chore(deps): 使用uv pyproject.toml替代requirements.txt管理依赖并增加vscode debug 配置，方便debug。
- 新增pyproject.toml文件，定义项目依赖和Python版本要求3.10 - 移除旧的requirements.txt文件，依赖迁移至pyproject.toml中 - 添加uv工具配置，支持依赖索引和同步管理 - 更新README文档中依赖安装指令，使用uv sync替代pip install - 统一运行命令前添加uv run，提高一致性和环境隔离 - 在.gitignore中添加虚拟环境和缓存相关文件夹忽略规则 - 新增.python-version文件指定Python版本3.10pull/756/head
100apps
parent
bdee223036
commit
97903ffe56
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@ -3,4 +3,12 @@ __pycache__/
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.DS_Store
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out
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website/
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docs-minimind/
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docs-minimind/
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minimind-3
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dataset/**
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!dataset/lm_dataset.py
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!dataset/__init__.py
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!dataset/dataset.md
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checkpoints/
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.venv/
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.uv-cache/
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@ -0,0 +1 @@
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3.10
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@ -0,0 +1,227 @@
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{
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// 使用 IntelliSense 了解可用属性。
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// 悬停以查看现有属性的相关描述。
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// 欲了解更多信息，请访问: https://go.microsoft.com/fwlink/?linkid=830387
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//
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// 使用说明：
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// 1. 先执行 `uv sync` 安装依赖
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// 2. 在 VS Code 中按 Ctrl+Shift+P → "Python: Select Interpreter"
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// 选择 `./.venv/Scripts/python.exe` (Windows) 或 `./.venv/bin/python` (Linux/Mac)
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// 3. 按 F5 或 Ctrl+Shift+D 选择对应配置启动调试
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//
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// 注：对应 README.md 中所有 `uv run python` / `uv run streamlit` 的单卡命令
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// 多卡训练 (`torchrun`) 建议在终端直接执行，不在此列出
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"version": "0.2.0",
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"configurations": [
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// ==================== 1. 推理与评测 ====================
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{
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"name": "推理: eval_llm (transformers格式)",
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"type": "debugpy",
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"request": "launch",
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"program": "${workspaceFolder}/eval_llm.py",
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"cwd": "${workspaceFolder}",
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"console": "integratedTerminal",
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"args": ["--load_from", "./minimind-3"]
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},
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{
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"name": "推理: eval_llm (pytorch格式)",
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"type": "debugpy",
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"request": "launch",
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"program": "${workspaceFolder}/eval_llm.py",
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"cwd": "${workspaceFolder}",
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"console": "integratedTerminal",
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"args": ["--load_from", "./model", "--weight", "full_sft"]
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},
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{
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"name": "推理: eval_llm (open_thinking)",
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"type": "debugpy",
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"request": "launch",
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"program": "${workspaceFolder}/eval_llm.py",
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"cwd": "${workspaceFolder}",
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"console": "integratedTerminal",
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"args": ["--load_from", "./minimind-3", "--open_thinking", "1"]
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},
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{
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"name": "推理: eval_llm (RoPE外推)",
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"type": "debugpy",
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"request": "launch",
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"program": "${workspaceFolder}/eval_llm.py",
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"cwd": "${workspaceFolder}",
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"console": "integratedTerminal",
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"args": ["--weight", "full_sft", "--inference_rope_scaling"]
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},
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{
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"name": "评测: eval_toolcall (full_sft)",
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"type": "debugpy",
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"request": "launch",
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"program": "${workspaceFolder}/scripts/eval_toolcall.py",
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"cwd": "${workspaceFolder}",
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"console": "integratedTerminal",
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"args": ["--weight", "full_sft"]
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},
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{
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"name": "评测: eval_toolcall (agent)",
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"type": "debugpy",
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"request": "launch",
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"program": "${workspaceFolder}/scripts/eval_toolcall.py",
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"cwd": "${workspaceFolder}",
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"console": "integratedTerminal",
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"args": ["--weight", "agent"]
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},
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// ==================== 2. WebUI 与服务 ====================
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{
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"name": "服务: WebUI (streamlit)",
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"type": "debugpy",
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"request": "launch",
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"module": "streamlit",
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"cwd": "${workspaceFolder}/scripts",
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"console": "integratedTerminal",
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"args": ["run", "web_demo.py"]
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},
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{
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"name": "服务: OpenAI API Server",
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"type": "debugpy",
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"request": "launch",
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"program": "${workspaceFolder}/scripts/serve_openai_api.py",
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"cwd": "${workspaceFolder}/scripts",
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"console": "integratedTerminal"
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},
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{
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"name": "服务: Chat API 测试",
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"type": "debugpy",
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"request": "launch",
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"program": "${workspaceFolder}/scripts/chat_api.py",
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"cwd": "${workspaceFolder}/scripts",
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"console": "integratedTerminal"
