chore(deps): 使用pyproject.toml和uv管理依赖环境
- 新增pyproject.toml文件，列出所有依赖及版本 - 移除旧的requirements.txt及相关内容 - 添加uv配置，支持不同平台的PyTorch索引 - 生成uv.lock文件，锁定依赖版本和来源 - 更新.gitignore，忽略虚拟环境和模型目录 - 修改README及README_en，指导使用uv同步和运行命令 - 将安装与运行示例均改为使用"uv run"和"uv sync" - 适配模型下载与训练脚本调用方式使用uv工具pull/120/head
100apps
parent
ee18831dce
commit
8fb354789b
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@ -1,5 +1,8 @@
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model/__pycache__
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__pycache__
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out
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website/
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docs-minimind-v/
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测试.py
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.venv
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model
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40
README.md
40
README.md
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@ -142,7 +142,7 @@
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* Ubuntu==20.04
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* CUDA==12.2
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* Python==3.10
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* [requirements.txt](./requirements.txt)
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* [pyproject.toml](./pyproject.toml)（使用 [uv](https://docs.astral.sh/uv/) 管理依赖）
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</details>
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@ -153,17 +153,17 @@
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```bash
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# 克隆仓库代码
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git clone --depth 1 https://github.com/jingyaogong/minimind-v
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# 安装必要依赖
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pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple
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# 安装必要依赖（使用 uv）
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uv sync
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```
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### 2' 下载资源
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```bash
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# 下载 SigLIP2 视觉编码器到 ./model/siglip2-base-p32-256-ve
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modelscope download --model gongjy/siglip2-base-p32-256-ve --local_dir ./model/siglip2-base-p32-256-ve
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uv run modelscope download --model gongjy/siglip2-base-p32-256-ve --local_dir ./model/siglip2-base-p32-256-ve
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# 下载 MiniMind 语言模型权重到 ./out 目录下（作为训练 VLM 的基座语言模型）
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modelscope download --model gongjy/minimind-3v-pytorch llm_768.pth --local_dir ./out
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uv run modelscope download --model gongjy/minimind-3v-pytorch llm_768.pth --local_dir ./out
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```
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注：也可从 [ModelScope Collection](https://modelscope.cn/collections/gongjy/MiniMind-V) 或 [HuggingFace Collection](https://huggingface.co/collections/jingyaogong/minimind-v-67000833fb60b3a2e1f3597d) 选择对应内容 `git clone`（需 LFS）下载，此处不再赘述。
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@ -186,29 +186,27 @@ minimind-v/
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```bash
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# 下载发布权重到 ./out 目录下
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modelscope download --model gongjy/minimind-3v-pytorch --local_dir ./out
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uv run modelscope download --model gongjy/minimind-3v-pytorch --local_dir ./out
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```
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### 2' 命令行问答
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```bash
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# load_from='model': 加载原生PyTorch权重, load_from='其他路径': 加载transformers格式
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python eval_vlm.py --load_from model --weight sft_vlm
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uv run python eval_vlm.py --load_from model --weight sft_vlm
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```
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如果使用 transformers 格式模型，可先下载模型目录：
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```bash
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git clone https://huggingface.co/jingyaogong/minimind-3v
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python eval_vlm.py --load_from minimind-3v
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uv run python eval_vlm.py --load_from minimind-3v
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```
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### 3' 启动 WebUI（可选）
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```bash
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# ⚠️ 须先将 transformers 格式模型文件夹复制到 ./scripts/ 目录下，web_demo_vlm 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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cp -r minimind-3v ./scripts/minimind-3v
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cd scripts && python web_demo_vlm.py
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uv run python scripts/web_demo_vlm.py
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```
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## Ⅱ 🛠️ 模型训练
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@ -234,7 +232,7 @@ print(torch.cuda.is_available())
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【注1】之前需解压50万零碎的图像文件可能非常慢。2025-12-27起，数据集格式统一为 Parquet，图文一体化存储，体积更小，无需解压，加载更快。
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【注2】Parquet 是列式存储格式，支持高效压缩和快速读取。如果你对它感到陌生，可以预览数据内容，在 `dataset/` 目录下执行 `python lm_dataset.py` 可视化前5条图文对。
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【注2】Parquet 是列式存储格式，支持高效压缩和快速读取。如果你对它感到陌生，可以预览数据内容，在 `dataset/` 目录下执行 `uv run python lm_dataset.py` 可视化前5条图文对。
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Pretrain 数据（可选；仅包含 caption 子集）：
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```bash
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@ -250,14 +248,14 @@ SFT 单文件 290 万条已把 Pretrain 作为子集合并，经全局 dictionar
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推荐直接执行 SFT。默认 `--freeze_llm 1`，即训练 `vision_proj` 和 LLM 首尾层，保留中间层原有语言能力：
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```bash
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python train_sft_vlm.py --epochs 2 --from_weight llm
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uv run python train_sft_vlm.py --epochs 2 --from_weight llm
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```
