Merge 68c00c22b0 into dddedc6881

- 新增_resolve_path函数以支持绝对路径和相对路径解析
- 在init_model中改用_resolve_path处理load_from和save_dir路径
- 修正模型权重文件路径拼接方式，避免路径错误
- 保证模型加载时使用绝对路径，提高加载稳定性
- 使代码结构更清晰，增强路径处理的健壮性

.gitignore

@@ -4,3 +4,11 @@ __pycache__/
 out
 website/
 docs-minimind/
+minimind-3
+dataset/**
+!dataset/lm_dataset.py
+!dataset/__init__.py
+!dataset/dataset.md
+checkpoints/
+.venv/
+.uv-cache/

.python-version

@@ -0,0 +1 @@
+3.10

.vscode/launch.json

@@ -0,0 +1,227 @@
+{
+    // 使用 IntelliSense 了解可用属性。
+    // 悬停以查看现有属性的相关描述。
+    // 欲了解更多信息，请访问: https://go.microsoft.com/fwlink/?linkid=830387
+    //
+    // 使用说明：
+    // 1. 先执行 `uv sync` 安装依赖
+    // 2. 在 VS Code 中按 Ctrl+Shift+P → "Python: Select Interpreter"
+    //    选择 `./.venv/Scripts/python.exe` (Windows) 或 `./.venv/bin/python` (Linux/Mac)
+    // 3. 按 F5 或 Ctrl+Shift+D 选择对应配置启动调试
+    //
+    // 注：对应 README.md 中所有 `uv run python` / `uv run streamlit` 的单卡命令
+    //     多卡训练 (`torchrun`) 建议在终端直接执行，不在此列出
+    "version": "0.2.0",
+    "configurations": [
+        // ==================== 1. 推理与评测 ====================
+        {
+            "name": "推理: eval_llm (transformers格式)",
+            "type": "debugpy",
+            "request": "launch",
+            "program": "${workspaceFolder}/eval_llm.py",
+            "cwd": "${workspaceFolder}",
+            "console": "integratedTerminal",
+            "args": ["--load_from", "./minimind-3"]
+        },
+        {
+            "name": "推理: eval_llm (pytorch格式)",
+            "type": "debugpy",
+            "request": "launch",
+            "program": "${workspaceFolder}/eval_llm.py",
+            "cwd": "${workspaceFolder}",
+            "console": "integratedTerminal",
+            "args": ["--load_from", "./model", "--weight", "full_sft"]
+        },
+        {
+            "name": "推理: eval_llm (open_thinking)",
+            "type": "debugpy",
+            "request": "launch",
+            "program": "${workspaceFolder}/eval_llm.py",
+            "cwd": "${workspaceFolder}",
+            "console": "integratedTerminal",
+            "args": ["--load_from", "./minimind-3", "--open_thinking", "1"]
+        },
+        {
+            "name": "推理: eval_llm (RoPE外推)",
+            "type": "debugpy",
+            "request": "launch",
+            "program": "${workspaceFolder}/eval_llm.py",
+            "cwd": "${workspaceFolder}",
+            "console": "integratedTerminal",
+            "args": ["--weight", "full_sft", "--inference_rope_scaling"]
+        },
+        {
+            "name": "评测: eval_toolcall (full_sft)",
+            "type": "debugpy",
+            "request": "launch",
+            "program": "${workspaceFolder}/scripts/eval_toolcall.py",
+            "cwd": "${workspaceFolder}",
+            "console": "integratedTerminal",
+            "args": ["--weight", "full_sft"]
+        },
+        {
+            "name": "评测: eval_toolcall (agent)",
+            "type": "debugpy",
+            "request": "launch",
+            "program": "${workspaceFolder}/scripts/eval_toolcall.py",
+            "cwd": "${workspaceFolder}",
+            "console": "integratedTerminal",
+            "args": ["--weight", "agent"]
+        },
+
+        // ==================== 2. WebUI 与服务 ====================
+        {
+            "name": "服务: WebUI (streamlit)",
+            "type": "debugpy",
+            "request": "launch",
+            "module": "streamlit",
+            "cwd": "${workspaceFolder}/scripts",
+            "console": "integratedTerminal",
+            "args": ["run", "web_demo.py"]
+        },
+        {
+            "name": "服务: OpenAI API Server",
+            "type": "debugpy",
+            "request": "launch",
+            "program": "${workspaceFolder}/scripts/serve_openai_api.py",
+            "cwd": "${workspaceFolder}/scripts",
+            "console": "integratedTerminal"
+        },
+        {
+            "name": "服务: Chat API 测试",
+            "type": "debugpy",
+            "request": "launch",
+            "program": "${workspaceFolder}/scripts/chat_api.py",
+            "cwd": "${workspaceFolder}/scripts",
+            "console": "integratedTerminal"
+        },
+
+        // ==================== 3. 预训练与微调 ====================
+        {
+            "name": "训练: Pretrain (预训练)",
+            "type": "debugpy",
+            "request": "launch",
+            "program": "${workspaceFolder}/trainer/train_pretrain.py",
+            "cwd": "${workspaceFolder}/trainer",
