docs: audit non-CUDA documentation

- Aligns CLI and strategy metric contracts - Refreshes architecture, dataflow, preprocessing, distributed, and eval guides - Corrects links, TOCs, defaults, and repository paths
2026-08-02 07:39:24 +08:00
parent 020e2eff4e
commit 288ba20db1
13 changed files with 483 additions and 267 deletions
@@ -62,6 +62,8 @@

 **1. 安装**

+AstrAI 需要 Python 3.12+，并精确固定 PyTorch 版本为 `2.11.0`。训练、`scripts/tools/generate.py`、生成式评估和生成演示需要 CUDA；CPU 支持仅适用于提供明确 CPU 设备路径的组件，例如 HTTP 服务和直接打分评估。
+
 ```bash
 git clone https://github.com/ViperEkura/AstrAI.git
 cd AstrAI
@@ -138,7 +140,7 @@ curl http://localhost:8000/v1/chat/completions \
 # 下载模型权重（运行演示前必需）
 python scripts/demo/download.py                      # model → params/

-# 交互式流式聊天（多轮对话，保持历史记录）
+# 单轮交互式流式提示循环（不保留对话历史）
 python scripts/demo/stream_chat.py
 # 在 >> 后输入消息，输入 !exit 退出

@@ -189,7 +191,7 @@ docker run --gpus all -v /path/to/data:/data -it astrai:latest
 # Docker Compose（GPU，默认）
 docker compose up -d

-# Docker Compose（仅 CPU）
+# Docker Compose CPU 服务配置（不支持仅限 CUDA 的生成脚本和演示）
 docker compose --profile cpu up -d
 ```

@@ -239,7 +241,7 @@ SSE 流式格式、错误码和统计端点详见[推理文档](guides/inference

 ### 贡献

-我们欢迎贡献！请参阅[贡献指南](../../CONTRIBUTING.md)了解详情。
+我们欢迎贡献！请参阅[贡献指南](../CONTRIBUTING.md)了解详情。

 1. Fork 本仓库。
 2. 创建功能分支。
@@ -256,10 +258,10 @@ SSE 流式格式、错误码和统计端点详见[推理文档](guides/inference

 ### 许可证

-本项目采用 [GPL-3.0 许可证](../../LICENSE)。
+本项目采用 [GPL-3.0 许可证](../LICENSE)。

 ---

 <div align="center">
  <em>专为高性能与易用性设计的轻量级 Transformer 框架。</em>
-</div>
+</div>
@@ -4,7 +4,7 @@

 - [Class Diagram](#class-diagram) — Full Mermaid class diagram across 10+ namespaces
 - [Module Overview](#module-overview) — Component inventory per module
- [Design Patterns](#design-patterns) — 13 documented patterns with classes
+- [Design Patterns](#design-patterns) — 15 documented patterns with classes
 - [Core Relationships](#core-relationships) — 11 key inter-component relationships

 ## Class Diagram
@@ -49,6 +49,11 @@ classDiagram
            +Optional[int] n_shared_experts
            +Optional[int] n_activated_experts
            +Optional[str] topk_method
+            +Optional[int] moe_intermediate_size
+            +Optional[int] shared_expert_intermediate_size
+            +bool norm_topk_prob
+            +int decoder_sparse_step
+            +Optional[List[int]] mlp_only_layers
        }

