fix: 修复文档多处不准确 + inference scheduler 越界 bug + SchedulerCallback 回调阶段修正

文档 (6 个文件):
- design.md: 15+ 处修正 — persistent_key_values→paged_cache,
  MLA 字段重写, Server/ParallelSetup 不存在类移除,
  关系箭头方向修复, SchedulerCallback 阶段修正等
- dataflow.md: 重写数据流图和描述, 修复训练回调顺序、
  数据键名、MLA 归属、MetricTracker 等错误
- introduction.md: 层数 32→24, MLP 图双 Linear 修正,
  默认值/响应字段/health 端点修复
- params.md: 补充 grpo 及 4 个 GRPO 参数
- README.md / README-zh-CN.md: generate.py 补全必需参数,
  删除重复注释, HuggingFace 声明修正

代码 (2 个文件):
- scheduler.py: n_pages 池加 page_size 余量防止越界;
  decode 前预分配页
- train_callback.py: SchedulerCallback 从 on_step_end 改
  回 on_batch_end (按 batch 步进学习率)

assets/docs/dataflow.md

@@ -9,13 +9,11 @@ AstrAI adopts a modular design with the following main components:
 - **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
-- **Config Module** (`astrai/config/`): Model, training, scheduler, and other configurations
+- **Config Module** (`astrai/config/`): ModelConfig, TrainConfig
 - **Factory Module** (`astrai/factory/`): Registry, BaseFactory for component registration
 - **Parallel Module** (`astrai/parallel/`): Distributed training support
 - **Serialization** (`astrai/serialization.py`): HDF5 data loading, checkpoint management
 
-The data flow can generally be divided into two main lines: **Training Data Flow** and **Inference Data Flow**.
-
 ## Data Flow Diagram
 
 ```mermaid
@@ -23,38 +21,36 @@ flowchart LR
     subgraph A[Data Preparation]
         direction TB
         A1[Raw Text] --> A2[AutoTokenizer]
-        A2 --> A3[Serialize to .h5 files]
+        A2 --> A3[Tokenized .h5 files]
         A3 --> A4[BaseDataset]
         A4 --> A5[ResumableDistributedSampler]
-        A5 --> A6[PyTorch DataLoader]
+        A5 --> A6[DataLoader]
     end
 
     subgraph B[Training]
         direction TB
-        B1[Batch Data] --> B2[TrainContextBuilder]
-        B2 --> B3[TrainContext]
-        B3 --> B4[BaseStrategy]
-        B4 --> B5[Transformer]
-        B5 --> B6[Compute Loss]
-        B6 --> B7[Backward]
-        B7 --> B8[Optimizer]
-        B8 --> B9[LRScheduler]
-        B9 --> B10[CheckpointCallback]
+        B1[DataLoader] --> B2[BaseStrategy]
+        B2 --> B3[Transformer Forward]
+        B3 --> B4[Loss + Backward]
+        B4 --> B5[Gradient Accumulation]
+        B5 -->|every accum_steps| B6[Optimizer Step]
+        B6 --> B7[LR Scheduler]
+        B7 -->|next batch| B2
+        B6 --> B8[CheckpointCallback]
     end
 
     subgraph C[Inference]
         direction TB
         C1[Checkpoint] --> C2[AutoModel]
-        C2 --> C3[Transformer + Tokenizer]
-        C3 --> C4[GenerationRequest + apply_chat_template]
-        C4 --> C5[InferenceEngine]
-        C5 --> C6[InferenceScheduler]
+        C1 --> C3[AutoTokenizer]
+        C2 --> C4[InferenceEngine]
+        C3 --> C4
+        C4 --> C5[InferenceScheduler]
+        C5 --> C6[Transformer Forward]
         C6 --> C7[sample]
-        C7 --> C8[Transformer Forward]
-        C8 --> C9[Paged KV Cache]
-        C9 --> C10{End Condition?}
-        C10 -->|No| C8
-        C10 -->|Yes| C11[Output Text]
+        C7 --> C8{End?}
+        C8 -->|No| C6
+        C8 -->|Yes| C9[Generated Text]
     end
 
     A --> B
@@ -65,215 +61,177 @@ flowchart LR
 
 ### 1. Serialization (`astrai/serialization.py`)
 
-- **`save_h5`**: Saves multiple tensors by groups as HDF5 files (`.h5`), each key corresponds to a list of tensors
-- **`load_h5`**: Loads `.h5` files, returns `Dict[str, List[Tensor]]`, supports shared memory (`share_memory=True`)
-- **`Checkpoint` class**: Encapsulates model state dict, training epoch, iteration count; supports safetensors format for saving and loading
+- **`save_h5`**: Saves tensors by groups as HDF5 files (`.h5`), each key maps to a list of tensors
+- **`load_h5`**: Loads `.h5` files, returns `Dict[str, List[Tensor]]`, supports shared memory
+- **`Checkpoint`**: Encapsulates model state dict + epoch + iteration; uses safetensors
 
 ### 2. Dataset Module
 
 #### 2.1 Dataset (`dataset.py`)
-- **`BaseDataset`**: Abstract base class, defines common logic for window sampling, stride, etc.
