docs: 修正 assets/docs/ 类图、数据流、参数文档及贡献指南

- design.md: 新增 ProtocolHandler/OpenAIHandler/AnthropicHandler 等缺失类
- design.md: 新增 Template Method、Storage 设计模式
- dataflow.md: 修正 GQA/MLA 为独立条目，补充 JSON 存储后端
- params.md: 标注 label_smoothing CLI 默认与 strategy 默认差异
- introduction.md: 修正 max_tokens 默认值 1024→2048
- CONTRIBUTING.md: 重写（纯 Python 无 conda、补充 CI 步骤与常见问题）
- .github/PULL_REQUEST_TEMPLATE.md: 修正 lint 命令，去除多余注释要求
- .github/ISSUE_TEMPLATE/bug_report.md: 修正 label（enhancement→bug）

.github/ISSUE_TEMPLATE/bug_report.md

@@ -2,7 +2,7 @@
 name: Bug report
 about: Create a report to help us improve
 title: "[BUG]"
-labels: enhancement
+labels: bug
 assignees: ''
 
 ---

.github/PULL_REQUEST_TEMPLATE.md

@@ -16,9 +16,9 @@ Please delete options that are not relevant.
 Please describe the tests that you ran to verify your changes. Provide instructions so we can reproduce.
 
 ## Checklist:
-- [ ] My code follows the style guidelines of this project (run `ruff format .` and `ruff check --fix .`)
+- [ ] My code follows the style guidelines of this project (run `ruff format .` and `ruff check . --select I`)
 - [ ] I have performed a self-review of my own code
-- [ ] I have commented my code, particularly in hard-to-understand areas
+- [ ] Code is self-documenting (no unnecessary comments)
 - [ ] I have made corresponding changes to the documentation
 - [ ] My changes generate no new warnings
 - [ ] I have added tests that prove my fix is effective or that my feature works

CONTRIBUTING.md

@@ -1,68 +1,100 @@
 # Contributing to AstrAI
 
-Thank you for your interest in contributing to AstrAI! This document provides guidelines and steps for contributing.
+Thank you for your interest in contributing! This document provides step-by-step guidelines.
 
-## How to Contribute
+## Quick Start
 
-### Reporting Issues
-If you encounter a bug or have a feature request, please open an issue on GitHub. Include as much detail as possible:
-- A clear description of the problem or request.
-- Steps to reproduce (for bugs).
-- Your environment (Python version, OS, etc.).
-
-### Submitting Changes
-1. **Fork** the repository.
-2. **Clone** your fork:
 ```bash
 git clone https://github.com/your-username/AstrAI.git
 cd AstrAI
+pip install -e ".[dev]"     # install with dev dependencies (pytest, ruff)
 ```
-3. **Create a feature branch**:
+
+## Before You Commit
+
+Run the following checks **in order** — CI will reject if any fail.
+
+### 1. Format
+
 ```bash
-   git checkout -b feature/your-feature-name
+ruff format .
 ```
-4. **Make your changes**. Follow the code style guidelines below.
+
-5. **Commit your changes** with a descriptive commit message:
+> **Note**: `ruff format` may rename parameters (e.g. `mask` → `attn_mask`).  
+> Always review the diff after formatting.
+
+### 2. Import sorting
+
 ```bash
-   git commit -m "Add: brief description of the change"
+ruff check . --select I
 ```
-6. **Push** to your fork:
+
+If this fails, **manually fix** import ordering (ruff does not auto-fix in this project's CI):
+
 ```bash
-   git push origin feature/your-feature-name
+ruff check . --select I --fix .
+ruff format .    # re-format after fix
 ```
-7. **Open a Pull Request** (PR) against the `main` branch of the upstream repository.
 
-## Code Style
+### 3. Run tests
 
-AstrAI uses [Ruff](https://docs.astral.sh/ruff/) for code formatting and linting. Please ensure your code is formatted before submitting.
-
-- Run Ruff to format and lint (requires conda environment `nlp`):
 ```bash
-  conda run -n nlp ruff format .
+python -u -m pytest tests/ -v
-  conda run -n nlp ruff check --fix .
 ```
-- The project uses **double quotes** for strings and **4‑space indentation** (as configured in `pyproject.toml`).
 
-## Testing
+> Failed tests may leave orphan tempdirs under `%TEMP%`. Clean them manually if needed.
 