+            "console": "integratedTerminal"
+        },
+        {
+            "name": "训练: Pretrain (断点续训)",
+            "type": "debugpy",
+            "request": "launch",
+            "program": "${workspaceFolder}/trainer/train_pretrain.py",
+            "cwd": "${workspaceFolder}/trainer",
+            "console": "integratedTerminal",
+            "args": ["--from_resume", "1"]
+        },
+        {
+            "name": "训练: Full SFT (全参数微调)",
+            "type": "debugpy",
+            "request": "launch",
+            "program": "${workspaceFolder}/trainer/train_full_sft.py",
+            "cwd": "${workspaceFolder}/trainer",
+            "console": "integratedTerminal"
+        },
+        {
+            "name": "训练: Full SFT (断点续训)",
+            "type": "debugpy",
+            "request": "launch",
+            "program": "${workspaceFolder}/trainer/train_full_sft.py",
+            "cwd": "${workspaceFolder}/trainer",
+            "console": "integratedTerminal",
+            "args": ["--from_resume", "1"]
+        },
+        {
+            "name": "训练: LoRA",
+            "type": "debugpy",
+            "request": "launch",
+            "program": "${workspaceFolder}/trainer/train_lora.py",
+            "cwd": "${workspaceFolder}/trainer",
+            "console": "integratedTerminal"
+        },
+        {
+            "name": "训练: Distillation (知识蒸馏)",
+            "type": "debugpy",
+            "request": "launch",
+            "program": "${workspaceFolder}/trainer/train_distillation.py",
+            "cwd": "${workspaceFolder}/trainer",
+            "console": "integratedTerminal"
+        },
+        {
+            "name": "训练: Tokenizer (分词器训练)",
+            "type": "debugpy",
+            "request": "launch",
+            "program": "${workspaceFolder}/trainer/train_tokenizer.py",
+            "cwd": "${workspaceFolder}/trainer",
+            "console": "integratedTerminal"
+        },
+
+        // ==================== 4. 强化学习 ====================
+        {
+            "name": "RL: DPO",
+            "type": "debugpy",
+            "request": "launch",
+            "program": "${workspaceFolder}/trainer/train_dpo.py",
+            "cwd": "${workspaceFolder}/trainer",
+            "console": "integratedTerminal"
+        },
+        {
+            "name": "RL: PPO",
+            "type": "debugpy",
+            "request": "launch",
+            "program": "${workspaceFolder}/trainer/train_ppo.py",
+            "cwd": "${workspaceFolder}/trainer",
+            "console": "integratedTerminal"
+        },
+        {
+            "name": "RL: GRPO",
+            "type": "debugpy",
+            "request": "launch",
+            "program": "${workspaceFolder}/trainer/train_grpo.py",
+            "cwd": "${workspaceFolder}/trainer",
+            "console": "integratedTerminal"
+        },
+        {
+            "name": "RL: CISPO (GRPO变体)",
+            "type": "debugpy",
+            "request": "launch",
+            "program": "${workspaceFolder}/trainer/train_grpo.py",
+            "cwd": "${workspaceFolder}/trainer",
+            "console": "integratedTerminal",
+            "args": ["--loss_type", "cispo"]
+        },
+        {
+            "name": "RL: Agentic RL",
+            "type": "debugpy",
+            "request": "launch",
+            "program": "${workspaceFolder}/trainer/train_agent.py",
+            "cwd": "${workspaceFolder}/trainer",
+            "console": "integratedTerminal"
+        },
+        {
+            "name": "RL: Agentic RL (SGLang)",
+            "type": "debugpy",
+            "request": "launch",
+            "program": "${workspaceFolder}/trainer/train_agent.py",
+            "cwd": "${workspaceFolder}/trainer",
+            "console": "integratedTerminal",
+            "args": [
+                "--rollout_engine", "sglang",
+                "--sglang_base_url", "http://localhost:8998",
+                "--sglang_shared_path", "./ckpt_mm",
+                "--data_path", "../dataset/agent_rl_math.jsonl",
+                "--use_wandb"
+            ]
+        },
+
+        // ==================== 5. 工具与转换 ====================
+        {
+            "name": "工具: 模型转换",
+            "type": "debugpy",
+            "request": "launch",
+            "program": "${workspaceFolder}/scripts/convert_model.py",
+            "cwd": "${workspaceFolder}/scripts",
+            "console": "integratedTerminal"
+        }
+    ]
+}