-- **`BaseSegmentFetcher`** and **`MultiSegmentFetcher`**: Efficiently fetch data from specified index ranges in multiple segments
-- **`DatasetFactory`**: Factory pattern, supports dynamic registration of dataset types (`seq`, `sft`, `dpo`, `grpo`)
-- After dataset loading, multiple data keys (such as `"sequence"`, `"mask"`) are managed through `MultiSegmentFetcher`
+- **`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)
 
 #### 2.2 Sampler (`sampler.py`)
-- **`ResumableDistributedSampler`**: Resumable sampler supporting distributed training
-- Records current epoch and iteration position, enabling training resume from breakpoints
-- Supports shuffle and drop_last options
+- **`ResumableDistributedSampler`**: Tracks `epoch` and `iter` for breakpoint resume; supports shuffle and drop_last
 
 ### 3. Model Module
 
-#### 3.1 Transformer / AutoModel (`transformer.py`, `automodel.py`)
-- **`AutoModel`**: Base class for autoregressive language models with `from_pretrained()` and `save_pretrained()` methods
-- **`Transformer`**: Core autoregressive decoder architecture (registered via `@AutoModel.register('transformer')`)
-- Contains embedding layer, multi-layer `DecoderBlock`, RMSNorm, and linear output head
-- Supports weight tying (`tie_weight=True`) to reduce parameter count
-- Uses Rotary Position Embedding (RoPE) to inject position information
-- Supports loading from safetensors format with automatic model type detection from `config.json`
+#### 3.1 Transformer / AutoModel
+- **`AutoModel`**: Base class with `from_pretrained()` / `save_pretrained()`
+- **`Transformer`**: Decoder-only architecture, registered via `@AutoModel.register('transformer')`
+- Embedding → N×DecoderBlock → RMSNorm → Linear lm_head
+- RoPE position encoding, optional weight tying
 
 #### 3.2 Submodules (`module.py`)
-- **`RotaryEmbedding`**: Generates RoPE cos/sin cache
-- **`DecoderBlock`**: Contains multi-head attention (supports GQA and MLA), feedforward network (FFN), residual connections
-- **`GQA`**: Grouped Query Attention implementation
-- **`MLA`**: Multi-Latent Attention implementation (like Qwen2-VL)
-- **`MLP`**: Feed-forward network with SiLU activation and gated mechanism
-- **`RMSNorm`**: Layer normalization variant
-- **`Linear`**, **`Embedding`**: Custom linear layer and embedding layer, supporting parallelism wrappers
+- **`DecoderBlock`**: GQA attention + residual + MLP + RMSNorm
+- **`GQA`**: Grouped Query Attention (also `MLA` for multi-latent attention)
+- **`MLP`**: `SiLU(gate(x)) * up(x)` → down projection
+- **`RotaryEmbedding`**: RoPE cos/sin cache
+- **`RMSNorm`**: Layer normalization
 
 ### 4. Training Module
 
 #### 4.1 Training Context (`train_context.py`)
-- **`TrainContext`**: Data class encapsulating all components needed for training (model, optimizer, data loader, strategy, etc.)