        class EncoderConfig {
@@ -63,6 +68,7 @@ classDiagram
            +Optional[int] num_attention_heads
            +Optional[int] num_key_value_heads
            +Optional[bool] use_qk_norm
+            +Optional[bool] use_gated_attention
            +str ffn_type
            +Optional[dict] rope_scaling
            +Optional[str] pooling_type
@@ -114,22 +120,25 @@ classDiagram
            +Dataset dataset
            +Callable optimizer_fn
            +Callable scheduler_fn
+            +Optional[str] optimizer_name
+            +Dict[str, Any] optimizer_hyperparameters
            +int n_epoch
            +int batch_per_device
            +int grad_accum_steps
            +Optional[float] max_grad_norm
            +list gradient_checkpointing_modules
+            +Optional[str] compile_mode
            +int start_epoch
            +int start_samples
            +str ckpt_dir
            +int ckpt_interval
-            +str log_dir
            +List[str] metrics
            +Optional[LoRAConfig] lora
            +int random_seed
            +int num_workers
            +Optional[int] prefetch_factor
            +bool pin_memory
+            +Optional[Callable] collate_fn
            +int nprocs
            +str backend
            +str master_addr
@@ -140,6 +149,7 @@ classDiagram
            +Optional[float] val_split
            +int val_step
            +float neftune_alpha
+            +float moe_aux_loss_coef
            +str parallel_mode
            +int rollout_interval
            +float rollout_temperature
@@ -149,7 +159,6 @@ classDiagram
            +Optional[Callable] reward_model_fn
            +dict executor_kwargs
            +dict extra_kwargs
-            +validate()
        }

    }
@@ -205,10 +214,6 @@ classDiagram
            -_fetch_record_key(key, index) Tensor
        }

-        class H5Store {
-            +load(path)
-        }
-
        class MmapStore {
            +List _mmap_refs
            +load(path)
@@ -260,11 +265,15 @@ classDiagram
    }

    namespace model {
-        class AutoModel {
-            +BaseModelConfig config
+        class ModelFactory {
            +Dict _entries
            +register(name) decorator
            +get_component_class(name) Type
+        }
+
+        class AutoModel {
+            <<nn.Module>>
+            +BaseModelConfig config
            +from_pretrained(path, disable_random_init, strict) nn.Module
            +save_pretrained(save_directory)
            +to(*args, **kwargs) Self
@@ -299,7 +308,13 @@ classDiagram
            +RMSNorm input_norm
            +nn.Module mlp        # MLP or DeepSeekMoE via FFNFactory
            +RMSNorm post_attention_norm
-            +forward(x, rotary_emb, attention_mask, kv_cache) Tensor
+            +forward(x, rotary_emb, attention_mask, kv_cache, is_causal) DecoderOutput
+        }
+
+        class DecoderOutput {
+            <<TypedDict>>
+            +Tensor hidden_states
+            +Optional[Tensor] aux_loss
        }

        class GQA {
@@ -314,7 +329,7 @@ classDiagram
            +Linear q_proj, k_proj, v_proj, o_proj
            +Linear gate  # only if use_gated_attention
            +RMSNorm q_norm, k_norm  # only if use_qk_norm
-            +forward(x, rotary_emb, attn_mask, kv_cache) Tensor
+            +forward(x, rotary_emb, attn_mask, kv_cache, is_causal) Tensor
        }

        class MLA {
@@ -334,12 +349,18 @@ classDiagram
            +Linear gate  # only if use_gated_attention
            +RMSNorm kv_norm
            +RMSNorm q_norm, k_norm  # only if use_qk_norm
-            +forward(x, rotary_emb, attn_mask, kv_cache) Tensor
+            +forward(x, rotary_emb, attn_mask, kv_cache, is_causal) Tensor
        }

        class MLP {
            +Linear up, gate, down
-            +forward(x) Tensor
+            +forward(x) FFNOutput
+        }
+
+        class FFNOutput {
+            <<TypedDict>>
+            +Tensor hidden_states
+            +Optional[Tensor] aux_loss
        }

        class DeepSeekMoE {
@@ -351,7 +372,7 @@ classDiagram
            +Linear router
            +ModuleList shared_experts
            +ModuleList routed_experts
-            +forward(x) Tensor
+            +forward(x) FFNOutput
        }

        class AttnFactory {
@@ -380,9 +401,8 @@ classDiagram
            +int max_len
            +float base
            +Optional[Dict] rope_scaling
-            +Tensor cos_table
-            +Tensor sin_table
-            +forward(x, position_ids=None) Tuple[Tensor, Tensor]
+            +Tensor freqs_cis
+            +forward(x, position_ids=None) Tensor
        }

        class Embedding {
@@ -486,10 +506,6 @@ classDiagram
            +save(output_dir, domain, shard_idx, tensors)
        }

