model = transformers.AutoModelForCausalLM.from_pretrained( model_args.model_name_or_path, cache_dir=training_args.cache_dir, ) tokenizer = transformers.AutoTokenizer.from_pretrained( model_args.model_name_or_path,cache_dir=training_args.cache_dir, model_max_length=training_args.model_max_length,...
vocab_size=32000, model_max_length=1000000000000000019884624838656, is_fast=True, padding_side='left', truncation_side='right', special_tokens={'bos_token': '', 'eos_token': '', 'unk_token': '<unk>
disable_tqdm=disable_tqdm, report_to="tensorboard", seed=42)# Create the trainertrainer = SFTTrainer( model=model, train_dataset=dataset, peft_config=peft_config, max_seq_length=max_seq_length, tokenizer=tokenizer, packing=packing, formatting_func=format_instruction, ...
sequences = pipeline ('I have tomatoes, basil and cheese at home. What can I cook for dinner?\n',do_sample=True,top_k=10,num_return_sequences=1,eos_token_id=tokenizer.eos_token_id,max_length=400,)for seq in sequences:print (f"{seq ['generated_text']}") 步骤4:运行 Llama 现在...
LlamaTokenizerFast(name_or_path='NousResearch/Llama-2-7b-hf', vocab_size=32000, model_max_length=1000000000000000019884624838656, is_fast=True, padding_side='left', truncation_side='right', special_tokens={'bos_token': '', 'eos_token': '', 'unk_token': '<unk>', 'pad_token': '<...
eos_token_id=tokenizer.eos_token_id,max_length=400, ) for seq in sequences: print (f"{seq ['generated_text']}") 步骤4:运行 Llama 现在,这个脚本已经可以运行了。保存脚本,回到 Conda 环境,输入 python < 脚本名称 >.py 并按回车键来运行脚本。
importtensorflowastf# 定义Llama模型defllama_model(input_shape,num_classes):model=tf.keras.Sequential()model.add(tf.keras.layers.Embedding(input_dim=vocab_size,output_dim=embedding_dim,input_length=max_length))model.add(tf.keras.layers.Conv1D(128,5,activation='relu'))model.add(tf.keras.layers....
pipeline_task = pipeline("text_generation", model='llama_7b', max_length=20) pipeline_result = pipeline_task("I love Beijing, because", top_k=3) print(pipeline_result) - 当我输入提示词: text_generation_text': I love Beijing, because ...
import torchfrom modelscope import snapshot_download, AutoModel, AutoTokenizerimport osmodel_dir = snapshot_download('LLM-Research/Meta-Llama-3-8B-Instruct', cache_dir='/root/autodl-tmp', revision='master')def process_func(example): MAX_LENGTH = 384 # Llama分词器会将一个中文字切分为...
Please, answer in pirate-speak."},]outputs = pipe( messages, max_new_tokens=256, do_sample=False,)assistant_response = outputs[]["generated_text"][-1]["content"]print(assistant_response)# Arrrr, me hearty! Yer lookin' fer a bit o' information about meself, eh? Alright then...