-If you add or modify functionality, please include appropriate tests.
+### 4. (Optional) Full pre-commit check
+
+If you have Git Bash available:
 
-- Run the test suite with:
 ```bash
-  conda run -n nlp python -u -m pytest
+bash scripts/pre_commit.sh
 ```
-- Ensure all tests pass before submitting your PR.
+
+This runs format check, import sort check, and tests in one go.
+
+## Commit Style
+
+```
+fix/feat/chore/docs/refactor/perf/test/style/ci/build/revert : short description (~50 chars)
+
+- bullet point body (each ~60 chars)
+```
+
+- **Type** must be one of: `fix`, `feat`, `chore`, `docs`, `refactor`, `perf`, `test`, `style`, `ci`, `build`, `revert`.
+- **Subject line** ends with no period.
+- **Body** uses bullet points starting with `-`.
+- No `(scope)` parentheses.
+
+## Common Issues
+
+| Problem | Cause | Fix |
+|---------|-------|-----|
+| `ruff check --select I` fails | Wrong import order | `ruff check . --select I --fix .` then `ruff format .` |
+| `ruff format` changed many files | Not formatted before commit | Review diff carefully before staging |
+| Pre-commit hook rejects | Tests or lint failed | Fix individually, do not `--no-verify` |
+| Tests fail with tempdir left | Test crash | Clean `%TEMP%` manually |
+
+## Submitting Changes
+
+1. Fork the repo.
+2. Create a feature branch: `git checkout -b feat/my-feature`
+3. Make changes following the steps above.
+4. Commit with the commit style above.
+5. Push: `git push origin feat/my-feature`
+6. Open a Pull Request against `main`.
 
 ## Code Review
 
-All submissions will be reviewed. We may request changes or discuss alternatives. Please be responsive to feedback.
+- All PRs are reviewed. We may request changes.
+- CI runs `ruff format --check .` then `ruff check . --select I` (no `--fix` in CI).
+- Ensure all tests pass.
 
 ## License
 
-By contributing, you agree that your contributions will be licensed under the same [GPL-3.0 License](LICENSE) that covers the project.
+By contributing, you agree that your contributions will be licensed under the [GPL-3.0 License](LICENSE).
 
 ---
 
-If you have any questions, feel free to ask in the [GitHub Discussions](https://github.com/ViperEkura/AstrAI/discussions) or open an issue.
+Questions? Ask in [GitHub Discussions](https://github.com/ViperEkura/AstrAI/discussions) or open an issue.
-
-Happy contributing!

assets/docs/dataflow.md

@@ -5,7 +5,7 @@ This document describes the data flow of the AstrAI project (a training and infe
 ## Overview
 
 AstrAI adopts a modular design with the following main components:
-- **Dataset Module** (`astrai/dataset/`): Dataset, sampler, serialization tools
+- **Dataset Module** (`astrai/dataset/`): Dataset, sampler, storage backends
 - **Model Module** (`astrai/model/`): AutoModel, Transformer model and its submodules
 - **Training Module** (`astrai/trainer/`): Trainer, training context, strategies, schedulers, callbacks, metric utilities
 - **Inference Module** (`astrai/inference/`): Inference engine with continuous batching, streaming generation
@@ -71,7 +71,8 @@ flowchart LR
 - **`BaseDataset`**: Abstract base class for windowed sequence sampling
 - **`BaseSegmentFetcher` / `MultiSegmentFetcher`**: Fetch tensor segments by index range
 - **`DatasetFactory`**: Creates dataset instances by `train_type` (`seq`, `sft`, `dpo`, `grpo`)
-- Data keys: `"sequence"` (SEQ), `"loss_mask"` (SFT), `"chosen_mask"/"rejected_mask"` (DPO), `"masks"` (GRPO)
+- Data keys: `"sequence"` (SEQ), `"loss_mask"` (SFT), `"chosen"/"rejected"` (DPO), `"prompts"/"responses"/"masks"/"rewards"` (GRPO)
+- Storage backends: HDF5 (`.h5`) or JSON (`.json`/`.jsonl`), auto-detected by `detect_format()`
 
 #### 2.2 Sampler (`sampler.py`)
 - **`ResumableDistributedSampler`**: Tracks `epoch` and `iter` for breakpoint resume; supports shuffle and drop_last
@@ -85,8 +86,9 @@ flowchart LR
 - RoPE position encoding, optional weight tying
 