-        class H5Writer {
-            +save(output_dir, domain, shard_idx, tensors)
-        }
-
        class Pipeline {
            +PipelineConfig config
            +List[str] paths
@@ -559,7 +575,7 @@ classDiagram
        class Trainer {
            +TrainConfig train_config
            +List[TrainCallback] callbacks
-            +train(resume_dir)
+            +train(param_path=None, resume=False)
            -_get_default_callbacks() List[TrainCallback]
        }

@@ -576,13 +592,17 @@ classDiagram
            +int epoch
            +int consumed_samples
            +float loss
-            +float grad_norm
+            +Dict[str, float] metrics
+            +Optional[float] grad_norm
+            +GradSNRTracker grad_snr_tracker
            +DataLoader val_dataloader
-            +float val_loss
+            +Optional[float] val_loss
            +int world_size
            +int rank
            +dict kwargs
-            +optimizer_step() int
+            +stop_requested (property) bool
+            +optimizer_step (property) int
+            +request_stop()
        }

        class TrainContextBuilder {
@@ -594,11 +614,22 @@ classDiagram
        class BaseStrategy {
            +Callable model
            +Optional[BaseExecutor] executor
-            +Optional[Callable] model_fn
+            +float moe_aux_loss_coef
            +dict extra_kwargs
            +str device
-            +__call__(batch) Tensor
+            +__call__(batch) LossOutput
            +compute_loss(batch) Tensor
+            +compute_loss_output(batch) LossOutput
+            +supports_online() bool
+            +set_rollout_runner(runner)
+            +prepare_from_rollout(result) Dict
+            +on_optimizer_step()
+        }
+
+        class LossOutput {
+            <<TypedDict>>
+            +Tensor loss
+            +Dict[str, float] metrics
        }

        class StrategyFactory {
@@ -636,9 +667,12 @@ classDiagram

        class RawRollout {
            +Tensor prompts
+            +Tensor prompt_mask
            +Tensor responses
            +Tensor response_mask
            +Tensor logprobs_old
+            +List[str] prompt_texts
+            +List[List[str]] response_texts
        }

        class RolloutResult {
@@ -647,10 +681,18 @@ classDiagram

        class BaseRewardModel {
            <<abstract>>
-            +score(prompts, responses) Tensor
+            +score(List[str] prompts, List[List[str]] responses) Tensor
        }

        class RolloutGenerator {
+            +InferenceScheduler scheduler
+            +int max_tokens
+            +int group_size
+            +float temperature
+            +int top_k
+            +float top_p
+            +float frequency_penalty
+            +int rep_window
            +generate(batch) RawRollout
        }

@@ -740,7 +782,7 @@ classDiagram
        }

        class MetricCallback {
-            +Path log_dir
+            +Path ckpt_dir
            +int save_interval
            +List[str] metrics
            +int val_step
@@ -764,9 +806,9 @@ classDiagram
            +nn.Module model
            +AutoTokenizer tokenizer
            +InferenceScheduler scheduler
-            +generate(prompt, stream, max_tokens, temperature, top_p, top_k) Union[Generator, str, List[str]]
+            +generate(prompt, stream, max_tokens, temperature, top_p, top_k, frequency_penalty, rep_window) 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
+            +generate_async(prompt, max_tokens, temperature, top_p, top_k, frequency_penalty, rep_window) AsyncGenerator
            +get_stats() Dict
            +shutdown()
        }
@@ -774,18 +816,18 @@ classDiagram
        class Executor {
            +AutoModel model
            +AutoTokenizer tokenizer
-            +KVCache page_cache
+            +PagePool kv_cache
            +Optional[str] device
            +Optional[torch.dtype] dtype
            +execute_prefill(tasks, prompt_len, start_pos)
-            +execute_decode(tasks) List[int]
+            +execute_decode(tasks, return_logprobs=False) Union[List[int], List[Tuple[int, float]]]
        }