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如果希望让 Projector 先完成一轮图文对齐，再进入 SFT，可额外执行 Pretrain：
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```bash
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python train_pretrain_vlm.py --epochs 2 --from_weight llm
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python train_sft_vlm.py --epochs 2 --from_weight pretrain_vlm
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uv run python train_pretrain_vlm.py --epochs 2 --from_weight llm
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uv run python train_sft_vlm.py --epochs 2 --from_weight pretrain_vlm
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```
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执行完成后，`out/` 下会生成 `sft_vlm_*.pth` 作为 SFT 权重。
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@ -272,7 +270,7 @@ python train_sft_vlm.py --epochs 2 --from_weight pretrain_vlm
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```bash
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# 训练中断后，使用相同命令并添加 --from_resume 1
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python train_sft_vlm.py --epochs 4 --from_resume 1
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uv run python train_sft_vlm.py --epochs 4 --from_resume 1
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```
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**参数说明：**
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@ -291,10 +289,10 @@ python train_sft_vlm.py --epochs 4 --from_resume 1
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```bash
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# 测试SFT模型（默认）
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python eval_vlm.py --weight sft_vlm
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uv run python eval_vlm.py --weight sft_vlm
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# 测试Pretrain模型
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python eval_vlm.py --weight pretrain_vlm
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uv run python eval_vlm.py --weight pretrain_vlm
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```
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---
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@ -305,7 +303,7 @@ python eval_vlm.py --weight pretrain_vlm
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单机N卡启动训练方式 (DDP, 支持多机多卡集群)
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```bash
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torchrun --nproc_per_node N train_xxx.py
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uv run torchrun --nproc_per_node N train_xxx.py
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```
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<details>
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@ -323,9 +321,9 @@ deepspeed --master_port 29500 --num_gpus=N train_xxx.py
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```bash
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# 需要登录: wandb login
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torchrun --nproc_per_node N train_xxx.py --use_wandb
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uv run torchrun --nproc_per_node N train_xxx.py --use_wandb
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# and
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python train_xxx.py --use_wandb
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uv run python train_xxx.py --use_wandb
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```
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通过添加`--use_wandb`参数，可以记录训练过程，训练完成后，可以在wandb网站上查看训练过程。通过修改`wandb_project`
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32
README_en.md
32
README_en.md
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@ -144,7 +144,7 @@ Is the training process difficult? Now, let's explore the answers and feel the j
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* Ubuntu==20.04
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* CUDA==12.2
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* Python==3.10
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* [requirements.txt](./requirements.txt)
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* [pyproject.toml](./pyproject.toml) (dependencies managed by [uv](https://docs.astral.sh/uv/))
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</details>
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@ -155,8 +155,8 @@ Is the training process difficult? Now, let's explore the answers and feel the j
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```bash
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# Clone the repository
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git clone --depth 1 https://github.com/jingyaogong/minimind-v
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# Install dependencies
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pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple
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# Install dependencies (using uv)
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uv sync
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```
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### 2' Download resources
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@ -195,14 +195,14 @@ modelscope download --model gongjy/minimind-3v-pytorch --local_dir ./out
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```bash
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# load_from='model': load native PyTorch weights, load_from='other path': load transformers format
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python eval_vlm.py --load_from model --weight sft_vlm
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uv run python eval_vlm.py --load_from model --weight sft_vlm
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```
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If using a transformers-format model, download the model directory first:
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```bash
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git clone https://huggingface.co/jingyaogong/minimind-3v
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python eval_vlm.py --load_from minimind-3v
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uv run python eval_vlm.py --load_from minimind-3v
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```
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### 3' Start WebUI (optional)
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@ -210,7 +210,7 @@ python eval_vlm.py --load_from minimind-3v
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```bash
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# ⚠️ The transformers-format model directory must be copied to ./scripts/ first. web_demo_vlm scans subdirectories under ./scripts/ that contain weight files and reports an error if none are found.
