input_ids += content_ids input_ids = input_ids[:tokenizer.model_max_length] labels = labels[:tokenizer.model_max_length] trunc_id = last_index(labels, IGNORE_TOKEN_ID) + 1 input_ids = input_ids[:trunc_id] labels = labels[:trunc_id] if len(labels) == 0: return tokenize(dummy_me...
input_ids=input_ids[:tokenizer.model_max_length]labels=labels[:tokenizer.model_max_length]trunc_id=last_index(labels,IGNORE_TOKEN_ID)+1input_ids=input_ids[:trunc_id]labels=labels[:trunc_id]iflen(labels)==0:returntokenize(dummy_message,tokenizer)input_ids=safe_ids(input_ids,tokenizer.vocab_s...
--gradient_accumulation_steps 8 \ --model_max_length 2048 \ --output_dir './hf_logs' \ --overwrite_output_dir \ --gradient_checkpointing \ --ddp_find_unused_parameters False 运行结果如下所示: *** train metrics *** epoch = 0.99 train_loss = 7.1505 train_runtime = 0:19:08.39 trai...
py \ --model_type qwen-7b-chat \ --dataset ms-agent \ --train_dataset_mix_ratio 2.0 \ --batch_size 1 \ --max_length 2048 \ --use_loss_scale True \ --gradient_accumulation_steps 16 \ --learning_rate 5e-05 \ --use_flash_attn True \ --eval_steps 2000 \ --save_steps 2000...
--model_revision master \ --sft_type lora \ --tuner_backend peft \ --template_type AUTO \ --dtype bf16 \ --output_dir output \ --dataset leetcode-python-en \ --train_dataset_sample -1 \ --num_train_epochs 1 \ --max_length 2048 \ ...
--model_revision master \ --sft_type lora \ --tuner_backend peft \ --template_type AUTO \ --dtype bf16 \ --output_dir output \ --dataset leetcode-python-en \ --train_dataset_sample -1\ --num_train_epochs1\ --max_length2048\ ...
old_output = old_model.generate(old_input_ids, max_length=max_length) old_output_text = old_tokenizer.batch_decode(old_output)print('old_output:{}'.format(old_output_text)) # 使用新模型对文本编码 new_model = AutoModelForCausalLM.from_pretrained(new_model_name_or_path) ...
| Model Type [LoRA] | Max Length | Training Speed (samples/s) | GPU Memory (GiB) | |---|---|---|---| | qwen-1_8b-chat | 512 | 9.88 | 6.99 | | 1024 | 9.90 | 10.71 | | 2048 | 8.77 | 16.35 | | 4096 | 5.92 | 23.80 | | 8192 | 4.19 | 37.03 |...
tokenizer=BertTokenizer.from_pretrained(modelName)model=BertModel.from_pretrained(modelName)inputs=tokenizer(text,return_tensors="pt",padding=True,truncation=True,max_length=512)outputs=model(**inputs)embeddings=outputs.last_hidden_state[:,0,:].detach().numpy()returnembeddings# 插入数据def setData...
val_tokens = tokenizer.batch_encode_plus(x_val.tolist(), max_length = 250, pad_to_max_length=True, truncation=True) 分词器返回一个字典,其中包含三个键值对,其中包含 input_ids,它们是与特定单词相关的标记;token_type_ids,它是区分输入的不同段或部分的整数列表。Attention_mask 指示要关注哪个标记...