update

examples/tutorials/lora_unsloth/step_2_train_model.py

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+#!/usr/bin/python3
+# -*- coding: utf-8 -*-
+import argparse
+import os
+from pathlib import Path
+import platform
+
+if platform.system() in ("Windows", "Darwin"):
+    from project_settings import project_path
+else:
+    project_path = os.path.abspath("../../../")
+    project_path = Path(project_path)
+
+# from unsloth import FastLanguageModel
+# from trl import SFTTrainer, SFTConfig
+from datasets import load_dataset
+# import torch
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--model_name",
+        default="unsloth/Qwen3-8B-unsloth-bnb-4bit",
+        type=str
+    )
+    parser.add_argument(
+        "--dataset_path",
+        default="miyuki2026/tutorials",
+        type=str
+    ),
+    parser.add_argument("--dataset_name", default=None, type=str),
+    parser.add_argument("--dataset_split", default=None, type=str),
+    parser.add_argument(
+        "--dataset_cache_dir",
+        default=(project_path / "hub_datasets").as_posix(),
+        type=str
+    ),
+    parser.add_argument("--dataset_streaming", default=None, type=str),
+
+    parser.add_argument(
+        "--num_workers",
+        default=None if platform.system() == "Windows" else os.cpu_count() // 2,
+        type=str
+    ),
+    args = parser.parse_args()
+    return args
+
+
+def convert_to_qwen_format(example):
+    """
+
+    :param example: {"conversation_id": 612, "category": "", "conversation": [{"human": "", "assistant": ""}], "dataset": ""}
+    :return:
+    """
+    conversations = []
+    for conversation in example["conversation"]:
+        for turn in conversation:
+            conversations.append([
+                {"role": "user", "content": turn["human"].strip()},
+                {"role": "assistant", "content": turn["assistant"].strip()},
+            ])
+    result = {"conversations": conversations}
+    print(result)
+    exit(0)
+    return result
+
+
+def main():
+    args = get_args()
+
+    model, tokenizer = FastLanguageModel.from_pretrained(
+        model_name=args.model_name,
+        max_seq_length=2048,
+        device_map="auto",
+        dtype=None,
+        load_in_4bit=True,
+        load_in_8bit=False,
+        full_finetuning=False
+    )
+
+    # model = FastLanguageModel.get_peft_model(
+    #     model,
+    #     r=32,  # 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=32,  # Best to choose alpha = rank or rank*2
+    #     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,  # rank stabilized LoRA
+    #     loftq_config=None,  # LoftQ
+    # )
+    # print(model)
+
+    def format_func(example):
+        formatted_texts = []
+        for conv in example['conversations']:
+            formatted_texts.append(
+                tokenizer.apply_chat_template(
+                    conv,
+                    tokenize=False,  # 训练时部分词，true返回的是张量
+                    add_generation_prompt=False,  # 训练期间要关闭，如果是推理则设为True
+                )
+            )
+
+        return {"text": formatted_texts}
+
+    dataset_dict = load_dataset(
+        path=args.dataset_path,
+        name=args.dataset_name,
+        data_dir="keywords",
+        # data_dir="psychology",
+        split=args.dataset_split,
+        cache_dir=args.dataset_cache_dir,
+        # num_proc=args.num_workers if not args.dataset_streaming else None,
+        streaming=args.dataset_streaming,
+    )
+    print(dataset_dict)
+    train_dataset = dataset_dict["train"]
+
+    train_dataset = train_dataset.map(
+        convert_to_qwen_format,
+        batched=True,
+        remove_columns=train_dataset.column_names
+    )
+    print(train_dataset)
+
+    train_dataset = train_dataset.map(
+        format_func,
+        batched=True,
+        remove_columns=train_dataset.column_names
+    )
+    print(train_dataset)
+
+    return
+
+
+if __name__ == "__main__":
+    main()