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cp -r minimind-3v ./scripts/minimind-3v
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cd scripts && python web_demo_vlm.py
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cd scripts && uv run python web_demo_vlm.py
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```
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## Ⅱ 🛠️ Model training
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@ -236,7 +236,7 @@ For a quick start, download `sft_i2t.parquet` from the [dataset link](https://hu
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**[Note 1]** The older dataset required extracting 500k fragmented image files, which could be very slow. Since 2025-12-27, the dataset has been unified into Parquet with image and text stored together. It is smaller, requires no decompression, and loads faster.
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**[Note 2]** Parquet is a columnar storage format with efficient compression and fast reading. If it is unfamiliar, run `python lm_dataset.py` under `dataset/` to visualize the first 5 image-text pairs.
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**[Note 2]** Parquet is a columnar storage format with efficient compression and fast reading. If it is unfamiliar, run `uv run python lm_dataset.py` under `dataset/` to visualize the first 5 image-text pairs.
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Pretrain data (optional; contains caption subset only):
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```bash
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@ -252,14 +252,14 @@ The single `sft_i2t.parquet` file contains 2.9M rows and absorbs Pretrain as a s
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SFT is the recommended starting point. By default, `--freeze_llm 1` trains `vision_proj` and the first/last LLM layers while keeping the middle layers' original language ability:
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```bash
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python train_sft_vlm.py --epochs 2 --from_weight llm
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uv run python train_sft_vlm.py --epochs 2 --from_weight llm
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```
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If the Projector should be aligned on image-text pairs before SFT, run Pretrain first:
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```bash
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python train_pretrain_vlm.py --epochs 2 --from_weight llm
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python train_sft_vlm.py --epochs 2 --from_weight pretrain_vlm
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uv run python train_pretrain_vlm.py --epochs 2 --from_weight llm
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uv run python train_sft_vlm.py --epochs 2 --from_weight pretrain_vlm
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```
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After training, `sft_vlm_*.pth` will be written under `out/` as the SFT weight.
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@ -274,7 +274,7 @@ After training, `sft_vlm_*.pth` will be written under `out/` as the SFT weight.
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```bash
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# To resume training after interruption, use the same command and add --from_resume 1
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python train_sft_vlm.py --epochs 4 --from_resume 1
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uv run python train_sft_vlm.py --epochs 4 --from_resume 1
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```
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**Parameter Description:**
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@ -294,10 +294,10 @@ from [here](https://huggingface.co/jingyaogong/minimind-3v-pytorch).
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```bash
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# Test SFT model (default)
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python eval_vlm.py --weight sft_vlm
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uv run python eval_vlm.py --weight sft_vlm
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# Test Pretrain model
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python eval_vlm.py --weight pretrain_vlm
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uv run python eval_vlm.py --weight pretrain_vlm
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```
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---
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@ -309,7 +309,7 @@ python eval_vlm.py --weight pretrain_vlm
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Single-machine N-card training method (DDP, supports multi-machine multi-card cluster)
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```bash
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torchrun --nproc_per_node N train_xxx.py
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uv run torchrun --nproc_per_node N train_xxx.py
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```
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<details>
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|
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@ -325,9 +325,9 @@ You can enable wandb logging during training:
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```bash
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# You need to log in: wandb login
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torchrun --nproc_per_node N train_xxx.py --use_wandb
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uv run torchrun --nproc_per_node N train_xxx.py --use_wandb
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# and
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python train_xxx.py --use_wandb
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uv run python train_xxx.py --use_wandb
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```
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By adding the `--use_wandb` parameter, you can log the training process, and after training is complete, you can view
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|
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|
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@ -0,0 +1 @@
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Subproject commit 1202082d61dcf6aa6afc36ec57df4e5dda4b3bec
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@ -0,0 +1,62 @@
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[project]
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name = "minimind-v"
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version = "0.1.0"
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description = "MiniMind-V multimodal vision-language model training and inference scripts."