 #### 3.2 Submodules (`module.py`)
-- **`DecoderBlock`**: GQA attention + residual + MLP + RMSNorm
+- **`DecoderBlock`**: Pre-LN (norm→attention→residual, norm→MLP→residual), uses `AttnFactory` / `FFNFactory`
-- **`GQA`**: Grouped Query Attention (also `MLA` for multi-latent attention)
+- **`GQA`**: Grouped Query Attention (q_heads ÷ kv_heads = n_rep)
+- **`MLA`**: Multi-head Latent Attention with KV compression (kv_lora_rank)
 - **`MLP`**: `SiLU(gate(x)) * up(x)` → down projection
 - **`RotaryEmbedding`**: RoPE complex cache (freqs_cis)
 - **`RMSNorm`**: Layer normalization
@@ -107,10 +109,12 @@ on_train_begin
     for each accumulation window of batches:        ← step phase
       on_step_begin
         for each batch in window:                    ← batch phase
-          on_batch_begin → strategy(batch) → loss → backward → on_batch_end
+          on_batch_begin → strategy(batch) → loss
+          (loss / window_size).backward() → on_batch_end
           iteration += 1
       on_step_end
       optimizer.step() → zero_grad
+      scheduler.step()                              ← per step, not per batch
 
     on_epoch_end
 on_train_end
@@ -120,7 +124,7 @@ Key points:
 - `on_step_*` fires every `accumulation_steps` batches, wrapping optimizer step AFTER the hook
 - `on_batch_*` fires every batch, wrapping loss computation
 - `GradientClippingCallback` fires on `on_step_end`
-- LR scheduler steps inline (no `SchedulerCallback` class)
+- LR scheduler steps inline (no `SchedulerCallback` class), once per optimizer step
 
 #### 4.3 Strategy (`strategy.py`)
 - **`SEQStrategy`**: Next-token prediction, cross-entropy with label smoothing
@@ -167,7 +171,7 @@ Background thread runs continuously:
 
 ### 6. Tokenizer Module
 
-- **`AutoTokenizer`**: Wraps HuggingFace tokenizers (BBPE); `encode`/`decode`/`apply_chat_template`
+- **`AutoTokenizer`**: Wraps HuggingFace `tokenizers.Tokenizer` (not `transformers`); `encode`/`decode`/`apply_chat_template`
 - **`ChatTemplate`**: Jinja2-based template rendering for multi-turn chat
 
 ### 7. Factory & Parallel
@@ -195,14 +199,14 @@ Background thread runs continuously:
    - Computes task-specific loss (cross-entropy, DPO, GRPO)
 
 5. **Backward & Accumulation**
-   - `loss = raw_loss / accumulation_steps`
+   - `stand_loss = loss / step_batch_nums` (divide by actual batch count in this window)
-   - `loss.backward()` accumulates gradients
+   - `stand_loss.backward()` accumulates gradients
    - Every `accumulation_steps` batches: `optimizer.step()` → `zero_grad()`
-   - Every batch: `scheduler.step()` updates learning rate
+   - Every optimizer step: `scheduler.step()` updates learning rate
 
 6. **Checkpoint**
    - `CheckpointCallback` saves `model.state_dict()` + metadata to safetensors at `ckpt_interval` iterations
-   - Does NOT save optimizer/scheduler state (resume resets those)
+   - Does NOT save optimizer/scheduler state by default; `Checkpoint.extra` or `save_extra_fn` can store arbitrary additional data
 
 ## Inference Data Flow — Detailed Steps
 
@@ -230,8 +234,8 @@ Background thread runs continuously:
 
 ## Checkpoint & Serialization
 
-- **Training Checkpoint**: safetensors weights + epoch/iteration metadata. Optimizer/scheduler state is NOT persisted.
+- **Training Checkpoint**: safetensors weights + epoch/iteration metadata + optional extras. Optimizer/scheduler state is NOT persisted by default but can be stored via `extra`.
 - **Inference Loading**: `AutoModel.from_pretrained()` loads from the same safetensors format.
 - **Dataset Serialization**: HDF5 with shared memory support for large-scale pre-training data.
 
-> Document Update Time: 2026-05-14
+> Document Update Time: 2026-05-15

assets/docs/design.md

@@ -22,6 +22,12 @@ classDiagram
             +int n_kv_heads
             +bool use_qk_norm
             +bool use_gated_attention
+            +str attn_type
+            +str ffn_type
+            +int n_routed_experts
+            +int n_shared_experts
+            +int n_activated_experts
+            +str moe_topk_method
             +load(config_path) ModelConfig
             +save(config_path)
         }
@@ -42,7 +48,7 @@ classDiagram
             +int ckpt_interval
             +int random_seed
             +int num_workers
-            +int prefetch_factor
+            +Optional[int] prefetch_factor
             +bool pin_memory
             +int nprocs
             +str backend
@@ -118,8 +124,8 @@ classDiagram
         }
 
         class ResumableDistributedSampler {
-            +int epoch
+            +int start_epoch
-            +int iter
+            +int start_iter
         }
 
