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_message, tokenizer) input_ids...
max_length=model.config.n_positionsstride=512seq_len=encodings.input_ids.size(1)nlls=[]prev_end_loc=0forbegin_locintqdm(range(0,seq_len,stride)):end_loc=min(begin_loc+max_length,seq_len)trg_len=end_loc-prev_end_loc# may be different from stride on last loopinput_ids=encodings....
swift sft --model_id_or_path qwen/Qwen-7B-Chat-Int4 --model_revision master --sft_type lora --tuner_backend swift --template_type qwen --dtype fp16 --output_dir output --dataset leetcode-python-en --train_dataset_sample -1 --num_train_epochs 1 --max_length 512 --check_dataset_...
def bert_embedding(text,modelName="bert-base-chinese"):fromtransformersimportBertModel,BertTokenizer 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)e...
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 \ ...
)classDistillationTrainer(Trainer):def__init__(self, teacher_model, *args, **kwargs): super().__init__(*args, **kwargs) self.teacher_model=teacher_modeldefcompute_loss(self, model, inputs, return_outputs=False): outputs= model(**inputs) ...
| 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 |...
gpt4-mini \ --train_dataset_sample 1000 \ --logging_steps 5 \ --max_length 4096 \ --learning_rate 5e-5 \ --warmup_ratio 0.4 \ --output_dir output \ --lora_target_modules ALL \ --self_cognition_sample 500 \ --model_name 小黄 'Xiao Huang' \ --model_author 魔搭 ModelScope ...
Model Type [LoRA] Max Length Training Speed (samples/s) GPU Memory (GiB) Model Type [FULL] Max Length Training Speed (samples/s) GPU Memory (GiB) 缺点 相比RAG,输出可解释性不强 存在幻觉问题 在精确问答场景上可能会产出非专业结果(如法律行业) ...