        class InferenceScheduler {
-            +KVCache _page_cache
+            +PagePool _cache
            +Executor _executor
            +TaskManager _task_mgr
-            +bool _running
+            +Event _stop_event
            +Thread _loop_thread
            +int max_seq_len
            +str device
@@ -795,6 +837,7 @@ classDiagram
            +start()
            +stop()
            +get_stats() Dict
+            +run_batch(prompt_ids_list, max_tokens, temperature, top_p, top_k, frequency_penalty, rep_window, return_logprobs) Union[List[List[int]], List[Tuple[List[int], List[float]]]]
        }

        class Allocator {
@@ -816,16 +859,6 @@ classDiagram
            +record(page_idx, token_ids, logical_page_idx)
        }

-        class PagePool {
-            -Allocator _alloc
-            -PrefixCache _prefix
-            +alloc() int
-            +free(idx)
-            +inc_ref(idx)
-            +lookup(token_ids) List[int]
-            +record(page_idx, token_ids, logical_page_idx)
-        }
-
        class KVStorage {
            +int size
            +Tensor k_buffer
@@ -852,8 +885,7 @@ classDiagram
            +Tensor seq_lens
            +Tensor out_cache_loc
            +int max_len
-            +Optional[Tensor] page_table
-            +Optional[Tensor] decode_mask
+            +Optional[Tensor] kv_indptr
        }

        class PagePool {
@@ -878,6 +910,8 @@ classDiagram
        +float temperature
        +float top_p
        +int top_k
+        +float frequency_penalty
+        +int rep_window
        +TaskStatus status
        +List output_ids
        +int input_tokens
@@ -923,27 +957,29 @@ classDiagram
            +float top_p
            +float temperature
            +Optional[int] max_tokens
+            +float frequency_penalty
+            +int rep_window
            +bool stream
        }

        class BaseSamplingStrategy {
            <<abstract>>
-            +apply(logits, filter_value) Tensor
+            +apply(logits, filter_value, input_ids, input_mask) Tensor
        }

        class TemperatureStrategy {
            +float temperature
-            +apply(logits, filter_value) Tensor
+            +apply(logits, filter_value, input_ids, input_mask) Tensor
        }

        class TopKStrategy {
            +int top_k
-            +apply(logits, filter_value) Tensor
+            +apply(logits, filter_value, input_ids, input_mask) Tensor
        }

        class TopPStrategy {
            +float top_p
-            +apply(logits, filter_value) Tensor
+            +apply(logits, filter_value, input_ids, input_mask) Tensor
        }

        class FrequencyPenaltyStrategy {
@@ -953,8 +989,8 @@ classDiagram

        class SamplingPipeline {
            +List[BaseSamplingStrategy] strategies
-            +apply(logits, filter_value) Tensor
-            +sample(logits, filter_value) Tensor
+            +apply(logits, filter_value, input_ids, input_mask) Tensor
+            +sample(logits, filter_value, input_ids, input_mask, return_logprobs) Union[Tensor, Tuple[Tensor, Tensor]]
        }

        class StreamDecoder {
@@ -1029,7 +1065,7 @@ classDiagram
            <<abstract>>
            +prepare(request, engine) Tuple[str, GenContext, List[str]]
            +format_stream_start(ctx) List[str]
-            +format_chunk(token) List[str]
+            +format_chunk(token, **kwargs) List[str]
            +format_stream_end(ctx, stop) List[str]
            +format_response(ctx, content, stop) Dict
        }
@@ -1037,7 +1073,7 @@ classDiagram
        class OpenAIResponseBuilder {
            +prepare(request, engine) Tuple
            +format_stream_start(ctx) List[str]
-            +format_chunk(token) List[str]
+            +format_chunk(token, **kwargs) List[str]
            +format_stream_end(ctx, stop) List[str]
            +format_response(ctx, content, stop) Dict
        }
@@ -1045,7 +1081,7 @@ classDiagram
        class AnthropicResponseBuilder {
            +prepare(request, engine) Tuple
            +format_stream_start(ctx) List[str]
-            +format_chunk(token) List[str]
+            +format_chunk(token, **kwargs) List[str]
            +format_stream_end(ctx, stop) List[str]
            +format_response(ctx, content, stop) Dict
        }
@@ -1153,10 +1189,13 @@ classDiagram