         class DatasetFactory {
@@ -135,6 +141,7 @@ classDiagram
             +dict state_dict
             +int epoch
             +int iteration
+            +dict extra
             +save(save_dir)
             +load(save_dir) Checkpoint
         }
@@ -158,15 +165,15 @@ classDiagram
             +ModuleList layers
             +RMSNorm norm
             +Linear lm_head
-            +forward(input_ids, input_mask, paged_cache, position_ids) Tensor
+            +forward(input_ids, input_mask, paged_cache, position_ids) Dict
             +load_state_dict(state_dict)
             +state_dict()
         }
 
         class DecoderBlock {
-            +GQA attention
+            +nn.Module attention  # GQA or MLA via AttnFactory
             +RMSNorm input_norm
-            +MLP mlp
+            +nn.Module mlp        # MLP or DeepSeekMoE via FFNFactory
             +RMSNorm post_attention_norm
             +forward(x, rotary_emb, attention_mask, paged_cache) Tensor
         }
@@ -175,8 +182,12 @@ classDiagram
             +int n_heads
             +int n_kv_heads
             +int head_dim
+            +int n_rep
+            +bool use_qk_norm
+            +bool use_gated_attention
             +Linear q_proj, k_proj, v_proj, o_proj
-            +RMSNorm q_norm, k_norm
+            +Linear gate  # only if use_gated_attention
+            +RMSNorm q_norm, k_norm  # only if use_qk_norm
             +forward(x, rotary_emb, attn_mask, paged_cache) Tensor
         }
 
@@ -187,8 +198,11 @@ classDiagram
             +int kv_lora_rank
             +int qk_nope_head_dim
             +int qk_rope_head_dim
+            +int n_rep
+            +bool use_gated_attention
             +Linear q_proj, kv_a_proj, kv_b_proj
             +Linear o_proj
+            +Linear gate  # only if use_gated_attention
             +RMSNorm kv_norm
             +forward(x, rotary_emb, attn_mask, paged_cache) Tensor
         }
@@ -198,6 +212,25 @@ classDiagram
             +forward(x) Tensor
         }
 
+        class DeepSeekMoE {
+            +int n_routed_experts
+            +int n_shared_experts
+            +int n_activated_experts
+            +str topk_method
+            +Linear router
+            +ModuleList shared_experts
+            +ModuleList routed_experts
+            +forward(x) Tensor
+        }
+
+        class AttnFactory {
+            +create(attn_type, **kwargs) nn.Module
+        }
+
+        class FFNFactory {
+            +create(ffn_type, dim, dim_ffn, **kwargs) nn.Module
+        }
+
         class RMSNorm {
             +Parameter weight
             +float norm_eps
@@ -206,7 +239,7 @@ classDiagram
 
         class Linear {
             +Parameter weight
-            +Parameter bias
+            +Optional[Parameter] bias  # only if bias=True
             +forward(x) Tensor
         }
 
@@ -365,7 +398,7 @@ classDiagram
 
         class GradientClippingCallback {
             +float max_grad_norm
-            +on_step_begin(context)
+            +on_step_end(context)
         }
 
         class CheckpointCallback {
@@ -410,15 +443,24 @@ classDiagram
             +shutdown()
         }
 
-        class InferenceScheduler {
+        class Executor {
-            +nn.Module model
+            +AutoModel model
             +AutoTokenizer tokenizer
+            +KVCache page_cache
+            +execute_prefill(tasks, prompt_len, start_pos)
+            +execute_decode(tasks) List[int]
+        }
+
+        class InferenceScheduler {
             +KVCache _page_cache
+            +Executor _executor
+            +TaskManager _task_mgr
+            +bool _running
+            +Thread _loop_thread
             +int max_batch_size
             +int max_seq_len
             +int max_prompt_len
             +int page_size
-            +TaskManager _task_mgr
             +add_task(prompt, max_tokens, temperature, top_p, top_k, stream_callback) str
             +remove_task(task_id)
             +start()
@@ -428,8 +470,8 @@ classDiagram
 
         class Allocator {
             +int _free_mask
-            +int refs_count
+            +List[int] _refs
-            +LRU _lru
+            +OrderedDict _lru
             +alloc() int
             +free(idx, keep_cached)
             +inc_ref(idx)
@@ -564,9 +606,9 @@ classDiagram
             +List[bool] _done
             +append(token, idx)
             +get_results() List[str]
-            +pop_all() List[str]
+            +pop_all() List[Tuple[int, str]]
             +wait(timeout) bool
-            +wait_completion()
+            +wait_completion(timeout)
         }
 
