286 lines
7.7 KiB
Markdown
286 lines
7.7 KiB
Markdown
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<div style="display: flex; flex-direction: column; align-items: center; justify-content: center; text-align: center; font-size: 16px; font-weight: bold; margin-top: 50px;">
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<div>
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<a href="#english" style="text-decoration: none; margin: 0 10px; color: blue;">English</a> |
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<a href="#chinese" style="text-decoration: none; margin: 0 10px; color: blue;">中文</a>
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</div>
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<h1 style="margin: 20px 0 0 0; font-size: 2.5em; font-weight: bold;">KHAOSZ </h1>
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</div>
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<h2 id="english">English Version</h2>
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A training and inference framework for autoregressive Transformer language models.
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**Model Download Options (choose one):**
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1. Visit [HuggingFace](https://huggingface.co/ViperEk/KHAOSZ) and check **Files and versions**
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2. Run `scripts/download.py` to download model parameters
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**Demo Video:** [bilibili](https://www.bilibili.com/video/BV1z5RPYHEkd)
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For training data sources, please refer to the **Model Card** section on the HuggingFace download page.
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**License:** The code follows the GPL-3.0 license. Please provide attribution when using it.
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- **📊 Device Selection:** Uses CUDA for training by default
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- **🌐 Performance Optimization:** Enable `dtype=torch.bfloat16` to accelerate training and reduce memory usage. Ensure your hardware supports this feature
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- **🤖 Language Support:** The model supports training in Chinese and English. Since the BBPE tokenizer hasn't been trained on multilingual text, OOV (Out-of-Vocabulary) issues are minimal for Chinese and English, but may exist for other languages
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### 📌 Training Guide
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To train this Transformer model, follow these steps:
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**(1). Prepare the Dataset:**
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Place the dataset in the specified root directory. This system uses the BBPE tokenizer for tokenization and requires training with pre-tokenized segments (stored as *.h5 format files).
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**(2). Install Dependencies:**
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```bash
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pip install -e .
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```
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**(3). Run the Training Script:**
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```bash
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python train.py \
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--train_type=train_type[seq, sft, dpo] \
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--data_root_path=/path/to/dataset \
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--param_path=/path/to/param_path \
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--n_epoch=5 \
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--batch_size=8 \
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--max_lr=2e-4 \
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--checkpoint_interval=10000 \
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--checkpoint_dir=checkpoints
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```
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**Parameter Explanation:**
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- `--train_type`: Training type (seq, sft, dpo)
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- `--data_root_path`: Dataset root directory
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- `--param_path`: Path to model training parameters
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- `--n_epoch`: Total number of training epochs
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- `--batch_size`: Batch size
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- `--accumulation_steps`: Number of batches per training step
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- `--warmup_steps`: Warmup steps
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- `--max_lr`: Maximum learning rate (using warmup + cosine decay)
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- `--checkpoint_interval`: Checkpoint saving interval
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- `--checkpoint_dir`: Checkpoint saving directory
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- `--resume_dir`: Resume training from specified path
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### 👉 Usage Guide
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**(1). Chat with the Model:**
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Open `chat.py` or use the streaming/non-streaming interfaces:
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**Streaming Output:**
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```python
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import torch
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from khaosz import Khaosz
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model_dir = "your_model_parameter_dir"
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model = Khaosz(model_dir).to(device='cuda', dtype=torch.bfloat16)
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history = []
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while True:
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query = input(">> ")
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if query == "!exit":
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break
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response_size = 0
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for response, history in model.stream_generate(
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query=query,
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history=history,
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temperature=0.85,
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top_p=0.95,
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top_k=50
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):
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print(response[response_size:], end="")
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response_size = len(response)
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```
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**Non-streaming Output:**
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```python
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import torch
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from khaosz import Khaosz
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model_dir = "your_model_parameter_dir"
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model = Khaosz(model_dir).to(device='cuda', dtype=torch.bfloat16)
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history = []
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while True:
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query = input(">> ")
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if query == "!exit":
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break
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response = model.generate(
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query=query,
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history=history,
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temperature=0.85,
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top_p=0.95,
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top_k=50
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)
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print(response)
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```
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**(2). Retrieval-Augmented Generation (RAG):**
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```python