-- **`TrainContextBuilder`**: Builder pattern, progressively assembles training context, supports resume from checkpoint
+- **`TrainContext`**: Dataclass holding model, optimizer, dataloader, strategy, scheduler, checkpoint state
+- **`TrainContextBuilder`**: Builder pattern — takes checkpoint for resume, builds all components
 
 #### 4.2 Trainer (`trainer.py`)
-- **`Trainer`**: Main training loop, manages callbacks (progress bar, checkpoint, metric logging, gradient clipping, scheduler)
-- Supports distributed training (launches multi-process via `spawn_parallel_fn`)
-- Training steps include:
-  1. `on_train_begin` → 2. `on_epoch_begin` → 3. `on_batch_begin` → 4. Forward/loss calculation → 5. `on_batch_end` → 6. Gradient accumulation → 7. `on_step_begin` → 8. Optimizer update → 9. `on_step_end` → 10. `on_epoch_end`
+
+The training loop is nested: **epoch** → **batch** (with step phase interspersed):
+
+```
+on_train_begin
+  on_epoch_begin
+    for each batch:
+      if iteration % accumulation_steps == 0:        ← step phase
+        on_step_begin → optimizer.step() → zero_grad → on_step_end
+                                                      ← batch phase
+      on_batch_begin → strategy(batch) → loss → backward → on_batch_end
+      iteration += 1
+
+    on_epoch_end
+on_train_end
+```
+
+Key points:
+- `on_step_*` wraps optimizer step (fires every `accumulation_steps` batches)
+- `on_batch_*` wraps loss computation (fires every batch)
+- `SchedulerCallback` fires on `on_batch_end` — LR scheduler steps every batch
+- `GradientClippingCallback` fires on `on_step_begin`
 
 #### 4.3 Strategy (`strategy.py`)
-- **`BaseStrategy`**: Defines training strategy interface
-- **`SEQStrategy`**: Standard next-token prediction training
-- **`SFTStrategy`**: Supervised Fine-tuning with loss masking
-- **`DPOStrategy`**: Direct Preference Optimization
-- **`GRPOStrategy`**: Group Relative Policy Optimization
-- Strategy receives batch data, executes model forward pass, loss calculation, returns loss tensor
-- Created dynamically by `StrategyFactory` according to configuration
+- **`SEQStrategy`**: Next-token prediction, cross-entropy with label smoothing
+- **`SFTStrategy`**: Supervised fine-tuning with loss masking
+- **`DPOStrategy`**: Direct Preference Optimization with reference model
+- **`GRPOStrategy`**: Group Relative Policy Optimization with clipped ratio
 
 #### 4.4 Scheduler (`schedule.py`)
-- **`BaseScheduler`**: Abstract base class defining learning rate scheduling interface
-- **`CosineScheduler`**: Cosine decay scheduler with warmup
-- **`SGDRScheduler`**: Stochastic Gradient Descent with Warm Restarts
-- **`SchedulerFactory`**: Factory pattern, supports registration of various schedulers
-- Scheduler is automatically created according to configuration and bound to optimizer
+- **`CosineScheduler`**: Cosine decay + linear warmup
+- **`SGDRScheduler`**: Cosine annealing with warm restarts
+- Created by `SchedulerFactory` and bound to optimizer
 
-#### 4.5 Callbacks (`train_callback.py`)
-- **`TrainCallback`**: Protocol interface for trainer callbacks
-- **`CheckpointCallback`**: Saves model checkpoints at configurable intervals
-- **`ProgressBarCallback`**: Displays training progress
-- **`MetricLoggerCallback`**: Logs training metrics to JSON files
-- **`GradientClippingCallback`**: Clips gradient norms
-- **`SchedulerCallback`**: Steps learning rate scheduler