        class BaseExecutor {
            +GradientState gradient_state
-            +prepare(model_fn, optimizer_fn, scheduler_fn, before_wrap) tuple
+            +prepare(model_fn, optimizer_fn, scheduler_fn, before_wrap, after_wrap) tuple
            +accumulate(model) context manager
            +backward(loss)
            +unwrap_model(model) dict
+            +checkpoint_context(model) context manager
+            +clip_grad_norm(model, max_norm) float
+            +use_distributed (property) bool
            +sync_gradients (property) bool
            +grad_accum_steps (property) int
        }
@@ -1173,7 +1212,8 @@ classDiagram
        class FSDPExecutor {
            -_prepare_model(model) nn.Module
            -_no_sync(model) context manager
-            +unwrap_model(model) dict
+            +unwrap_model(model) Optional[dict]
+            +clip_grad_norm(model, max_norm) float
        }

        class ExecutorFactory {
@@ -1182,33 +1222,6 @@ classDiagram
            +create(parallel_mode, **kwargs) BaseExecutor
        }

-        class ParallelModel {
-            +dist.ProcessGroup process_group
-            +int rank
-            +int world_size
-        }
-
-        class ColumnParallelLinear {
-            +int in_features
-            +int out_features
-            +int out_features_per_rank
-            +bool gather_results
-            +Parameter weight
-            +Optional[Parameter] bias
-            +forward(x) Tensor
-            +load_state_dict(state_dict)
-        }
-
-        class RowParallelLinear {
-            +int in_features
-            +int out_features
-            +int in_features_per_rank
-            +bool reduce_results
-            +Parameter weight
-            +Optional[Parameter] bias
-            +forward(x) Tensor
-            +load_state_dict(state_dict)
-        }
    }

