_transformers import SentenceTransformer ...
{role:"user",content:"Hello, how are you?"},{role:"assistant",content:"I'm doing great. How can I help you today?"},{role:"user",content:"I'd like to show off how chat templating works!"},];consttext=tokenizer.apply_chat_template(chat,{tokenize:false});// "<s>[INST] Hello...
text_inputs, ...vision_inputs, max_new_tokens: 100, }); // Decode generated text const generated_text = tokenizer.batch_decode(generated_ids, { skip_special_tokens: false, })[0]; // Post-process the generated text const result = processor.post_process_generation( generated_text, task...
tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name).to(device) 1. 2. 3. 4. 5. 6. 7. 8. Downloading: 0%| | 0.00/689 [00:00<?, ?B/s] Downloading: 0%| | 0.00/0.99M [00:00<?, ?B/s] ...
(right)<|fim_middle|>"""model_inputs = TOKENIZER([input_text], return_tensors="pt").to(device)# Use `max_new_tokens` to control the maximum output length.generated_ids = MODEL.generate(model_inputs.input_ids, max_new_tokens=512, do_sample=False)[0]# The generated_ids include ...