README.md

@@ -220,7 +220,7 @@ minimind2系列旧模型均经过权重映射+（微调训练）QKVO线性层校
 * Ubuntu==20.04
 * CUDA==12.2
 * Python==3.10.16
-* [requirements.txt](./requirements.txt)
+* [uv](https://docs.astral.sh/uv/) + [pyproject.toml](./pyproject.toml)
 
 </details>
 
@@ -229,7 +229,8 @@ minimind2系列旧模型均经过权重映射+（微调训练）QKVO线性层校
 ```bash
 # 克隆仓库、安装依赖
 git clone --depth 1 https://github.com/jingyaogong/minimind
-cd minimind && pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple
+cd minimind
+uv sync
 ```
 
 ## Ⅰ 🚀 模型推理
@@ -239,7 +240,7 @@ cd minimind && pip install -r requirements.txt -i https://mirrors.aliyun.com/pyp
 在项目根目录：
 ```bash
 # 方式1
-modelscope download --model gongjy/minimind-3 --local_dir ./minimind-3
+uv run modelscope download --model gongjy/minimind-3 --local_dir ./minimind-3
 # 方式2
 git clone https://huggingface.co/jingyaogong/minimind-3
 ```
@@ -248,17 +249,17 @@ git clone https://huggingface.co/jingyaogong/minimind-3
 
 ```bash
 # 方式1：使用 Transformers 格式模型
-python eval_llm.py --load_from ./minimind-3
+uv run python eval_llm.py --load_from ./minimind-3
 # 方式2：基于 PyTorch 模型（确保./out目录下有对应权重）
-python eval_llm.py --load_from ./model --weight full_sft
+uv run python eval_llm.py --load_from ./model --weight full_sft
 ```
 
 ### 3'（可选）WebUI
 
 ```bash
-# 可能需要`python>=3.10`，安装 `pip install streamlit`
+# 使用 `uv sync` 安装依赖后即可运行
 # ⚠️ 须先将 transformers 格式模型文件夹复制到 ./scripts/ 目录下（例如：cp -r minimind-3 ./scripts/minimind-3），web_demo 脚本会自动扫描该目录下包含权重文件的子文件夹，如不存在则报错
-cd scripts && streamlit run web_demo.py
+cd scripts && uv run streamlit run web_demo.py
 ```
 