    %% Relationships — UML notation: <|-- generalization, *-- composition, o-- aggregation, --> association, ..> dependency
@@ -1230,11 +1243,8 @@ classDiagram
    BaseDataset <|-- SFTDataset
    BaseDataset <|-- DPODataset
    BaseDataset <|-- GRPODataset
-    Store <|-- H5Store
    Store <|-- MmapStore
    Store <|-- JsonlStore
-    H5Store --|> Streamable
-    H5Store --|> Recordable
    MmapStore --|> Streamable
    MmapStore --|> Recordable
    JsonlStore --|> Streamable
@@ -1243,8 +1253,6 @@ classDiagram
    BaseSamplingStrategy <|-- TopKStrategy
    BaseSamplingStrategy <|-- TopPStrategy
    BaseSamplingStrategy <|-- FrequencyPenaltyStrategy
-    ParallelModel <|-- RowParallelLinear
-    ParallelModel <|-- ColumnParallelLinear
    AutoModel <|-- AutoRegressiveLM
    AutoModel <|-- EmbeddingEncoder
    BaseConfig <|-- BaseModelConfig
@@ -1255,7 +1263,7 @@ classDiagram
    BaseConfig <|-- PipelineConfig
    BaseModelConfig <|-- AutoRegressiveLMConfig
    BaseModelConfig <|-- EncoderConfig
-    BaseFactory <|-- AutoModel
+    BaseFactory <|-- ModelFactory
    BaseFactory <|-- AttnFactory
    BaseFactory <|-- FFNFactory
    BaseFactory <|-- DatasetFactory
@@ -1286,7 +1294,6 @@ classDiagram
    PositionIdStrategy <|-- DocResetPositionId
    PositionIdStrategy <|-- ContinuousPositionId
    StoreWriter <|-- BinWriter
-    StoreWriter <|-- H5Writer
    RawRollout <|-- RolloutResult
    LaunchStrategy <|-- TorchrunStrategy
    LaunchStrategy <|-- LocalStrategy
@@ -1317,8 +1324,6 @@ classDiagram
    %% --- Aggregation (weak ownership) ---
    AutoModel o-- BaseModelConfig
    AutoTokenizer o-- ChatTemplate
-    PagePool o-- Allocator
-    PagePool o-- PrefixCache
    Trainer o-- TrainCallback
    TrainContext o-- BaseStrategy
    TrainContext o-- BaseScheduler
@@ -1352,11 +1357,12 @@ classDiagram
    FFNFactory ..> DeepSeekMoE : creates
    DecoderBlock ..> AttnFactory : uses
    DecoderBlock ..> FFNFactory : uses
-    StoreFactory ..> H5Store : creates
    StoreFactory ..> MmapStore : creates
    StoreFactory ..> JsonlStore : creates
    ConfigFactory ..> AutoRegressiveLMConfig : creates
    ConfigFactory ..> EncoderConfig : creates
+    ModelFactory ..> AutoRegressiveLM : creates
+    ModelFactory ..> EmbeddingEncoder : creates
    ExecutorFactory ..> NoneExecutor : creates
    ExecutorFactory ..> DDPExecutor : creates
    ExecutorFactory ..> FSDPExecutor : creates
@@ -1399,10 +1405,10 @@ classDiagram
 | Module | Components | Description |
 |--------|------------|-------------|
 | **astrai.config** | BaseConfig, BaseModelConfig, AutoRegressiveLMConfig, EncoderConfig, ConfigFactory, TrainConfig, PipelineConfig, InputConfig, ProcessingConfig, OutputConfig | Configuration management (to_dict/from_dict, to_file/from_file) |
-| **astrai.preprocessing** | SectionRenderer, BaseMaskBuilder, MaskBuilderFactory, SectionedMaskBuilder, SingleOutputMaskBuilder, MultiOutputMaskBuilder, Pipeline, TokenizeTransform, PackingStrategy, PackingStrategyFactory, SimplePacking, BFDPacking, BFDSplitPacking, PositionIdStrategy, PositionIdStrategyFactory, NoPositionId, DocResetPositionId, ContinuousPositionId, StoreWriter, StoreWriterFactory, BinWriter, H5Writer | Declarative JSON-driven data preprocessing |
-| **astrai.dataset** | BaseDataset, SEQDataset, SFTDataset, DPODataset, GRPODataset, Store, Streamable, Recordable, H5Store, MmapStore, JsonlSource, JsonlStore, StoreFactory, RDSampler, DatasetFactory | Dataset loading and management |
+| **astrai.preprocessing** | SectionRenderer, BaseMaskBuilder, MaskBuilderFactory, SectionedMaskBuilder, SingleOutputMaskBuilder, MultiOutputMaskBuilder, Pipeline, TokenizeTransform, PackingStrategy, PackingStrategyFactory, SimplePacking, BFDPacking, BFDSplitPacking, PositionIdStrategy, PositionIdStrategyFactory, NoPositionId, DocResetPositionId, ContinuousPositionId, StoreWriter, StoreWriterFactory, BinWriter | Declarative JSON-driven data preprocessing |
+| **astrai.dataset** | BaseDataset, SEQDataset, SFTDataset, DPODataset, GRPODataset, Store, Streamable, Recordable, MmapStore, JsonlSource, JsonlStore, StoreFactory, RDSampler, DatasetFactory | Dataset loading and management |
 | **astrai.serialization** | Checkpoint | Model serialization |
-| **astrai.model** | AutoModel, AutoRegressiveLM, EmbeddingEncoder, DecoderBlock, GQA, MLA, MLP, DeepSeekMoE, AttnFactory, FFNFactory, RMSNorm, Linear, LoRAConfig, LoRALinear, RotaryEmbedding, Embedding | Neural network model |
+| **astrai.model** | ModelFactory, AutoModel, AutoRegressiveLM, EmbeddingEncoder, DecoderBlock, GQA, MLA, MLP, DeepSeekMoE, AttnFactory, FFNFactory, RMSNorm, Linear, LoRAConfig, LoRALinear, RotaryEmbedding, Embedding | Neural network model |
 | **astrai.tokenize** | AutoTokenizer, ChatTemplate | Tokenizer and chat template |
 | **astrai.trainer** | Trainer, TrainContext, TrainContextBuilder, BaseStrategy–GRPOStrategy, StrategyFactory, BaseScheduler–WSDScheduler, SchedulerFactory, TrainCallback(Protocol)–MetricCallback, CallbackFactory, RawRollout, RolloutResult, BaseRewardModel, RolloutGenerator, RolloutRunner | Training workflow |
 | **astrai.inference** | InferenceEngine, InferenceScheduler, Executor, PagePool, KVStorage, ReqToTokenPool, KVCache, Allocator, PrefixCache, Task, TaskManager, TaskStatus, StreamDecoder, GenerationRequest, GenerateResult, BaseSamplingStrategy–SamplingPipeline, FrequencyPenaltyStrategy, ProtocolHandler, ResponseBuilder, OpenAIResponseBuilder, AnthropicResponseBuilder, StopChecker, GenContext, StopInfo, ChatMessage, FunctionDef, ToolDef, ChatCompletionRequest, AnthropicMessage, MessagesRequest, BaseToolParser, ToolParserFactory, SimpleJsonToolParser | Inference service |
@@ -1415,7 +1421,7 @@ classDiagram

