update

examples/tutorials/lora_unsloth/step_2_train_model.py

@@ -41,6 +41,9 @@ def get_args():
     ),
     parser.add_argument("--dataset_streaming", default=None, type=str),
 
+    parser.add_argument("--valid_dataset_size", default=None, type=str),
+    parser.add_argument("--shuffle_buffer_size", default=None, type=str),
+
     parser.add_argument(
         "--num_workers",
         default=None if platform.system() == "Windows" else os.cpu_count() // 2,
@@ -97,8 +100,16 @@ def main():
         # num_proc=args.num_workers if not args.dataset_streaming else None,
         streaming=args.dataset_streaming,
     )
-    print(dataset_dict)
-    train_dataset = dataset_dict["train"]
+    dataset = dataset_dict["train"]
+
+    if args.dataset_streaming:
+        valid_dataset = dataset.take(args.valid_dataset_size)
+        train_dataset = dataset.skip(args.valid_dataset_size)
+        train_dataset = train_dataset.shuffle(buffer_size=args.shuffle_buffer_size, seed=None)
+    else:
+        dataset = dataset.train_test_split(test_size=args.valid_dataset_size, seed=None)
+        train_dataset = dataset["train"]
+        valid_dataset = dataset["test"]
 
     train_dataset = train_dataset.map(
         format_func,

examples/tutorials/lora_unsloth/step_4_evaluation.py

@@ -0,0 +1,149 @@
+#!/usr/bin/python3
+# -*- coding: utf-8 -*-
+import argparse
+import json
+import os
+from pathlib import Path
+import platform
+
+os.environ["UNSLOTH_USE_MODELSCOPE"] = "1"
+
+if platform.system() in ("Windows", "Darwin"):
+    from project_settings import project_path
+else:
+    project_path = os.path.abspath("../../../")
+    project_path = Path(project_path)
+
+from datasets import load_dataset
+from unsloth import FastLanguageModel
+from transformers import TextStreamer
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--model_name",
+        default="unsloth/Qwen3-8B-unsloth-bnb-4bit",
+        type=str
+    )
+    parser.add_argument(
+        "--lora_adapter_path",
+        default=(project_path / "trained_models" / "Qwen3-8B-sft-lora-adapter-unsloth").as_posix(),
+        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("--valid_dataset_size", default=None, type=str),
+    parser.add_argument("--shuffle_buffer_size", default=None, type=str),
+
+    parser.add_argument(
+        "--max_new_tokens",
+        default=1024, # 8192, 128
+        type=int, help="最大生成长度（注意：并非模型实际长文本能力）"
+    )
+    parser.add_argument("--top_p", default=0.85, type=float, help="nucleus采样阈值（0-1）")
+    parser.add_argument("--temperature", default=0.85, type=float, help="生成温度，控制随机性（0-1，越大越随机）")
+
+    parser.add_argument(
+        "--num_workers",
+        default=None if platform.system() == "Windows" else os.cpu_count() // 2,
+        type=str
+    )
+    parser.add_argument("--output_file", default="evaluation.jsonl", type=str),
+
+    args = parser.parse_args()
+    return args
+
+
+def main():
+    args = get_args()
+
+    output_file = Path(args.output_file)
+    output_file.parent.mkdir(parents=True, exist_ok=True)
+
+    model, tokenizer = FastLanguageModel.from_pretrained(
+        model_name=args.model_name,
+        max_seq_length=2048,  # 支持32K+长上下文
+        device_map="auto",
+        dtype=None,  # 自动选择最优精度
+        load_in_4bit=True,  # 4bit量化节省70%显存
+    )
+
+    # 2、注入lora适配器
+    model.load_adapter(args.lora_adapter_path)
+
+    # 启用unsloth推理加速
+    FastLanguageModel.for_inference(model)
+    model.eval()
+
+    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,
+    )
+    dataset = dataset_dict["train"]
+
+    if args.dataset_streaming:
+        valid_dataset = dataset.take(args.valid_dataset_size)
+        # train_dataset = dataset.skip(args.valid_dataset_size)
+        # train_dataset = train_dataset.shuffle(buffer_size=args.shuffle_buffer_size, seed=None)
+    else:
+        dataset = dataset.train_test_split(test_size=args.valid_dataset_size, seed=None)
+        # train_dataset = dataset["train"]
+        valid_dataset = dataset["test"]
+
+    with open(output_file.as_posix(), "w", encoding="utf-8") as f:
+        for example in valid_dataset:
+            conversation = example["conversation"]
+            prompt = conversation[:-1]
+            response = conversation[-1]["content"]
+
+            format_messages = tokenizer.apply_chat_template(
+                prompt,
+                tokenize=False,  # 训练时部分词，true返回的是张量
+                add_generation_prompt=True,  # 训练期间要关闭，如果是推理则设为True
+            )
+
+            # 4、调用tokenizer得到input
+            inputs = tokenizer(format_messages, return_tensors="pt").to(model.device)
+
+            # 5、调用model.generate()
+            generated_ids = model.generate(
+                **inputs,
+                max_new_tokens=args.max_new_tokens, do_sample=True,
+                pad_token_id=tokenizer.pad_token_id, eos_token_id=tokenizer.eos_token_id,
+                top_p=args.top_p, temperature=args.temperature, repetition_penalty=1.0,
+            )
+
+            response_: str = tokenizer.decode(generated_ids[0][len(inputs["input_ids"][0]):], skip_special_tokens=True)
+            response_ = response_.split("</thinking>")[-1].strip()
+
+            row = {
+                "prompt": prompt,
+                "response": response,
+                "response_": response_,
+            }
+            row = json.dumps(row, ensure_ascii=False)
+            f.write(f"{row}\n")
+    return
+
+
+if __name__ == "__main__":
+    main()