chore: 版本号升至 1.3.5

README.md

@@ -65,6 +65,16 @@ For development dependencies:
 pip install -e ".[dev]"
 ```
 
+#### Download Pre-trained Model
+
+Download pre-trained model weights (1B bilingual checkpoint) to `params/`:
+
+```bash
+python scripts/demo/download.py
+```
+
+Or download manually from [HuggingFace](https://huggingface.co/ViperEk/KHAOSZ) into `params/`.
+
 #### Train a Model
 
 ```bash

assets/docs/README-zh-CN.md

@@ -71,6 +71,16 @@ pip install -e .
 pip install -e ".[dev]"
 ```
 
+#### 下载预训练模型
+
+下载预训练模型权重（1B 双语检查点）到 `params/` 目录：
+
+```bash
+python scripts/demo/download.py
+```
+
+或从 [HuggingFace](https://huggingface.co/ViperEk/KHAOSZ) 手动下载放入 `params/`。
+
 #### 训练模型
 
 ```bash

assets/docs/dataflow.md

@@ -88,7 +88,7 @@ flowchart LR
 - **`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
+- **`RotaryEmbedding`**: RoPE complex cache (freqs_cis)
 - **`RMSNorm`**: Layer normalization
 
 ### 4. Training Module
@@ -104,22 +104,23 @@ The training loop is nested: **epoch** → **batch** (with step phase interspers
 ```
 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
+    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
+          iteration += 1
+      on_step_end
+      optimizer.step() → zero_grad
 
     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`
+- `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)
 
 #### 4.3 Strategy (`strategy.py`)
 - **`SEQStrategy`**: Next-token prediction, cross-entropy with label smoothing
@@ -136,8 +137,7 @@ Key points:
 - **`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`
+- **`GradientClippingCallback`**: `clip_grad_norm_` on `on_step_end`
 
 ### 5. Inference Module
 

assets/docs/design.md

@@ -91,8 +91,8 @@ classDiagram
         }
 
         class BaseStorage {
-            +Dict segments
-            +List keys
+            +MultiSegmentFetcher _fetcher
+            +keys (property)
             +load(load_path, tokenizer)
             +fetch(begin, end, keys)
             +__len__()
@@ -145,7 +145,7 @@ classDiagram
             +ModelConfig config
             +Registry _registry
             +register(model_type) decorator
-            +get_model_class(model_type) Type
+            +get_component_class(model_type) Type
             +from_pretrained(path, disable_random_init) nn.Module
             +save_pretrained(save_directory)
             +to(*args, **kwargs) Self
@@ -214,7 +214,7 @@ classDiagram
             +int dim
             +int max_len
             +float base
-            +forward(x, position_ids=None) Tuple[Tensor, Tensor]
+            +forward(x, position_ids=None) Tensor
         }
 
         class Embedding {
@@ -225,13 +225,10 @@ classDiagram
 
     namespace tokenize {
         class AutoTokenizer {
-            +List[int] stop_ids
-            +int bos_id
-            +int eos_id
-            +int pad_id
             +vocab_size int
             +encode(tokens, out_ids, add_special_tokens) List[int]
             +decode(tokens, skip_special_tokens) str
+            +__getattr__(name) Any (bos_id, eos_id, pad_id, stop_ids)
             +apply_chat_template(messages, tokenize) Union[str, List[int]]
             +set_chat_template(template)
             +load(path)
@@ -325,6 +322,8 @@ classDiagram
             +float clip_eps
             +float kl_coef
             +int group_size
+            +str reduction
+            +int sync_interval
             +compute_loss(batch) Tensor
         }
 
@@ -369,11 +368,6 @@ classDiagram
             +on_step_begin(context)
         }
 
-        class SchedulerCallback {
-            +on_train_begin(context)
-            +on_batch_end(context)
-        }
-
         class CheckpointCallback {
             +str save_dir
             +int interval
@@ -409,8 +403,6 @@ classDiagram
             +nn.Module model
             +AutoTokenizer tokenizer
             +InferenceScheduler scheduler
-            +int max_batch_size
-            +Optional int max_seq_len
             +generate(prompt, stream, max_tokens, temperature, top_p, top_k) Union[Generator, str, List[str]]
             +generate_with_request(request) Union[Generator, str, List[str]]
             +generate_async(prompt, max_tokens, temperature, top_p, top_k) AsyncGenerator
@@ -421,13 +413,12 @@ classDiagram
         class InferenceScheduler {
             +nn.Module model
             +AutoTokenizer tokenizer
-            +KVCache page_cache
+            +KVCache _page_cache
             +int max_batch_size
             +int max_seq_len
             +int max_prompt_len
             +int page_size
-            +List waiting_queue
-            +List active_tasks
+            +TaskManager _task_mgr
             +add_task(prompt, max_tokens, temperature, top_p, top_k, stream_callback) str
             +remove_task(task_id)
             +start()
@@ -568,7 +559,7 @@ classDiagram
         }
 
         class GenerateResult {
-            +List[str] tokens
+            +List[Tuple[int, str]] tokens
             +List[str] results
             +List[bool] _done
             +append(token, idx)
@@ -643,7 +634,6 @@ classDiagram
     BaseScheduler <|-- SGDRScheduler
     CallbackFactory ..> TrainCallback : creates
     TrainCallback <|-- GradientClippingCallback
-    TrainCallback <|-- SchedulerCallback
     TrainCallback <|-- CheckpointCallback
     TrainCallback <|-- ProgressBarCallback
     TrainCallback <|-- MetricLoggerCallback

astrai/__init__.py

@@ -1,4 +1,4 @@
-__version__ = "1.3.4"
+__version__ = "1.3.5"
 __author__ = "ViperEkura"
 
 from astrai.config import (