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1from transformers import AutoModelForCausalLM, AutoTokenizer,BitsAndBytesConfig
2
3peft_model_id = "checkpoint-2000"
4model = AutoModelForCausalLM.from_pretrained(peft_model_id,device_map="cuda")
5
6tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b")
7
8input_text = """
9Generate a title for the article:
10
11{content}
12
13---
14Title:
15""" # 固定格式
16encoding = tokenizer(input_text, return_tensors="pt").to("cuda")
17
18outputs = model.generate(**encoding,max_length=8192,temperature=0.2,do_sample=True)
19generated_ids = outputs[:, encoding.input_ids.shape[1]:]
20generated_texts = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
21print(generated_texts[0])
221CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
2 --stage sft \
3 --do_train True \
4 --model_name_or_path google/gemma-2b \
5 --finetuning_type lora \
6 --template default \
7 --dataset title \
8 --use_unsloth \
9 --cutoff_len 8192 \
10 --learning_rate 5e-05 \
11 --num_train_epochs 10.0 \
12 --max_samples 10000 \
13 --per_device_train_batch_size 4 \
14 --per_device_eval_batch_size 4 \
15 --gradient_accumulation_steps 4 \
16 --lr_scheduler_type cosine \
17 --max_grad_norm 1.0 \
18 --logging_steps 10 \
19 --save_steps 100 \
20 --eval_steps 100 \
21 --evaluation_strategy steps \
22 --warmup_steps 0 \
23 --output_dir saves/Gemma-2B/lora/train_2024-03-01-04-36-32 \
24 --bf16 True \
25 --lora_rank 8 \
26 --lora_dropout 0.1 \
27 --lora_target q_proj,v_proj \
28 --val_size 0.1 \
29 --load_best_model_at_end True \
30 --plot_loss True \
31 --report_to "tensorboard"