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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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This is a Chinese-English bilingual Transformer model supporting both languages. It contains model configurations and training workflows, completing training by loading parameters defined in `param_path/config.json`. The training script `train.py` parses command-line arguments, including dataset root directory, number of training epochs, batch size, checkpoint interval, and checkpoint directory.
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**Model Download Options (Choose One):**
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1. Visit [HuggingFace](https://huggingface.co/ViperEk/KHAOSZ) to access **Files and versions**
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2. Run `scripts/download.py` to download parameters
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**Demo Video:** [bilibili](https://www.bilibili.com/video/BV1z5RPYHEkd)
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Training dataset sources are listed in the **Model Card** section of the HuggingFace download link.
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**License:** Code follows Apache-2.0 protocol. Please credit the source code when used.
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- **📊 Device Selection:** Code defaults to CUDA training
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- **🌐 Performance Optimization:** `dtype=torch.bfloat16` is enabled to accelerate training and reduce memory usage. Ensure hardware supports this feature.
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- **🤖 Language Support:** Model supports Chinese and English training. The BBPE tokenizer was trained without multilingual text, so OOV (out-of-vocabulary) issues are minimized for these languages but may exist for others.
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### 📌 Training Guide
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To train this Transformer model, follow these steps:
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**(1). Prepare Dataset:**
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Place datasets in the designated root directory. Files should be text documents in Chinese, English, or mixed. Format should align with model input requirements - preferably pre-tokenized token_ids stored as `torch.Tensor` (using `torch.Tensor` saves memory compared to Python lists, which default to 64-bit precision).
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**(2). Install Dependencies:**
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```bash
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pip install -r requirements.txt
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pip install .
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```
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**(3). Run 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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--n_iter_ckpt=10000 \
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--ckpt_dir=checkpoints
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```
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**Parameters Explanation:**
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- `--train_type`: Training type (seq, sft, dpo)
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- `--data_root_path`: Root directory of the dataset
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- `--param_path`: Path to the 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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- `--n_iter_step`: Number of batches per training step
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- `--warning_step`: Number of warmup steps
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- `--max_lr`: Maximum learning rate (using warmup + cosine decay)
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- `--n_iter_ckpt`: Checkpoint saving interval
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- `--ckpt_dir`: Directory to save checkpoints
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- `--resume_dir`: Resume training from the specified path
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Training logs will be saved in `train_log.txt`. Checkpoints will be saved in the specified directory for resuming training or evaluation.
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### 👉 Usage Guide
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**(1). Chatting with the Model:**
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Open `chat.py` or use 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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### 📌 Model Specifications
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This model is based on a 24-layer Transformer with parameters defined in `config.json`, totaling approximately 1.0 billion (1.0B) parameters.
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**Key Design Choices:**
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- Weight tying between embedding and final linear layers (standard for small models to save parameters)
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- Embedding layer optimization: Without weight tying, a 10,000-word vocabulary would consume ~102M parameters (0.1B)
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**Limitations:**
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- May struggle with complex language phenomena due to smaller parameter size
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- Prone to overfitting on specialized datasets
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- Limited multilingual capabilities
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**Advantages:**
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- Runs efficiently on lower-spec hardware
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- Shorter training time compared to larger models
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**Training Pipeline:**
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The model has completed pre-training + SFT (Supervised Fine-Tuning) + DPO (Direct Preference Optimization) workflows. All corresponding training code is included in the repository.
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<h2 id="chinese">中文版本</h2>
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这是一个支持中英文双语的 Transformer 模型，能够处理两种语言。模型包含配置文件和训练流程，通过加载 `param_path/config.json` 中定义的参数完成训练。训练脚本 `train.py` 支持命令行参数解析，包括数据集根目录、训练轮数（epochs）、批量大小（batch size）、检查点保存间隔、检查点目录等。
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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 Apache-2.0 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。文件应为包含中文、英文或混合文本的文本文档。格式应符合模型输入要求——建议使用预分词后的 `token_ids` 并以 `torch.Tensor` 格式保存（使用 `torch.Tensor` 相比 Python 列表更节省内存，列表默认为 64 位精度）。
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#### **(2). 安装依赖：**
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```bash
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pip install -r requirements.txt
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pip install .
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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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--n_iter_ckpt=10000 \
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--ckpt_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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- `--n_iter_step`: 每个训练步骤的 batch 数量
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- `--warning_step`: 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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- `--n_iter_ckpt`: 检查点保存间隔
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- `--ckpt_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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训练日志将保存在 `train_log.txt` 中。检查点将保存在指定目录，用于恢复训练或评估。
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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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```
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### 📌 模型规格说明（重复部分）
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该模型基于一个 24 层的 Transformer 架构，参数配置定义在 `config.json` 中，总参数量约为 10 亿（1.0B）。
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**关键设计选择：**
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- 在嵌入层（embedding）与最终线性层之间进行权重绑定（weight tying），这是小型模型中常见的节省参数量的做法
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- 嵌入层优化：若不进行权重绑定，一个包含 10,000 个词的词汇表将消耗约 1.02 亿（0.1B）参数
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**局限性：**
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- 由于参数规模较小，可能在处理复杂语言现象时表现受限
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- 在特定领域的数据集上容易出现过拟合
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- 多语言能力有限
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**优势：**
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- 可在低配置硬件上高效运行
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- 相较于大型模型，训练时间更短
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**训练流程：**
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该模型已完成预训练（pre-training）+ 监督微调（SFT, Supervised Fine-Tuning）+ 直接偏好优化（DPO, Direct Preference Optimization）的全流程。所有相关的训练代码均已包含在代码库中。
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