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},
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// ==================== 3. 预训练与微调 ====================
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{
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"name": "训练: Pretrain (预训练)",
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"type": "debugpy",
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"request": "launch",
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"program": "${workspaceFolder}/trainer/train_pretrain.py",
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"cwd": "${workspaceFolder}/trainer",
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"console": "integratedTerminal"
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},
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{
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"name": "训练: Pretrain (断点续训)",
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"type": "debugpy",
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"request": "launch",
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"program": "${workspaceFolder}/trainer/train_pretrain.py",
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"cwd": "${workspaceFolder}/trainer",
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"console": "integratedTerminal",
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"args": ["--from_resume", "1"]
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},
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{
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"name": "训练: Full SFT (全参数微调)",
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"type": "debugpy",
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"request": "launch",
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"program": "${workspaceFolder}/trainer/train_full_sft.py",
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"cwd": "${workspaceFolder}/trainer",
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"console": "integratedTerminal"
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},
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{
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"name": "训练: Full SFT (断点续训)",
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"type": "debugpy",
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"request": "launch",
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"program": "${workspaceFolder}/trainer/train_full_sft.py",
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"cwd": "${workspaceFolder}/trainer",
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"console": "integratedTerminal",
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"args": ["--from_resume", "1"]
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},
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{
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"name": "训练: LoRA",
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"type": "debugpy",
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"request": "launch",
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"program": "${workspaceFolder}/trainer/train_lora.py",
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"cwd": "${workspaceFolder}/trainer",
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"console": "integratedTerminal"
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},
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{
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"name": "训练: Distillation (知识蒸馏)",
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"type": "debugpy",
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"request": "launch",
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"program": "${workspaceFolder}/trainer/train_distillation.py",
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"cwd": "${workspaceFolder}/trainer",
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"console": "integratedTerminal"
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},
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{
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"name": "训练: Tokenizer (分词器训练)",
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"type": "debugpy",
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"request": "launch",
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"program": "${workspaceFolder}/trainer/train_tokenizer.py",
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"cwd": "${workspaceFolder}/trainer",
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"console": "integratedTerminal"
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},
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// ==================== 4. 强化学习 ====================
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{
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"name": "RL: DPO",
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"type": "debugpy",
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"request": "launch",
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"program": "${workspaceFolder}/trainer/train_dpo.py",
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"cwd": "${workspaceFolder}/trainer",
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"console": "integratedTerminal"
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},
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{
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"name": "RL: PPO",
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"type": "debugpy",
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"request": "launch",
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"program": "${workspaceFolder}/trainer/train_ppo.py",
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"cwd": "${workspaceFolder}/trainer",
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"console": "integratedTerminal"
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},
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{
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"name": "RL: GRPO",
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"type": "debugpy",
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"request": "launch",
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"program": "${workspaceFolder}/trainer/train_grpo.py",
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"cwd": "${workspaceFolder}/trainer",
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"console": "integratedTerminal"
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},
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{
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"name": "RL: CISPO (GRPO变体)",
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"type": "debugpy",
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"request": "launch",
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"program": "${workspaceFolder}/trainer/train_grpo.py",
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"cwd": "${workspaceFolder}/trainer",
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"console": "integratedTerminal",
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"args": ["--loss_type", "cispo"]
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},
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{
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"name": "RL: Agentic RL",
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"type": "debugpy",
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"request": "launch",
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"program": "${workspaceFolder}/trainer/train_agent.py",
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"cwd": "${workspaceFolder}/trainer",
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"console": "integratedTerminal"
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},
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{
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"name": "RL: Agentic RL (SGLang)",
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"type": "debugpy",
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"request": "launch",
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"program": "${workspaceFolder}/trainer/train_agent.py",
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"cwd": "${workspaceFolder}/trainer",
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"console": "integratedTerminal",
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"args": [
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"--rollout_engine", "sglang",
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"--sglang_base_url", "http://localhost:8998",
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"--sglang_shared_path", "./ckpt_mm",
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"--data_path", "../dataset/agent_rl_math.jsonl",
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"--use_wandb"
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]
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},
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// ==================== 5. 工具与转换 ====================