 ### 4'（可选）第三方推理框架
@@ -282,7 +283,7 @@ print(torch.cuda.is_available())
 
 若你计划使用 CUDA 训练，建议先确认当前环境是否已正确识别 GPU。  
 若 `cuda` 不可用，也仍可根据自身设备选择 `CPU` 或 `MPS` 运行，但训练速度与兼容性会有非常大的差异。  
-如需安装或更换 PyTorch 版本，可参考 [torch_stable](https://download.pytorch.org/whl/torch_stable.html) 与[链接](https://blog.csdn.net/weixin_45456738/article/details/141029610?ops_request_misc=&request_id=&biz_id=102&utm_term=%E5%AE%89%E8%A3%85torch&utm_medium=distribute.pc_search_result.none-task-blog-2~all~sobaiduweb~default-2-141029610.nonecase&spm=1018.2226.3001.4187)
+默认 `pyproject.toml` 使用 PyTorch 官方 `cu128` wheel，可覆盖 Windows 11 + RTX 50 系显卡的常见 CUDA 运行环境；如需切换 CPU 或其它 CUDA 版本，请调整 `pyproject.toml` 中的 `pytorch-cu128` 索引与 `torch` / `torchvision` 版本后重新执行 `uv sync`。
 
 </details>
 
@@ -291,7 +292,11 @@ print(torch.cuda.is_available())
 从下文提供的[数据集下载链接](https://www.modelscope.cn/datasets/gongjy/minimind_dataset/files) 下载所需数据文件，并放入 `./dataset` 目录
 
 > 当前默认仅需下载 `pretrain_t2t_mini.jsonl` 与 `sft_t2t_mini.jsonl`，即可较快复现 `MiniMind Zero` 对话模型。
-如有更多需求，下文提供多种搭配方案，可根据自身任务目标与 GPU 资源灵活选择。
+
+```bash
+// （可选）如有更多需求，下文提供多种搭配方案，可根据自身任务目标与 GPU 资源灵活选择。
+uv run modelscope download --dataset gongjy/minimind_dataset --local_dir ./dataset
+```
 
 ### 2' 开始训练
 
@@ -301,8 +306,8 @@ print(torch.cuda.is_available())
 所有训练脚本均支持检查点保存。添加 `--from_resume 1` 参数后，即可自动检测并恢复训练进度：
 
 ```bash
-python train_pretrain.py --from_resume 1
+uv run python train_pretrain.py --from_resume 1
-python train_full_sft.py --from_resume 1
+uv run python train_full_sft.py --from_resume 1
 # ...
 ```
 
@@ -319,7 +324,7 @@ python train_full_sft.py --from_resume 1
 #### 2.1 预训练（必须）
 
 ```bash
-cd trainer && python train_pretrain.py
+cd trainer && uv run python train_pretrain.py
 ```
 
 > 训练后，将得到 `out/pretrain_*.pth` 作为输出权重（其中 `*` 为模型 dimension，默认为 `768`）
@@ -327,17 +332,22 @@ cd trainer && python train_pretrain.py
 #### 2.2 指令微调（必须）
 
 ```bash
-cd trainer && python train_full_sft.py
+cd trainer && uv run python train_full_sft.py
 ```
 