         class ChatMessage {
@@ -584,6 +626,65 @@ classDiagram
             +Optional[str] stop
             +Optional[int] n
         }
+
+        class AnthropicMessage {
+            +str role
+            +Union[str, List[Dict]] content
+        }
+
+        class MessagesRequest {
+            +List[AnthropicMessage] messages
+            +Optional[str] system
+            +float temperature
+            +float top_p
+            +int top_k
+            +int max_tokens
+            +bool stream
+            +Optional[List[str]] stop_sequences
+        }
+
+        class ProtocolHandler {
+            <<abstract>>
+            +build_prompt() str
+            +create_response_id() str
+            +format_stream_start(ctx) List[str]
+            +format_stream_token(ctx, token) str
+            +format_stream_end(ctx) List[str]
+            +format_non_stream_response(ctx, content) Dict
+            +handle() Union[StreamingResponse, Dict]
+        }
+
+        class OpenAIHandler {
+            +build_prompt() str
+            +create_response_id() str
+        }
+
+        class AnthropicHandler {
+            +List[str] stop_sequences
+            +build_prompt() str
+            +create_response_id() str
+            +on_token(ctx, token, stop_checker) Optional[str]
+        }
+
+        class StopChecker {
+            +check(text) Optional[str]
+            +trim(text, matched) str
+        }
+
+        class StreamContext {
+            +str resp_id
+            +int created
+            +str model
+            +int prompt_tokens
+            +int completion_tokens
+            +str accumulated
+            +Optional[str] stop_matched
+        }
+
+        class app {
+            <<singleton>>
+            +FastAPI app
+        }
     }
 
