chore: 更新项目名称

assets/docs/dataflow.md

@@ -1,16 +1,16 @@
-# KHAOSZ Data Flow Documentation
+# AstrAI Data Flow Documentation
 
-This document describes the data flow of the KHAOSZ project (a training and inference framework for autoregressive Transformer language models). It covers the complete flow from raw data to model training and inference.
+This document describes the data flow of the AstrAI project (a training and inference framework for autoregressive Transformer language models). It covers the complete flow from raw data to model training and inference.
 
 ## Overview
 
-KHAOSZ adopts a modular design with the following main components:
-- **Data Module** (`khaosz/data/`): Dataset, sampler, tokenizer, serialization tools
-- **Model Module** (`khaosz/model/`): Transformer model and its submodules
-- **Training Module** (`khaosz/trainer/`): Trainer, training context, strategies, schedulers
-- **Inference Module** (`khaosz/inference/`): Generation core, KV cache management, streaming generation
-- **Config Module** (`khaosz/config/`): Model, training, scheduler, and other configurations
-- **Parallel Module** (`khaosz/parallel/`): Distributed training support
+AstrAI adopts a modular design with the following main components:
+- **Data Module** (`astrai/data/`): Dataset, sampler, tokenizer, serialization tools
+- **Model Module** (`astrai/model/`): Transformer model and its submodules
+- **Training Module** (`astrai/trainer/`): Trainer, training context, strategies, schedulers
+- **Inference Module** (`astrai/inference/`): Generation core, KV cache management, streaming generation
+- **Config Module** (`astrai/config/`): Model, training, scheduler, and other configurations
+- **Parallel Module** (`astrai/parallel/`): Distributed training support
 
 The data flow can generally be divided into two main lines: **Training Data Flow** and **Inference Data Flow**.
 
@@ -199,7 +199,7 @@ flowchart LR
 
 ## Summary
 
-The data flow design of KHAOSZ reflects the characteristics of modularity, extensibility, and resumability. The training data flow supports large-scale distributed training through chunk loading, resumable sampling, gradient accumulation, and other mechanisms; the inference data flow achieves efficient text generation using KV cache and sampling strategies. Clear interfaces between modules facilitate customization and extension.
+The data flow design of AstrAI reflects the characteristics of modularity, extensibility, and resumability. The training data flow supports large-scale distributed training through chunk loading, resumable sampling, gradient accumulation, and other mechanisms; the inference data flow achieves efficient text generation using KV cache and sampling strategies. Clear interfaces between modules facilitate customization and extension.
 
 > Document Update Time: 2026-03-30  
 > Corresponding Code Version: Refer to version number defined in `pyproject.toml`

assets/docs/design.md

@@ -2,7 +2,7 @@
 
 There are many large language models on the market today, such as GPT, LLaMA, and others, with tens of billions or even hundreds of billions of parameters. But honestly, these models have extremely high hardware requirements, making them inaccessible for ordinary developers. I thought: **Can we create a model that is both useful and can run on ordinary computers?** This is also what most people currently hope for - a locally deployable AI project that achieves complete privatization while maintaining some level of intelligence.
 
-Thus, the KHAOSZ project was born - 1B parameters, Chinese-English bilingual, supporting dialogue, text generation, RAG retrieval, and the training code is open source!
+Thus, the AstrAI project was born - 1B parameters, Chinese-English bilingual, supporting dialogue, text generation, RAG retrieval, and the training code is open source!
 
 ## 2. System Architecture
 

assets/docs/params.md

@@ -83,8 +83,8 @@
 ### Usage Example
 
 ```python
-from khaosz.config.param_config import ModelParameter
-from khaosz.inference.generator import StreamGenerator, GenerationRequest
+from astrai.config.param_config import ModelParameter
+from astrai.inference.generator import StreamGenerator, GenerationRequest
 
 # Load model
 param = ModelParameter.load("your_model_dir")