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readme = "README.md"
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requires-python = ">=3.10,<3.11"
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dependencies = [
|
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"datasets==3.6.0",
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"datasketch==1.6.4",
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"Flask==3.0.3",
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"Flask-Cors==4.0.0",
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"jieba==0.42.1",
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"jsonlines==4.0.0",
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"marshmallow==3.22.0",
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"matplotlib==3.5.1",
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"ngrok==1.4.0",
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"nltk==3.8",
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"numpy==1.26.4",
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"openai==1.59.6",
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"peft==0.7.1",
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"psutil==5.9.8",
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"pydantic==2.8.2",
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"rich==13.7.1",
|
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"scikit-learn==1.5.1",
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"sentence-transformers==2.3.1",
|
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"simhash==2.1.2",
|
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"tiktoken==0.10.0",
|
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"transformers==4.57.6",
|
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"jinja2==3.1.2",
|
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"trl==0.13.0",
|
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"ujson==5.1.0",
|
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"wandb==0.18.3",
|
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"streamlit==1.50.0",
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"gradio==5.49.1",
|
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"modelscope-studio==1.6.1",
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"python-multipart==0.0.22",
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"pyarrow==23.0.0",
|
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"Pillow",
|
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"swanlab==0.6.12",
|
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"torch==2.10.0",
|
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"torchvision==0.25.0",
|
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]
|
||||
|
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[tool.uv]
|
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package = false
|
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|
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[tool.uv.sources]
|
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torch = [
|
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{ index = "pytorch-cu128", marker = "sys_platform == 'win32' or sys_platform == 'linux'" },
|
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]
|
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torchvision = [
|
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{ index = "pytorch-cu128", marker = "sys_platform == 'win32' or sys_platform == 'linux'" },
|
||||
]
|
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|
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[[tool.uv.index]]
|
||||
url = "https://pypi.tuna.tsinghua.edu.cn/simple"
|
||||
default = true
|
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|
||||
[[tool.uv.index]]
|
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name = "pytorch-cu128"
|
||||
url = "https://download.pytorch.org/whl/cu128"
|
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explicit = true
|
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|
|
@ -1,34 +0,0 @@
|
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datasets==3.6.0
|
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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==3.5.1
|
||||
ngrok==1.4.0
|
||||
nltk==3.8
|
||||
numpy==1.26.4
|
||||
openai==1.59.6
|
||||
peft==0.7.1
|
||||
psutil==5.9.8
|
||||
pydantic==2.8.2
|
||||
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
|
||||
trl==0.13.0
|
||||
ujson==5.1.0
|
||||
wandb==0.18.3
|
||||
streamlit==1.50.0
|
||||
gradio==5.49.1
|
||||
modelscope-studio==1.6.1
|
||||
python-multipart==0.0.22
|
||||
pyarrow==23.0.0
|
||||
Pillow
|
||||
swanlab==0.6.12
|
||||
# torch==2.6.0
|
||||
# torchvision==0.21.0
|
||||
|
|
@ -154,7 +154,7 @@ def launch_gradio_server(server_name="0.0.0.0", server_port=7788):
|
|||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser(description="Chat with MiniMind-V")
|
||||
parser.add_argument('--load_from', default='./', type=str, help="transformers模型扫描目录")
|
||||
parser.add_argument('--vision_model', default='../model/siglip2-base-p32-256-ve', type=str, help="视觉编码器路径")
|
||||
parser.add_argument('--vision_model', default='./model/siglip2-base-p32-256-ve', type=str, help="视觉编码器路径")
|
||||
parser.add_argument('--temperature', default=0.7, type=float, help="生成温度")
|
||||
parser.add_argument('--top_p', default=0.95, type=float, help="nucleus采样阈值")
|
||||
parser.add_argument('--device', default='cuda' if torch.cuda.is_available() else 'cpu', type=str, help="运行设备")
|
||||
|
|
|
|||
Loading…
Reference in New Issue