     namespace parallel {
@@ -610,79 +711,104 @@ classDiagram
         }
     }
 
-    %% Relationships
+    %% Relationships — UML notation: <|-- generalization, *-- composition, o-- aggregation, --> association, ..> dependency
-    TrainConfig --> BaseDataset : uses
+
-    TrainConfig ..> BaseStrategy : selects
+    %% --- Generalization (inheritance) ---
-    StrategyFactory ..> BaseStrategy : creates
     BaseStrategy <|-- SEQStrategy
     BaseStrategy <|-- SFTStrategy
     BaseStrategy <|-- DPOStrategy
     BaseStrategy <|-- GRPOStrategy
-    DPOStrategy --> Transformer : uses
-    GRPOStrategy --> Transformer : uses
-    Trainer --> TrainConfig : uses
-    Trainer --> TrainContextBuilder : uses
-    Trainer --> TrainCallback : manages
-    TrainContextBuilder --> TrainContext : creates
-    TrainContextBuilder --> StrategyFactory : uses
-    Checkpoint ..> Checkpoint : serializes
-    TrainContext --> Checkpoint : manages
-    TrainContext --> BaseStrategy : uses
-    TrainContext --> BaseScheduler : uses
-    SchedulerFactory ..> BaseScheduler : creates
     BaseScheduler <|-- CosineScheduler
     BaseScheduler <|-- SGDRScheduler
-    CallbackFactory ..> TrainCallback : creates
     TrainCallback <|-- GradientClippingCallback
     TrainCallback <|-- CheckpointCallback
     TrainCallback <|-- ProgressBarCallback
     TrainCallback <|-- MetricLoggerCallback
-    PagePool --> Allocator : composes
-    PagePool --> PrefixCache : composes
-    KVCache --> PagePool : composes
-    KVCache --> Storage : composes
-    KVCache --> TaskTable : composes
-    KvcacheView --> Storage : wraps
-    InferenceEngine --> InferenceScheduler : uses
-    InferenceEngine --> GenerationRequest : uses
-    InferenceEngine --> GenerateResult : creates
-    InferenceScheduler --> Task : manages
-    InferenceScheduler --> TaskStatus : uses
-    InferenceScheduler --> KVCache : uses
-    InferenceScheduler --> Transformer : uses
-    Task --> TaskStatus : uses
-    InferenceEngine --> Transformer : uses
-    BaseSamplingStrategy <|-- TemperatureStrategy
-    BaseSamplingStrategy <|-- TopKStrategy
-    BaseSamplingStrategy <|-- TopPStrategy
-    SamplingPipeline --> BaseSamplingStrategy : composes
     BaseDataset <|-- SEQDataset
     BaseDataset <|-- SFTDataset
     BaseDataset <|-- DPODataset
     BaseDataset <|-- GRPODataset
-    DatasetFactory ..> BaseDataset : creates
     BaseStorage <|-- H5Storage
     BaseStorage <|-- JSONStorage
-    BaseDataset --> BaseStorage : uses
+    BaseSamplingStrategy <|-- TemperatureStrategy
-    MultiSegmentFetcher --> BaseSegmentFetcher : uses
+    BaseSamplingStrategy <|-- TopKStrategy
-    AutoModel <|-- Transformer
+    BaseSamplingStrategy <|-- TopPStrategy
-    AutoModel --> ModelConfig : contains
-    Transformer --> DecoderBlock : uses
-    Transformer --> RotaryEmbedding : uses
-    Transformer --> Embedding : uses
-    DecoderBlock --> GQA : uses
-    DecoderBlock --> MLP : uses
-    DecoderBlock --> RMSNorm : uses
-    TrainContextBuilder --> ResumableDistributedSampler : creates
-    ResumableDistributedSampler --> BaseDataset : samples
     ParallelModel <|-- RowParallelLinear
     ParallelModel <|-- ColumnParallelLinear
-    AutoTokenizer --> ChatTemplate : uses
+    AutoModel <|-- Transformer
     BaseFactory <|-- AutoModel
+    BaseFactory <|-- AttnFactory
+    BaseFactory <|-- FFNFactory
     BaseFactory <|-- DatasetFactory
     BaseFactory <|-- StrategyFactory
     BaseFactory <|-- SchedulerFactory
     BaseFactory <|-- CallbackFactory
+    ProtocolHandler <|-- OpenAIHandler
+    ProtocolHandler <|-- AnthropicHandler
+
+    %% --- Composition (strong ownership, part destroyed with whole) ---
+    KVCache *-- PagePool
+    KVCache *-- Storage
+    KVCache *-- TaskTable
+    KVCache *-- Allocator
+    KVCache *-- PrefixCache
+    InferenceEngine *-- InferenceScheduler
+    InferenceScheduler *-- KVCache
+    InferenceScheduler *-- Executor
+    InferenceScheduler *-- TaskManager
+    SamplingPipeline *-- BaseSamplingStrategy
+    TrainContextBuilder *-- TrainContext
+    Transformer *-- DecoderBlock
+    Transformer *-- RotaryEmbedding
+    Transformer *-- Embedding
+    DecoderBlock *-- RMSNorm
+    BaseDataset *-- BaseStorage
+
+    %% --- Aggregation (weak ownership) ---
+    AutoModel o-- ModelConfig
+    Trainer o-- TrainCallback
+    TrainContext o-- BaseStrategy
+    TrainContext o-- BaseScheduler
+    TrainContext o-- Checkpoint
+    AutoTokenizer o-- ChatTemplate
+    KvcacheView o-- Storage
+    BaseFactory o-- Registry
+
+    %% --- Dependency (uses temporarily) ---
+    TrainConfig ..> BaseStrategy : selects
+    StrategyFactory ..> BaseStrategy : creates
+    SchedulerFactory ..> BaseScheduler : creates
+    DatasetFactory ..> BaseDataset : creates
+    CallbackFactory ..> TrainCallback : creates
+    AttnFactory ..> GQA : creates
+    AttnFactory ..> MLA : creates
+    FFNFactory ..> MLP : creates
+    FFNFactory ..> DeepSeekMoE : creates
+    DecoderBlock ..> AttnFactory : uses
+    DecoderBlock ..> FFNFactory : uses
+    Trainer ..> TrainContextBuilder : uses
+    Trainer ..> Functions : spawns
+    TrainContextBuilder ..> StrategyFactory : uses
+    TrainContextBuilder ..> ResumableDistributedSampler : creates
+    Checkpoint ..> Checkpoint : serializes
+    CheckpointCallback ..> Checkpoint : creates
+    KVCache ..> KvcacheView : binds
+    InferenceEngine ..> GenerationRequest : uses
+    InferenceEngine ..> GenerateResult : creates
+
+    %% --- Association (general usage) ---
+    Trainer --> TrainConfig
+    DPOStrategy --> Transformer
+    GRPOStrategy --> Transformer
+    InferenceScheduler --> Task
+    InferenceScheduler --> TaskStatus
+    Task --> TaskStatus
+    InferenceEngine --> Transformer
+    Executor --> Transformer
+    Executor --> AutoTokenizer
+    TaskManager --> AutoTokenizer
+    MultiSegmentFetcher --> BaseSegmentFetcher
+    ResumableDistributedSampler --> BaseDataset
 ```
 
 ### Module Overview
@@ -690,14 +816,14 @@ classDiagram
 | Module | Components | Description |
 |--------|------------|-------------|
 | **astrai.config** | ModelConfig, TrainConfig | Configuration management |
-| **astrai.dataset** | BaseDataset, SEQDataset, SFTDataset, DPODataset, GRPODataset, BaseStorage, H5Storage, JSONStorage, BaseSegmentFetcher, MultiSegmentFetcher, ResumableDistributedSampler, DatasetFactory, save_h5, load_h5 | Dataset loading and management |
+| **astrai.dataset** | BaseDataset, SEQDataset, SFTDataset, DPODataset, GRPODataset, BaseStorage, H5Storage, JSONStorage, BaseSegmentFetcher, MultiSegmentFetcher, ResumableDistributedSampler, DatasetFactory, save_h5, load_h5, save_json, load_json, create_storage, detect_format | Dataset loading and management |