 > 训练后，将得到 `out/full_sft_*.pth` 作为输出权重（其中 `full` 表示全参数微调）
 
 #### 2.3 测试已训练模型（可选）
 
-确保待测试的模型 `*.pth` 文件位于 `./out/` 目录下；也可直接前往[此处](https://www.modelscope.cn/models/gongjy/minimind-3-pytorch/files)下载我已训练好的 `*.pth` 权重。
+确保待测试的模型 `*.pth` 文件位于 `./out/` 目录下；也可直接前往[此处](https://www.modelscope.cn/models/gongjy/minimind-3-pytorch/files)下载我已训练好的 `*.pth` 权重
 
 ```bash
-python eval_llm.py --weight full_sft
+// （可选） 下载已经训练好的模型
+uv run modelscope download --model gongjy/minimind-3-pytorch --local_dir ./out    
+```
+
+```bash
+uv run python eval_llm.py --weight full_sft
 ```
 
 > `--weight` 用于指定权重名称前缀，例如 `pretrain`、`full_sft` 等；更多参数可直接参考 `eval_llm.py`
@@ -350,7 +360,7 @@ python eval_llm.py --weight full_sft
 2、若你的设备有 `N (N > 1)` 张显卡，可通过以下方式启动单机 `N` 卡训练（DDP，也支持扩展到多机多卡）：
 
 ```bash
-torchrun --nproc_per_node N train_xxx.py
+uv run torchrun --nproc_per_node N train_xxx.py
 ```
 
 3、可根据需要开启 wandb 记录训练过程。

README_en.md

@@ -219,7 +219,7 @@ After this update, maintenance for the entire `minimind-v1` series will be disco
 * Ubuntu==20.04
 * CUDA==12.2
 * Python==3.10.16
-* [requirements.txt](./requirements.txt)
+* [uv](https://docs.astral.sh/uv/) + [pyproject.toml](./pyproject.toml)
 
 </details>
 
@@ -228,7 +228,8 @@ After this update, maintenance for the entire `minimind-v1` series will be disco
 ```bash
 # Clone repository and install dependencies
 git clone --depth 1 https://github.com/jingyaogong/minimind
-cd minimind && pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple
+cd minimind
+uv sync
 ```
 
 ## Ⅰ 🚀 Model Inference
@@ -238,7 +239,7 @@ cd minimind && pip install -r requirements.txt -i https://mirrors.aliyun.com/pyp
 In the project root directory:
 ```bash
 # Method 1
-modelscope download --model gongjy/minimind-3 --local_dir ./minimind-3
+uv run modelscope download --model gongjy/minimind-3 --local_dir ./minimind-3
 # Method 2
 git clone https://huggingface.co/jingyaogong/minimind-3
 ```
@@ -247,17 +248,17 @@ git clone https://huggingface.co/jingyaogong/minimind-3
 
 ```bash
 # Method 1: Using Transformers format model
-python eval_llm.py --load_from ./minimind-3
+uv run python eval_llm.py --load_from ./minimind-3
 # Method 2: Based on PyTorch model (ensure corresponding weights are in the ./out directory)
-python eval_llm.py --load_from ./model --weight full_sft
+uv run python eval_llm.py --load_from ./model --weight full_sft
 ```
 
 ### 3' (Optional) WebUI
 
 ```bash
-# May need `python>=3.10`, install `pip install streamlit`
+# Run this after installing dependencies with `uv sync`
 # ⚠️ You must first copy the transformers-format model folder into ./scripts/ (e.g.: cp -r minimind-3 ./scripts/minimind-3). The web_demo script will auto-scan subdirectories containing weight files; it will throw an error if none are found.
-cd scripts && streamlit run web_demo.py
+cd scripts && uv run streamlit run web_demo.py
 ```
 
