vocab_size=65000,model_max_length=1024, is_fast=False, padding_side='right', truncation_side='right', special_tokens={'bos_token': '', 'eos_token': '', 'unk_token': '<unk>', 'pad_token': ''}, clean_up_tokenization_spaces=False), added_tokens_decoder={ 0: AddedToken("...
"stage3_max_reuse_distance": 1e9, "stage3_gather_16bit_weights_on_model_save": true } 注意点 和ZeRO-2一样,如果不想启用某一个offload,直接把"device":"cpu"改成"device":"none" stage3_gather_16bit_weights_on_model_save设置为true时会对速度和显存/内存开销有很大的影响,但截至本文时间他必须...
--data_path "/data2/xinyuuliu/Baichuan2-main/fine-tune/data/全网评价总结训练数据.json" \ --model_name_or_path "/data1/xinyuuliu/Baichuan2-13B-Chat" \ --output_dir "output_lora_summary" \ --model_max_length 10000\ --num_train_epochs 10 \ --per_device_train_batch_size 4 \ --...
max_length=tokenizer.model_max_length, padding=padding, truncation=True)# Tokenize targets with the `text_target` keyword argumentlabels = tokenizer(text_target=sample[summary_column],
model_args = dict( dim=opt.dim, n_layers=opt.n_layers, n_heads=opt.n_heads, n_kv_heads=opt.n_heads, vocab_size=opt.vocab_size,#64793, multiple_of=opt.multiple_of, max_seq_len=opt.max_seq_len, dropout=opt.dropout, ) # model_args = get_model_args(opt) gptconf = ModelArgs...
DeepSpeed-Chat还有一个很严重的问题就是，在make experience的时候，强制Actor Model生成到最大长度（设置max_length=min_length=max_min_length），这样子导致模型生成偏差很大。对于一个简单的问题，模型可能本来生成简单的一句话就可以完美回答了，但是却必须强制生成到最大长度，这样训练的模型和我们实际用起来的模型...
() self.wte = model.transformer.wte self.wpe = model.transformer.wpe self.config = model.config self.drop = model.transformer.drop max_positions = self.config.max_position_embeddings self.register_buffer( "bias", torch.tril(torch.ones((max_positions, max_positions), dtype=torch.bool)), ...
keys())[0]]) # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can # customize this part to your needs. if total_length >= block_size: total_length = (total_length // block_size) * block_size # Split by chunks of max_len...
defpreprocess_function(sample, padding="max_length"): # created prompted input inputs = [prompt_template.format(input=item)foriteminsample[text_column]] # tokenize inputs model_inputs = tokenizer(inputs, max_length=tokenizer.model_max_length, padding=padding, truncation=True) ...
outputs = model.generate(inputs["input_ids"], max_length=100) # 解码输出 output_text = tokenizer.decode(outputs[0], skip_special_tokens=True) print(output_text) 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. ...