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import torch
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from khaosz import Khaosz
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model_dir = "your_model_parameter_dir"
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model = Khaosz(model_dir).to(device='cuda', dtype=torch.bfloat16)
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retrieved_content = model.retrieve_generate(
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query=query,
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retrieve_top_k=5,
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temperature=0.6,
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top_k=30,
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top_p=0.95
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)
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print(retrieved_content)
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```
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<h2 id="chinese">中文版本</h2>
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这是一个支持基于自回归模式的 Transfomer 语言模型训练以及推理框架
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**模型下载选项（任选其一）：**
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1. 访问 [HuggingFace](https://huggingface.co/ViperEk/KHAOSZ) 查看 **Files and versions**
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2. 运行 `scripts/download.py` 下载模型参数
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**演示视频：** [bilibili](https://www.bilibili.com/video/BV1z5RPYHEkd)
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训练数据来源请参见 HuggingFace 下载页面中的 **Model Card** 部分。
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**许可证：** 代码遵循 GPL-3.0 协议，使用时请注明出处。
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- **📊 设备选择：** 默认使用 CUDA 进行训练
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- **🌐 性能优化：** 启用 `dtype=torch.bfloat16` 以加速训练并减少内存占用，请确保硬件支持该特性
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- **🤖 语言支持：** 模型支持中文和英文训练。由于 BBPE 分词器未使用多语言文本训练，因此中英文的 OOV（未登录词）问题较少，其他语言可能存在 OOV 问题
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### 📌 训练指南
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要训练该 Transformer 模型，请按照以下步骤操作：
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**(1). 准备数据集：**
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将数据集放置在指定的根目录下， 本系统采用 BBPE 分词器进行分词，并且要求使用已经经过分词的 token 分段训练（分段存储为 *.h5 格式）
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**(2). 安装依赖：**
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```bash
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pip install -e .
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```
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**(3). 运行训练脚本：**
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```bash
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python train.py \
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--train_type=train_type[seq, sft, dpo] \
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--data_root_path=/path/to/dataset \
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--param_path=/path/to/param_path \
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--n_epoch=5 \
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--batch_size=8 \
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--max_lr=2e-4 \
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--checkpoint_interval=10000 \
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--checkpoint_dir=checkpoints
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```
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**参数说明：**
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- `--train_type`: 训练类型（seq, sft, dpo）
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- `--data_root_path`: 数据集根目录
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- `--param_path`: 模型训练参数路径
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- `--n_epoch`: 总训练轮数
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- `--batch_size`: 批量大小
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- `--accumulation_steps`: 每个训练步骤的 batch 数量
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- `--warmup_steps`: 预热步数（warmup steps）
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- `--max_lr`: 最大学习率（使用预热 + 余弦衰减）
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- `--checkpoint_interval`: 检查点保存间隔
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- `--checkpoint_dir`: 检查点保存目录
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- `--resume_dir`: 从指定路径恢复训练
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### 👉 使用指南
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**(1). 与模型对话：**
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打开 `chat.py` 或使用流式/非流式接口：
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**流式输出：**
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```python
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import torch
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from khaosz import Khaosz
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model_dir = "your_model_parameter_dir"
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model = Khaosz(model_dir).to(device='cuda', dtype=torch.bfloat16)
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history = []
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while True:
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query = input(">> ")
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if query == "!exit":
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break
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response_size = 0
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for response, history in model.stream_generate(
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query=query,
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history=history,
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temperature=0.85,
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top_p=0.95,
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top_k=50
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):
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print(response[response_size:], end="")
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response_size = len(response)
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```
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**非流式输出：**
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```python
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import torch
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from khaosz import Khaosz
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model_dir = "your_model_parameter_dir"
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model = Khaosz(model_dir).to(device='cuda', dtype=torch.bfloat16)
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history = []
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while True:
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query = input(">> ")
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if query == "!exit":
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break
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response = model.generate(
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query=query,
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history=history,
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temperature=0.85,
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top_p=0.95,
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top_k=50
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)
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print(response)
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```
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**(2). 基于检索的生成（RAG）：**
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```python
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import torch
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from khaosz import Khaosz
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model_dir = "your_model_parameter_dir"
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model = Khaosz(model_dir).to(device='cuda', dtype=torch.bfloat16)
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retrieved_content = model.retrieve_generate(
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query=query,
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retrieve_top_k=5,
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temperature=0.6,
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top_k=30,
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top_p=0.95
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)
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print(retrieved_content)
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``` |