 ### 4' (Optional) Third-party Inference Frameworks
@@ -281,7 +282,7 @@ print(torch.cuda.is_available())
 
 If you plan to use CUDA for training, it is recommended to first confirm whether the current environment has correctly recognized the GPU.  
 If `cuda` is not available, you can still choose `CPU` or `MPS` to run based on your device, but training speed and compatibility will differ significantly.  
-If you need to install or switch PyTorch versions, refer to [torch_stable](https://download.pytorch.org/whl/torch_stable.html) and [this link](https://blog.csdn.net/weixin_45456738/article/details/141029610?ops_request_misc=&request_id=&biz_id=102&utm_term=%E5%AE%89%E8%A3%85torch&utm_medium=distribute.pc_search_result.none-task-blog-2~all~sobaiduweb~default-2-141029610.nonecase&spm=1018.2226.3001.4187)
+By default, `pyproject.toml` uses the official PyTorch `cu128` wheels, which cover common CUDA runtime setups for Windows 11 + RTX 50-series GPUs. To switch to CPU or another CUDA version, adjust the `pytorch-cu128` index and the `torch` / `torchvision` versions in `pyproject.toml`, then run `uv sync` again.
 
 </details>
 
@@ -300,8 +301,8 @@ For other use cases, several data combinations are provided below and can be sel
 All training scripts support checkpoint saving. After adding the `--from_resume 1` parameter, the script will automatically detect and resume training progress:
 
 ```bash
-python train_pretrain.py --from_resume 1
+uv run python train_pretrain.py --from_resume 1
-python train_full_sft.py --from_resume 1
+uv run python train_full_sft.py --from_resume 1
 # ...
 ```
 
@@ -318,7 +319,7 @@ python train_full_sft.py --from_resume 1
 #### 2.1 Pretraining (Required)
 
 ```bash
-cd trainer && python train_pretrain.py
+cd trainer && uv run python train_pretrain.py
 ```
 
 > After training, `out/pretrain_*.pth` will be produced as output weights (where `*` is the model dimension, default `768`)
@@ -326,7 +327,7 @@ cd trainer && python train_pretrain.py
 #### 2.2 Instruction Fine-tuning (Required)
 
 ```bash
-cd trainer && python train_full_sft.py
+cd trainer && uv run python train_full_sft.py
 ```
 
 > After training, `out/full_sft_*.pth` will be produced as output weights (where `full` indicates full-parameter fine-tuning)
@@ -336,7 +337,7 @@ cd trainer && python train_full_sft.py
 Ensure the model `*.pth` files to be tested are located in the `./out/` directory; you can also go directly to [here](https://www.modelscope.cn/models/gongjy/minimind-3-pytorch/files) to download my pre-trained `*.pth` weights.
 
 ```bash
-python eval_llm.py --weight full_sft
+uv run python eval_llm.py --weight full_sft
 ```
 
 > `--weight` is used to specify the weight name prefix, such as `pretrain`, `full_sft`, etc.; for more parameters, refer directly to `eval_llm.py`
@@ -349,7 +350,7 @@ python eval_llm.py --weight full_sft
 2. If your device has `N (N > 1)` GPUs, you can start single-machine `N`-GPU training as follows (DDP, also supports extension to multi-machine multi-GPU):
 
 ```bash
-torchrun --nproc_per_node N train_xxx.py
+uv run torchrun --nproc_per_node N train_xxx.py
 ```
 