+#### 4.5 Callbacks
+- **`CheckpointCallback`**: Saves safetensors at `ckpt_interval` iterations
+- **`ProgressBarCallback`**: tqdm progress display
+- **`MetricLoggerCallback`**: Writes JSONL metrics to `{ckpt_dir}/logs/`
+- **`GradientClippingCallback`**: `clip_grad_norm_` on `on_step_begin`
+- **`SchedulerCallback`**: `scheduler.step()` on `on_batch_end`
 
-#### 4.6 Metric Utility (`metric_util.py`)
-- **`MetricTracker`**: Tracks and aggregates training metrics across epochs
-- **`get_learning_rate`**: Utility to extract current learning rates from optimizer param groups
+### 5. Inference Module
 
-### 5. Factory Module
+#### 5.1 Inference Engine (`engine.py`)
+- **`InferenceEngine`**: Facade over scheduler; provides `generate()`, `generate_with_request()`, `generate_async()`
+- Accepts `prompt: str | List[str]`, returns generator (stream) or string (non-stream)
 
-#### 5.1 Registry and BaseFactory (`factory.py`)
-- **`Registry`**: Flexible registry for component classes with category and priority support
-- **`BaseFactory`**: Generic factory class for component registration and creation
-- Supports decorator-based registration pattern for extensible components
-- Provides methods for registration, retrieval, and listing with filtering
+#### 5.2 Scheduler 4-Phase Loop (`scheduler.py`)
 
-### 6. Parallel Module
+Background thread runs continuously:
 
-#### 6.1 Setup (`setup.py`)
-- **`spawn_parallel_fn`**: Spawns multiple processes for distributed training using PyTorch multiprocessing
-- **`setup_parallel`**: Context manager for initializing distributed process group (NCCL/CCL backend)
-- **`only_on_rank`**: Decorator to execute functions only on specific ranks
-- **`get_rank`**: Returns current process rank in distributed group
-- **`get_world_size`**: Returns total number of processes in distributed group
-- **`get_current_device`**: Returns current device from environment
+```
+1. Cleanup → Remove finished tasks, free KV cache pages
+2. Refill  → Pop from waiting_queue, alloc pages, add to active
+3. Prefill → Group active tasks by prompt_len, run full forward pass
+4. Decode  → Pick largest same-position group, run single-token forward
+```
 
-#### 6.2 Parallel Layers (`module.py`)
-- **`ParallelModel`**: Base class for parallel models with process group
-- **`ColumnParallelLinear`**: Column-parallel linear layer with input splitting and output gathering
-- **`RowParallelLinear`**: Row-parallel linear layer with output reduction
+- **`Task`**: Tracks prompt_ids, output_ids, page_table, status (PENDING/RUNNING/FINISHED/ABORTED)
+- **`PagedCache`**: Bitmask-based page allocator with page-table-indirected read/write
+- **`CacheView`**: Batch view bundling cache + page table for attention layers
+- **`sample()`**: Temperature → top-k → top-p → multinomial
 
-### 7. Inference Module
+#### 5.3 Server (`server.py`)
+- FastAPI with OpenAI `/v1/chat/completions` and Anthropic `/v1/messages` endpoints
+- Streaming via SSE, health check at `/health`, stats at `/stats`
 
-#### 7.1 Inference Engine (`engine.py`)
-- **`InferenceEngine`**: Unified inference interface, supports streaming, async streaming, and non-streaming generation
-- **`InferenceScheduler`**: Continuous batching scheduler with paged KV cache
-- **`GenerationRequest`**: Encapsulates generation parameters (top_k, top_p, temperature, max_len, messages, etc.)