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{
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"name": "工具: 模型转换",
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"type": "debugpy",
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"request": "launch",
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"program": "${workspaceFolder}/scripts/convert_model.py",
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"cwd": "${workspaceFolder}/scripts",
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"console": "integratedTerminal"
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}
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]
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}
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29
README.md
29
README.md
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@ -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
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* Ubuntu==20.04
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* CUDA==12.2
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* Python==3.10.16
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* [requirements.txt](./requirements.txt)
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* [uv](https://docs.astral.sh/uv/) + [pyproject.toml](./pyproject.toml)
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</details>
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@ -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
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```bash
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# 克隆仓库、安装依赖
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git clone --depth 1 https://github.com/jingyaogong/minimind
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cd minimind && pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple
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cd minimind
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uv sync
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```
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## Ⅰ 🚀 模型推理
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@ -239,7 +240,7 @@ cd minimind && pip install -r requirements.txt -i https://mirrors.aliyun.com/pyp
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在项目根目录：
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```bash
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# 方式1
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modelscope download --model gongjy/minimind-3 --local_dir ./minimind-3
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uv run modelscope download --model gongjy/minimind-3 --local_dir ./minimind-3
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# 方式2
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git clone https://huggingface.co/jingyaogong/minimind-3
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```
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@ -248,17 +249,17 @@ git clone https://huggingface.co/jingyaogong/minimind-3
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```bash
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# 方式1：使用 Transformers 格式模型
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python eval_llm.py --load_from ./minimind-3
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uv run python eval_llm.py --load_from ./minimind-3
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# 方式2：基于 PyTorch 模型（确保./out目录下有对应权重）
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python eval_llm.py --load_from ./model --weight full_sft
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uv run python eval_llm.py --load_from ./model --weight full_sft
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```
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### 3'（可选）WebUI
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```bash
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# 可能需要`python>=3.10`，安装 `pip install streamlit`
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# 使用 `uv sync` 安装依赖后即可运行
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# ⚠️ 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
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cd scripts && streamlit run web_demo.py
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cd scripts && uv run streamlit run web_demo.py
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```
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### 4'（可选）第三方推理框架
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@ -282,7 +283,7 @@ print(torch.cuda.is_available())
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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。
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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。
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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)
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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`。
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</details>
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@ -301,8 +302,8 @@ print(torch.cuda.is_available())
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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：
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```bash
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python train_pretrain.py --from_resume 1
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python train_full_sft.py --from_resume 1
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uv run python train_pretrain.py --from_resume 1
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uv run python train_full_sft.py --from_resume 1
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# ...
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```
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@ -319,7 +320,7 @@ python train_full_sft.py --from_resume 1
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#### 2.1 预训练（必须）
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```bash
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cd trainer && python train_pretrain.py
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cd trainer && uv run python train_pretrain.py
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```
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||||
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||||
> 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`）
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|
@ -327,7 +328,7 @@ cd trainer && python train_pretrain.py
|
|||
#### 2.2 指令微调（必须）
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||||
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||||
```bash
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||||
cd trainer && python train_full_sft.py
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||||
cd trainer && uv run python train_full_sft.py
|
||||
```
|
||||
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||||
> 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）
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||||
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@ -337,7 +338,7 @@ cd trainer && python train_full_sft.py
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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。
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||||
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||||
```bash
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||||
python eval_llm.py --weight full_sft
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||||
uv run python eval_llm.py --weight full_sft
|
||||
```
|
||||
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||||
> `--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`
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||||
|
|
@ -350,7 +351,7 @@ 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
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||||
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。
|
||||
|
|
|
|||
31
README_en.md
31
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 @@ If you have more needs, various combination schemes are provided below, which ca
|
|||
All training scripts support checkpoint saving. After adding the `--from_resume 1` parameter, training progress can be automatically detected and resumed:
|
||||
|
||||
```bash
|
||||
python train_pretrain.py --from_resume 1
|
||||
python train_full_sft.py --from_resume 1
|
||||
uv run python train_pretrain.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.
|
||||
|
|
@ -1989,4 +1990,4 @@ If `MiniMind` has been helpful to your research or work, feel free to cite:
|
|||
|
||||
# ⚖️ License
|
||||
|
||||
This project is open-sourced under the [Apache License 2.0](LICENSE).
|
||||
This project is open-sourced under the [Apache License 2.0](LICENSE).
|
||||
|
|
|
|||
|
|
@ -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
|
||||
|
|
@ -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
|
||||
Loading…
Reference in New Issue