 | **astrai.serialization** | Checkpoint | Model serialization and checkpoint management |
-| **astrai.model** | AutoModel, Transformer, DecoderBlock, GQA, MLA, MLP, RMSNorm, Linear, RotaryEmbedding, Embedding | Neural network model |
+| **astrai.model** | AutoModel, Transformer, DecoderBlock, GQA, MLA, MLP, DeepSeekMoE, AttnFactory, FFNFactory, RMSNorm, Linear, RotaryEmbedding, Embedding | Neural network model |
 | **astrai.tokenize** | AutoTokenizer, ChatTemplate | Tokenizer and chat template |
 | **astrai.trainer** | Trainer, TrainContext, TrainContextBuilder, BaseStrategy, StrategyFactory, BaseScheduler, SchedulerFactory, TrainCallback, CallbackFactory | Training workflow management |
-| **astrai.inference** | InferenceEngine, InferenceScheduler, KVCache, KvcacheView, Allocator, PrefixCache, PagePool, Storage, TaskTable, Task, TaskStatus, GenerationRequest, BaseSamplingStrategy, TemperatureStrategy, TopKStrategy, TopPStrategy, SamplingPipeline, ChatMessage, ChatCompletionRequest | Inference service with continuous batching and paged KV cache |
+| **astrai.inference** | InferenceEngine, InferenceScheduler, Executor, KVCache, KvcacheView, Allocator, PrefixCache, PagePool, Storage, TaskTable, Task, TaskManager, TaskStatus, GenerationRequest, BaseSamplingStrategy, TemperatureStrategy, TopKStrategy, TopPStrategy, SamplingPipeline, sample, ChatMessage, ChatCompletionRequest, AnthropicMessage, MessagesRequest, OpenAIHandler, AnthropicHandler, ProtocolHandler, StreamContext, StopChecker, app, run_server | Inference service with continuous batching and paged KV cache |
-| **astrai.parallel** | spawn_parallel_fn, setup_parallel, get_rank, get_world_size, get_current_device, ParallelModel, ColumnParallelLinear, RowParallelLinear | Distributed parallel |
+| **astrai.parallel** | spawn_parallel_fn, setup_parallel, get_rank, get_world_size, get_current_device, only_on_rank, ParallelModel, ColumnParallelLinear, RowParallelLinear | Distributed parallel |
-| **astrai.factory** | Registry, BaseFactory | Generic component registration |
+| **astrai.factory** | Registry, BaseFactory[T] | Generic component registration with decorator pattern |
 
 ### Design Patterns
 
@@ -706,7 +832,7 @@ classDiagram
 | **Strategy** | `BaseStrategy`, `SEQStrategy`, `SFTStrategy`, `DPOStrategy`, `GRPOStrategy`, `StrategyFactory` | Flexible training strategy switching, supports SEQ/SFT/DPO/GRPO |
 | **Builder** | `TrainContextBuilder` | Chain-building training context, step-by-step initialization of components |
 | **Factory** | `StrategyFactory`, `SchedulerFactory`, `DatasetFactory`, `CallbackFactory`, `BaseFactory` | Decorator registration mechanism, dynamically create training strategies, schedulers, datasets, and callbacks |
-| **Observer** | `TrainCallback`, `CallbackFactory` | Callback mechanism for training process monitoring (checkpoint, early stopping, metrics) |
+| **Observer** | `TrainCallback`, `CallbackFactory` | Callback mechanism for training process monitoring (checkpoint, gradient clipping, metrics) |
 | **Context** | `TrainContext` | Training process state container with model, optimizer, scheduler and checkpoint |
 | **Registry** | `BaseFactory`, `Registry` | Generic component registration with category and priority support |
 | **Object Pool** | `Allocator`, `PagePool` | Page-based KV cache with O(1) alloc/free via bitmask + LRU eviction |
@@ -715,6 +841,8 @@ classDiagram
 | **Event-Driven** | `threading.Event`, `_task_event` | Non-blocking wait mechanism for task scheduling using Python's `threading` module |
 | **AutoModel Registry** | `AutoModel`, `Transformer` | Model type registration and dynamic loading via decorator pattern |
 | **Generator Pattern** | `GenerateResult`, `GenerationRequest` | Event-based result notification for streaming/non-streaming generation |
+| **Template Method** | `ProtocolHandler`, `OpenAIHandler`, `AnthropicHandler` | `handle()` template with stream/non-stream branches, protocol-specific format hooks |
+| **Storage** | `BaseStorage`, `H5Storage`, `JSONStorage`, `_STORAGE_REGISTRY` | Format-agnostic data access with registry-dispatch (HDF5 / JSON) |
 
 ### Core Relationships
 
@@ -723,7 +851,7 @@ classDiagram
 3. **Strategy Selection**: `StrategyFactory` creates corresponding strategy instance based on `train_type`
 4. **Inference Flow**: `InferenceEngine` → `InferenceScheduler` → `Transformer`, uses `KVCache` (backed by `Allocator` + `PrefixCache` + `PagePool` + `Storage`) for paged KV cache management and `SamplingPipeline` for efficient continuous batching with streaming/non-streaming
 5. **Distributed Support**: `spawn_parallel_fn` and `setup_parallel` provide multi-process training capability for `Trainer`
-6. **Dataset Loading**: `DatasetFactory` creates datasets (SEQDataset, SFTDataset, DPODataset, GRPODataset), supports HDF5 loading via `BaseSegmentFetcher` and `MultiSegmentFetcher`
+6. **Dataset Loading**: `DatasetFactory` creates datasets (SEQDataset, SFTDataset, DPODataset, GRPODataset), supports HDF5 and JSON loading via `BaseStorage` (`H5Storage` / `JSONStorage`) with `BaseSegmentFetcher` and `MultiSegmentFetcher`
 7. **Checkpoint Management**: `Checkpoint` handles model state serialization/deserialization with safetensors
 8. **Scheduler Support**: `SchedulerFactory` creates learning rate schedulers (CosineScheduler, SGDRScheduler)
 9. **AutoModel Loading**: `AutoModel.from_pretrained()` dynamically loads model based on `config.json` model_type, uses `Registry` pattern for model type registration
@@ -776,4 +904,4 @@ The final loss is the sum of both: $L = L_{\text{policy}} + L_{KL}$
 
 Through the above three-stage progressive training, the model completes its evolution from a general language foundation to a specialized, highly-aligned dialogue intelligence.
 
-> Document Update Time: 2026-05-14
+> Document Update Time: 2026-05-15

assets/docs/introduction.md

@@ -224,7 +224,7 @@ curl -X POST http://localhost:8000/v1/chat/completions \
 | `temperature` | float | 1.0 | Sampling temperature (0.0-2.0) |
 | `top_p` | float | 1.0 | Nucleus sampling threshold |
 | `top_k` | int | 50 | Top-k sampling parameter |
-| `max_tokens` | int | 1024 | Maximum tokens to generate |
+| `max_tokens` | int | 2048 | Maximum tokens to generate |
 | `stream` | bool | false | Enable streaming response |
 
 **Response (non-streaming):**
@@ -331,4 +331,4 @@ curl http://localhost:8000/stats
 # {"total_tasks": 10, "total_tokens": 5000, "active_tasks": 1, "waiting_queue": 0}
 ```
 