-- **`GenerationParams`**: Immutable value object for sampling hyperparameters
-- **`messages` format**: List of message dictionaries with `role` (system/user/assistant) and `content`
-- **`apply_chat_template`** (from `tokenizer.py`): Converts messages into prompt string using ChatML format
-- Provides streaming (`stream=True`), async streaming (`generate_async`), and non-streaming (`stream=False`) generation interfaces
-- Supports continuous batching with `max_batch_size` and `max_seq_len` parameters
-- Uses separate model and tokenizer initialization for flexibility
+### 6. Tokenizer Module
 
-#### 7.2 Cache (`cache.py`)
-- **`PagedCache`**: Page-based KV cache with page-table-indirected read/write; uses bitmask for O(1) page allocation/deallocation
-- **`CacheView`**: Per-batch view bundling a `PagedCache` with its page table for attention layer access
+- **`AutoTokenizer`**: Wraps HuggingFace tokenizers (BBPE); `encode`/`decode`/`apply_chat_template`
+- **`ChatTemplate`**: Jinja2-based template rendering for multi-turn chat
 
-#### 7.3 Scheduler (`scheduler.py`)
-- **`Task`**: Individual generation task with state management (PENDING, RUNNING, FINISHED, ABORTED)
-- **`TaskStatus`**: Task state enumeration
-- **`sample`** (from `sampling.py`): Applies temperature, top-k, top-p sampling to logits via composable `SamplingPipeline`
-- Uses `PagedCache` for paged KV cache management with page table indirection
-- Continuous batching: new requests can join at any time, completed requests release pages immediately
+### 7. Factory & Parallel
 
-#### 7.4 Server (`server.py`)
-- FastAPI-based HTTP inference server
-- OpenAI-compatible `/v1/chat/completions` endpoint
-- Health check and statistics endpoints
-- Supports both streaming and non-streaming responses
+- **`Registry` / `BaseFactory`**: Decorator-based component registration
+- **`spawn_parallel_fn`**: Multi-process DDP launcher with NCCL backend
+- **`ParallelModel` / `ColumnParallelLinear` / `RowParallelLinear`**: Tensor model parallelism
 
-### 8. Tokenizer Module
-
-#### 8.1 Tokenizer (`tokenizer.py`)
-- Implemented based on HuggingFace tokenizers library (Byte-Level BPE)
-- **`AutoTokenizer`**: Auto-loading tokenizer class
-- Supports special tokens: `<｜begin▁of▁sentence｜>`, `<｜end▁of▁sentence｜>`, `<｜▁pad▁｜>`, `<｜im▁start｜>`, `<｜im▁end｜>`
-- Provides `encode`/`decode` methods for mutual conversion between text and token IDs
-- Uses `AutoTokenizer` for loading pre-trained tokenizers
-
-#### 8.2 Chat Template (`chat_template.py`)
-- **`ChatTemplate`**: Jinja2-based chat template with rendering support
-- Handles multi-role message formatting (system, user, assistant)
-- Supports dynamic prompts and generation prompts
-
-## Training Data Flow - Detailed Steps
+## Training Data Flow — Detailed Steps
 
 1. **Data Preparation**
-   - Raw text is converted to token ID sequences through AutoTokenizer
-   - Token ID sequences (possibly with masks, labels, etc.) are saved by groups as `.h5` files
-   - Files can contain multiple segments, each segment corresponds to a tensor
+   - Raw text → token IDs via `AutoTokenizer.encode()`
+   - Save as `.h5` files (groups of tensor lists per data key)
 
 2. **Dataset Loading**
-   - `BaseDataset`'s `load` method calls `load_h5`, obtaining `segments` dictionary
-   - Create `MultiSegmentFetcher` to manage data for multiple keys
-   - Calculate total sample count, and determine start/end indices for each sample based on window size and stride
+   - `BaseDataset.load()` calls `load_h5()`, builds `MultiSegmentFetcher`
+   - Sliding window of `window_size` with `stride` determines sample boundaries
 
-3. **Sampling and Batch Loading**
-   - `ResumableDistributedSampler` generates index sequence based on current epoch and iteration position
-   - PyTorch `DataLoader` uses sampler to get indices, calls dataset's `__getitem__` to get actual data
-   - Batch data shape is `[batch_size, window_size]` (or varies according to specific dataset type)
+3. **Sampling & Batching**
+   - `ResumableDistributedSampler` produces shuffled index sequences
+   - `DataLoader` fetches `[batch_size, window_size]` tensors via `__getitem__`
 
-4. **Strategy Forward and Loss Calculation**
-   - Batch data is passed to strategy (such as `SEQStrategy`)
-   - Strategy internally calls `Transformer` model, obtaining logits
-   - Calculate cross-entropy loss (or DPO loss, etc.) according to task type
-   - Return loss tensor
+4. **Strategy Forward**
+   - Strategy receives batch, calls `Transformer.forward()` for logits