 3. You can enable wandb to record the training process as needed.

pyproject.toml

@@ -0,0 +1,62 @@
+[project]
+name = "minimind"
+version = "0.1.0"
+description = "MiniMind training and inference scripts."
+readme = "README.md"
+requires-python = ">=3.10,<3.11"
+dependencies = [
+    "datasets==3.6.0",
+    "datasketch==1.6.4",
+    "einops==0.8.1",
+    "fastapi==0.115.14",
+    "flask==3.0.3",
+    "flask-cors==4.0.0",
+    "jieba==0.42.1",
+    "jinja2==3.1.2",
+    "jsonlines==4.0.0",
+    "marshmallow==3.22.0",
+    "modelscope>=1.36.3",
+    "ngrok==1.4.0",
+    "nltk==3.8",
+    "numpy==1.26.4",
+    "openai==1.59.6",
+    "psutil==5.9.8",
+    "pydantic==2.11.5",
+    "requests==2.32.5",
+    "rich==13.7.1",
+    "scikit-learn==1.5.1",
+    "sentence-transformers==2.3.1",
+    "setuptools>=82.0.1",
+    "simhash==2.1.2",
+    "streamlit==1.50.0",
+    "swanlab==0.7.11",
+    "tiktoken==0.10.0",
+    "tokenizers==0.22.1",
+    "torch==2.10.0",
+    "torchvision==0.25.0",
+    "transformers==4.57.6",
+    "trl==0.13.0",
+    "ujson==5.1.0",
+    "uvicorn==0.35.0",
+    "wandb==0.18.3",
+]
+
+[tool.uv]
+package = false
+
+[tool.uv.sources]
+torch = [
+    { index = "pytorch-cu128", marker = "sys_platform == 'win32' or sys_platform == 'linux'" },
+]
+torchvision = [
+    { index = "pytorch-cu128", marker = "sys_platform == 'win32' or sys_platform == 'linux'" },
+]
+
+[[tool.uv.index]]
+url = "https://pypi.tuna.tsinghua.edu.cn/simple"
+default = true
+
+[[tool.uv.index]]
+name = "pytorch-cu128"
+url = "https://download.pytorch.org/whl/cu128"
+explicit = true

requirements.txt

@@ -1,32 +0,0 @@
-datasets==3.6.0
-datasketch==1.6.4
-Flask==3.0.3
-Flask_Cors==4.0.0
-jieba==0.42.1
-jsonlines==4.0.0
-marshmallow==3.22.0
-# matplotlib==3.10.0
-ngrok==1.4.0
-nltk==3.8
-numpy==1.26.4
-openai==1.59.6
-# peft==0.7.1
-psutil==5.9.8
-pydantic==2.11.5
-rich==13.7.1
-scikit_learn==1.5.1
-sentence_transformers==2.3.1
-simhash==2.1.2
-tiktoken==0.10.0
-transformers==4.57.6
-jinja2==3.1.2
-jsonlines==4.0.0
-trl==0.13.0
-ujson==5.1.0
-wandb==0.18.3
-streamlit==1.50.0
-einops==0.8.1
-swanlab==0.7.11
-modelscope==1.30.0
-# torch==2.6.0
-# torchvision==0.21.0

scripts/eval_toolcall.py

@@ -54,15 +54,27 @@ TEST_CASES = [
 ]
 
 
+def _resolve_path(path):
+    if os.path.isabs(path):
+        return path
+    abs_path = os.path.abspath(path)
+    if os.path.isdir(abs_path):
+        return abs_path
+    script_dir = os.path.dirname(os.path.abspath(__file__))
+    return os.path.abspath(os.path.join(script_dir, path))
+
+
 def init_model(args):
-    tokenizer = AutoTokenizer.from_pretrained(args.load_from)
+    load_path = _resolve_path(args.load_from)
+    tokenizer = AutoTokenizer.from_pretrained(load_path)
     if 'model' in args.load_from:
         model = MiniMindForCausalLM(MiniMindConfig(hidden_size=args.hidden_size, num_hidden_layers=args.num_hidden_layers, use_moe=bool(args.use_moe)))
         moe_suffix = '_moe' if args.use_moe else ''
-        ckp = f'./{args.save_dir}/{args.weight}_{args.hidden_size}{moe_suffix}.pth'
+        save_dir = _resolve_path(args.save_dir)
+        ckp = os.path.join(save_dir, f'{args.weight}_{args.hidden_size}{moe_suffix}.pth')
         model.load_state_dict(torch.load(ckp, map_location=args.device), strict=True)
     else:
-        model = AutoModelForCausalLM.from_pretrained(args.load_from, trust_remote_code=True)
+        model = AutoModelForCausalLM.from_pretrained(load_path, trust_remote_code=True)
     get_model_params(model, model.config)
     return model.half().eval().to(args.device), tokenizer