一种解决方案是使用语言特定的预分词其,比如XLM使用一种特殊的中文、日文、泰文预分词器。为了更加通用地解决这个问题,SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing (Kudo et al., 2018)一文将输入看作原始的输入流,将空格本身也囊括在符号集合中,...
tokenizer._tokenizer.post_processor = BertProcessing( ("", tokenizer.token_to_id("")), ("", tokenizer.token_to_id("")), ) tokenizer.enable_truncation(max_length=512) # Test the tokenizer tokenizer.encode("knit midi dress with vneckline straps.") # Show the tokens created tokenizer.enco...
model_tokenizer:模型tokenizer,由平台从上传的模型中加载; messages:用户调用会话模型服务时传入的会话信息,当为会话模式时生效; kwargs:其他参数,目前未使用。当未来功能升级时,做向前兼容使用。 函数输出结果: token_ids: 转换后的一维token id数组,将用于喂入模型 样例: chatglm3 6b: # !/usr/bin/env py...
执行转换流程如下：首先获取 LLaMA 权重并将其转为 Hugging Face Transformers 模型格式。使用 transformers 提供的脚本 convert_llama_weights_to_hf.py，将原版 LLaMA 模型转换为 Hugging Face 格式。在转换过程中，需要将 Tokenizer、model、generation 等文件放在指定目录，并将其他文件放置于相应目录下。...
workshoptransformerspytorchdatasetsdlhftokenizers UpdatedNov 30, 2024 VNA software with an Arduino arduinoencoderamateur-radioantennavnahfad9851amateurradioad9850antenna-swr-analyzerantenna-analyzersga3386ad8302 UpdatedApr 25, 2018 Arduino sh123/nano_power_meter ...
tokenizer=tokenizer, num_total_iters=num_total_iters, args=args) trainer = DeepSpeedPPOTrainer(engine=engine, args=args) for prompt_batch in prompt_train_dataloader: out = trainer.generate_experience(prompt_batch) actor_loss, critic_loss = trainer.train_rlhf(out) ...
base_model_name)train_dataset = ... # make sure to have columns "input_ids"eval_dataset = ...trainer = RLOOTrainer( config=RLOOConfig( per_device_train_batch_size=1, gradient_accumulation_steps=64, total_episodes=30000, ), tokenizer=tokenizer, policy=policy, ref_...
tokenizer=tokenizer, num_total_iters=num_total_iters, args=args) trainer = DeepSpeedPPOTrainer(engine=engine, args=args)forprompt_batchinprompt_train_dataloader: out = trainer.generate_experience(prompt_batch) actor_loss, critic_loss = trainer.train_rlhf(out) ...
encoding = self.tokenizer(title1, title2, max_length=self.max_len, padding='max_length', truncation=True, return_tensors='pt') return { 'input_ids': encoding['input_ids'].flatten(), 'attention_mask': encoding['attention_mask'].flatten(), ...
tokenizer=tokenizer, num_total_iters=num_total_iters, args=args) rlhf_engine中会包含4个模型对象:self.actor: sft模型,可训练,作为策略模型;self.ref: sft模型,不可训练,只做前向推断,用于约束self.actor生成结果的向量分布;self.critic:reward模型,可训练,价值模型,用于对生成的每个动作打分;self.reward:re...