-> Document Update Time: 2026-05-14
+> Document Update Time: 2026-05-15

assets/docs/params.md

@@ -60,7 +60,7 @@
 | Parameter | Description | Default | Used by |
 |-----------|-------------|---------|---------|
 | `--dpo_beta` | DPO beta value | 0.1 | `dpo` |
-| `--label_smoothing` | Label smoothing for cross-entropy loss | 0.1 | `seq`, `sft` |
+| `--label_smoothing` | Label smoothing for cross-entropy loss | 0.1 (CLI) / 0.0 (strategy default) | `seq`, `sft` |
 | `--group_size` | GRPO group size | 4 | `grpo` |
 | `--grpo_clip_eps` | GRPO clipping epsilon | 0.2 | `grpo` |
 | `--grpo_kl_coef` | GRPO KL penalty coefficient | 0.01 | `grpo` |
@@ -98,7 +98,7 @@ python scripts/tools/train.py \
 | `temperature` | Sampling temperature (higher = more random) | 1.0 |
 | `top_p` | Nucleus sampling threshold | 1.0 |
 | `top_k` | Top-k sampling count | 50 |
-| `max_tokens` | Maximum generation length | None (unlimited) |
+| `max_tokens` | Maximum generation length | None (defaults to max_seq_len - prompt_len) |
 | `stream` | Whether to stream output | False |
 
 ### Usage Example
@@ -155,4 +155,4 @@ result = engine.generate(
 | `stream=True` | Streaming output, yields token by token |
 | `stream=False` | Non-streaming output, returns complete result |
 
-> Document Update Time: 2026-05-14
+> Document Update Time: 2026-05-15