deepseek-chat模型LorA微调完没有adapter_config.json

[HolyCrazy]

deepseek-chat模型LorA微调完没有adapter_config.json，看其他issue里说，是因为transformer的版本问题，LorA微调出来的参数和基座模型的参数直接合并了，但是直接运行Lora微调出来的参数，模型回复的所有token都是0～，感觉是没有进行推理～

[KMnO4-zx]

可以是尝试一下 transformers版本为 4.31.3

[HolyCrazy]

transformers 4.31.3没有～用了transformers==4.31.0～但还是不行～感觉是不是我脚本写的有问题～

MAX_LENGTH = 384 # Llama分词器会将一个中文字切分为多个token，因此需要放开一些最大长度，保证数据的完整性 path = '/mnt/bn/models/deepseek-coder-6.7b-instruct' tokenizer = AutoTokenizer.from_pretrained(path, use_fast=False, trust_remote_code=True) tokenizer.padding_side = 'right' # padding在右边

model = AutoModelForCausalLM.from_pretrained(path, trust_remote_code=True, torch_dtype=torch.half, device_map="auto") model.generation_config = GenerationConfig.from_pretrained(path) model.generation_config.pad_token_id = model.generation_config.eos_token_id

def process_func(example): input_ids, attention_mask, labels = [], [], [] instruction = tokenizer(f"User: {example['instruction']+example['input']}\n\n", add_special_tokens=False) # add_special_tokens 不在开头加 special_tokens response = tokenizer(f"Assistant: {example['output']}<｜end▁of▁sentence｜>", add_special_tokens=False) input_ids = instruction["input_ids"] + response["input_ids"] + [tokenizer.pad_token_id] attention_mask = instruction["attention_mask"] + response["attention_mask"] + [1] # 因为eos token咱们也是要关注的所以 补充为1 labels = [-100] * len(instruction["input_ids"]) + response["input_ids"] + [tokenizer.pad_token_id]
if len(input_ids) > MAX_LENGTH: # 做一个截断 input_ids = input_ids[:MAX_LENGTH] attention_mask = attention_mask[:MAX_LENGTH] labels = labels[:MAX_LENGTH] return { "input_ids": input_ids, "attention_mask": attention_mask, "labels": labels }

dataset = load_dataset("json", data_files={"/zhen_huan_dataset_1000.json"}, split = 'train')

processed_dataset = dataset.map( process_func, remove_columns=["instruction", "input", "output"] ) print(processed_dataset)

config = LoraConfig( task_type=TaskType.CAUSAL_LM, target_modules=["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"], inference_mode=False, # 训练模式 r=8, # Lora 秩 lora_alpha=32, # Lora alaph，具体作用参见 Lora 原理 lora_dropout=0.1# Dropout 比例 )

output_dir="/code/lora"

args = TrainingArguments( output_dir=output_dir, per_device_train_batch_size=8, gradient_accumulation_steps=2, logging_steps=10, num_train_epochs=3, save_steps=100, learning_rate=1e-4, save_on_each_node=True, gradient_checkpointing=True )

trainer = Trainer( model=model, args=args, train_dataset=processed_dataset, data_collator=DataCollatorForSeq2Seq(tokenizer=tokenizer, padding=True), ) trainer.train() print("train finish___")