+   - Computes task-specific loss (cross-entropy, DPO, GRPO)
 
-5. **Backpropagation and Optimization**
-   - Loss is normalized by dividing by accumulation steps, then `loss.backward()` is executed
-   - After accumulating `accumulation_steps` batches, optimizer `step()` and `zero_grad()` are executed
-   - Learning rate scheduler updates learning rate after each step
+5. **Backward & Accumulation**
+   - `loss = raw_loss / accumulation_steps`
+   - `loss.backward()` accumulates gradients
+   - Every `accumulation_steps` batches: `optimizer.step()` → `zero_grad()`
+   - Every batch: `scheduler.step()` updates learning rate
 
-6. **Checkpoint Saving**
-   - `CheckpointCallback` saves checkpoints at set intervals
-   - Checkpoints contain model state dict, current epoch, iteration, and other metadata
-   - Saved in safetensors format, ensuring safety and efficiency
+6. **Checkpoint**
+   - `CheckpointCallback` saves `model.state_dict()` + metadata to safetensors at `ckpt_interval` iterations
+   - Does NOT save optimizer/scheduler state (resume resets those)
 
-## Inference Data Flow - Detailed Steps
+## Inference Data Flow — Detailed Steps
 
 1. **Model Loading**
-   - Load `Transformer` model from checkpoint via `AutoModel.from_pretrained()`
-   - Set model to evaluation mode (`model.eval()`), enable inference mode (`torch.inference_mode`)
+   - `AutoModel.from_pretrained(path)` loads weights from safetensors
+   - `torch.inference_mode()` wraps generation
 
-2. **Prompt Construction and Encoding**
-   - User messages (list of dict with role and content) are converted to ChatML format string through `apply_chat_template` method in tokenizer
-   - Tokenizer encodes prompt string to token ID sequence `input_ids`
-   - For batch generation, use `pad_sequence` for padding
+2. **Prompt Construction**
+   - Messages → `apply_chat_template(messages, tokenize=False)` → prompt string
+   - `tokenizer.encode(prompt)` → token IDs (truncated to `max_prompt_len`)
 
-3. **Autoregressive Generation Loop**
-   - Scheduler allocates pages via `PagedCache.alloc_n()` for each task's prompt
-   - Prefill phase: runs full prompt through model with `PagedCache.bind()` to fill initial KV cache pages
-   - Decode phase: loops until generating `max_len` tokens or encountering stop token:
-     - Input last token ID to model, obtain `logits`
-     - Apply `sample()` (temperature, top-k, top-p) to `logits`
-     - Sample next token ID from the processed distribution
-     - Write new KV entries into paged cache; allocate additional pages as needed
-     - For streaming generation, yield each token to caller immediately via `stream_callback`
+3. **Continuous Batching Loop**
+   - **Cleanup**: Finished tasks → `stream_callback(STOP)`, free KV pages
+   - **Refill**: Pop from waiting queue, `PagedCache.alloc_n()` for prompt pages
+   - **Prefill**: Group by prompt length, run full forward with `start_pos=0`
+   - **Decode**: Pick position group with most tasks, single-token forward:
+     - Model forward → `logits` → `sample()` → next token ID
+     - Append to `output_ids`, update `output_tokens`
+     - `_maybe_alloc_page()` grows page table as needed
+     - `stream_callback(token)` for streaming clients
 
-4. **Decoding and Output**
-   - Decode generated token ID sequence to text through tokenizer
-   - Remove special tokens, return plain text response
+4. **Output**
+   - `tokenizer.decode(output_ids)` → text
+   - Return to caller (streaming: token-by-token; non-streaming: complete string)
 
-## Checkpoint and Serialization
+## Checkpoint & Serialization
 
-- **Training Checkpoint**: Saves model parameters, optimizer state, scheduler state, current epoch and iteration
-- **Model Parameters**: Supports safetensors format, automatically handles special logic like weight tying during loading
-- **Dataset Serialization**: HDF5 format supports efficient random access and shared memory, suitable for large-scale pre-training data
+- **Training Checkpoint**: safetensors weights + epoch/iteration metadata. Optimizer/scheduler state is NOT persisted.
+- **Inference Loading**: `AutoModel.from_pretrained()` loads from the same safetensors format.
+- **Dataset Serialization**: HDF5 with shared memory support for large-scale pre-training data.
 
-## Summary
-
-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 paged KV cache, continuous batching, and composable sampling strategies. Clear interfaces between modules facilitate customization and extension.
-
-> Document Update Time: 2026-04-09
+> Document Update Time: 2026-05-09