| from unsloth import FastLanguageModel, get_chat_template | |
| import torch | |
| # 加载模型并合并 LoRA | |
| model, tokenizer = FastLanguageModel.from_pretrained( | |
| model_name = "mistralai/Mistral-Small-3.1-24B-Base-2503", | |
| dtype = None, | |
| max_seq_length = 2048, | |
| load_in_4bit = True, | |
| ) | |
| model = FastLanguageModel.get_peft_model( | |
| model, | |
| r = 16, # Choose any number > 0 ! Suggested 8, 16, 32, 64, 128 | |
| target_modules = ["q_proj", "k_proj", "v_proj", "o_proj", | |
| "gate_proj", "up_proj", "down_proj",], | |
| lora_alpha = 16, | |
| lora_dropout = 0, # Supports any, but = 0 is optimized | |
| bias = "none", # Supports any, but = "none" is optimized | |
| # [NEW] "unsloth" uses 30% less VRAM, fits 2x larger batch sizes! | |
| use_gradient_checkpointing = "unsloth", # True or "unsloth" for very long context | |
| random_state = 3407, | |
| use_rslora = False, # We support rank stabilized LoRA | |
| loftq_config = None, # And LoftQ | |
| ) | |
| model.load_adapter("/home/outputs/checkpoint-4000","default") | |
| tokenizer = get_chat_template(tokenizer, chat_template="chatml", map_eos_token=True) | |
| print(type(model)) | |
| # 正确保存合并模型（使用 Unsloth 官方函数） | |
| model.save_pretrained_merged( | |
| save_directory = "/home/merged_model1", | |
| tokenizer = tokenizer, | |
| save_method = "merged_16bit", | |
| ) |