KHAOSZ

English Version

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 params/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.

Model Download Options (Choose One):

Visit HuggingFace to access Files and versions
Run params/download.py to download parameters

Demo Video: bilibili

Training dataset sources are listed in the Model Card section of the HuggingFace download link.

License: Code follows Apache-2.0 protocol. Please credit the source code when used.

📊 Device Selection: Code defaults to CUDA training
🌐 Performance Optimization: dtype=torch.bfloat16 is enabled to accelerate training and reduce memory usage. Ensure hardware supports this feature.
🤖 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.

📌 Training Guide

To train this Transformer model, follow these steps:

(1). Prepare Dataset:

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).

(2). Install Dependencies:

pip install -r requirements.txt
pip install .

(3). Run Training Script:

python train.py \
--train_type=train_type[seq, sft, dpo] \
--data_root_path=/path/to/dataset \
--n_epoch=5 \
--batch_size=8 \
--max_lr=2e-4 \
--n_iter_ckpt=10000 \
--ckpt_dir checkpoints

Parameters Explanation:

--train_type: Training type (seq, sft, dpo)
--data_root_path: Dataset root directory
--n_epoch: Total training epochs
--batch_size: Batch size
--n_iter_step: Number of batches per training step
--warning_step: Warmup steps
--max_lr: Maximum learning rate (uses warmup + cosine decay)
--n_iter_ckpt: Checkpoint saving interval
--ckpt_dir: Checkpoint directory
--resume_dir: Path to resume training from checkpoint

Training logs are saved in train_log.txt. Checkpoints will be stored in the specified directory for resuming training or evaluation.

👉 Usage Guide

(1). Chatting with the Model:

Open chat.py or use streaming/non-streaming interfaces:

Streaming Output:

import torch
from khaosz import Khaosz

model_dir = "your_model_parameter_dir"
model = Khaosz(model_dir).to(device='cuda', dtype=torch.bfloat16)
history = []

while True:
    query = input(">> ")
    if query == "!exit":
        break
    
    response_size = 0
    for response, history in model.stream_generate(
        query=query, 
        history=history,
        temperature=0.85,
        top_p=0.95,
        top_k=50
    ):
        print(response[response_size:], end="")
        response_size = len(response)

Non-streaming Output:

import torch
from khaosz import Khaosz

model_dir = "your_model_parameter_dir"
model = Khaosz(model_dir).to(device='cuda', dtype=torch.bfloat16)
history = []

while True:
    query = input(">> ")
    if query == "!exit":
        break
    
    response = model.generate(
        query=query, 
        history=history,
        temperature=0.85,
        top_p=0.95,
        top_k=50
    )
    print(response)

(2) Retrieval-Augmented Generation (RAG):

import torch
from khaosz import Khaosz

model_dir = "your_model_parameter_dir"
model = Khaosz(model_dir).to(device='cuda', dtype=torch.bfloat16)

retrieved_content = model.retrieve_generate(
    query=query,
    retrieve_top_k=5,
    temperature=0.6,
    top_k=30,
    top_p=0.95
)
print(retrieved_content)

📌 Model Specifications

This model is based on a 24-layer Transformer with parameters defined in config.json, totaling approximately 1.0 billion (1.0B) parameters.

Key Design Choices:

Weight tying between embedding and final linear layers (standard for small models to save parameters)
Embedding layer optimization: Without weight tying, a 10,000-word vocabulary would consume ~102M parameters (0.1B)

Limitations:

May struggle with complex language phenomena due to smaller parameter size
Prone to overfitting on specialized datasets
Limited multilingual capabilities

Advantages:

Runs efficiently on lower-spec hardware
Shorter training time compared to larger models

Training Pipeline: The model has completed pre-training + SFT (Supervised Fine-Tuning) + DPO (Direct Preference Optimization) workflows. All corresponding training code is included in the repository.

中文版本

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 `params/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。

模型下载选项（任选其一）：

访问 HuggingFace 查看 Files and versions
运行 params/download.py 下载模型参数

演示视频： bilibili

训练数据来源请参见 HuggingFace 下载页面中的 Model Card 部分。

许可证： 代码遵循 Apache-2.0 协议，使用时请注明出处。

📊 设备选择： 默认使用 CUDA 进行训练
🌐 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
🤖 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

📌 训练指南

要训练该 Transformer 模型，请按照以下步骤操作：

(1). 准备数据集：

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）。

(2). 安装依赖：

pip install -r requirements.txt
pip install .

(3). 运行训练脚本：

python train.py \
--train_type=train_type[seq, sft, dpo] \
--data_root_path=/path/to/dataset \
--n_epoch=5 \
--batch_size=8 \
--max_lr=2e-4 \
--n_iter_ckpt=10000 \
--ckpt_dir checkpoints

参数说明：

--train_type: 训练类型（seq, sft, dpo）
--data_root_path: 数据集根目录
--n_epoch: 总训练轮数
--batch_size: 批量大小
--n_iter_step: 每个训练步骤的 batch 数量
--warning_step: 预热步数（warmup steps）
--max_lr: 最大学习率（使用预热 + 余弦衰减）
--n_iter_ckpt: 检查点保存间隔
--ckpt_dir: 检查点保存目录
--resume_dir: 从指定路径恢复训练

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。

👉 使用指南

(1). 与模型对话：

打开 chat.py 或使用流式/非流式接口：

流式输出：

import torch
from khaosz import Khaosz

model_dir = "your_model_parameter_dir"
model = Khaosz(model_dir).to(device='cuda', dtype=torch.bfloat16)
history = []

while True:
    query = input(">> ")
    if query == "!exit":
        break
    
    response_size = 0
    for response, history in model.stream_generate(
        query=query, 
        history=history,
        temperature=0.85,
        top_p=0.95,
        top_k=50
    ):
        print(response[response_size:], end="")
        response_size = len(response)

非流式输出：

import torch
from khaosz import Khaosz

model_dir = "your_model_parameter_dir"
model = Khaosz(model_dir).to(device='cuda', dtype=torch.bfloat16)
history = []

while True:
    query = input(">> ")
    if query == "!exit":
        break
    
    response = model.generate(
        query=query, 
        history=history,
        temperature=0.85,
        top_p=0.95,
        top_k=50
    )
    print(response)

(2). 基于检索的生成（RAG）：

import torch
from khaosz import Khaosz

model_dir = "your_model_parameter_dir"
model = Khaosz(model_dir).to(device='cuda', dtype=torch.bfloat16)

retrieved_content = model.retrieve_generate(
    query=query,
    retrieve_top_k=5,
    temperature=0.6,
    top_k=30,
    top_p=0.95
)
print(retrieved_content)

📌 模型规格说明（重复部分）

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）。

关键设计选择：

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
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

局限性：

由于参数规模较小，可能在处理复杂语言现象时表现受限
在特定领域的数据集上容易出现过拟合
多语言能力有限

优势：

可在低配置硬件上高效运行
相较于大型模型，训练时间更短

训练流程：
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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KHAOSZ