 | Pattern | Classes | Purpose |
 |---------|---------|---------|
-| **Factory** | `AttnFactory`, `FFNFactory`, `StrategyFactory`, `DatasetFactory`, `SchedulerFactory`, `CallbackFactory`, `StoreFactory`, `ConfigFactory`, `ExecutorFactory`, `MaskBuilderFactory`, `StoreWriterFactory`, `PackingStrategyFactory`, `PositionIdStrategyFactory`, `ToolParserFactory` | Decorator-based component creation |
+| **Factory** | `ModelFactory`, `AttnFactory`, `FFNFactory`, `StrategyFactory`, `DatasetFactory`, `SchedulerFactory`, `CallbackFactory`, `StoreFactory`, `ConfigFactory`, `ExecutorFactory`, `MaskBuilderFactory`, `StoreWriterFactory`, `PackingStrategyFactory`, `PositionIdStrategyFactory`, `ToolParserFactory` | Decorator-based component creation |
 | **Registry** | `BaseFactory` | Component registration |
 | **Strategy** | `SEQStrategy`, `SFTStrategy`, `DPOStrategy`, `GRPOStrategy` | Training strategy switching |
 | **Strategy (Sampling)** | `TemperatureStrategy`, `TopKStrategy`, `TopPStrategy`, `SamplingPipeline` | Composable logit transformations |
@@ -1427,9 +1433,9 @@ classDiagram
 | **Strategy (Attention)** | `AttentionBackend`, `TorchNativeBackend`, `CudaBackend` | Attention computation backend switching via context manager |
 | **Auto-dispatch (Rotary)** | `apply_rotary_emb`, `rotary_backend.py`, `rotary_ops.py` | Rotary embedding CUDA kernel auto-dispatch with torch fallback |
 | **Executor** | `BaseExecutor`, `NoneExecutor`, `DDPExecutor`, `FSDPExecutor` | Gradient accumulation & model distribution |
-| **Storage** | `Store`, `H5Store`, `MmapStore`, `JsonlStore` | Format-agnostic data access with multi-segment support |
+| **Storage** | `Store`, `MmapStore`, `JsonlStore` | Format-agnostic data access with multi-segment support |
 | **Producer-Consumer** | `InferenceScheduler`, `Task`, queues | Continuous batching |
-| **AutoModel Registry** | `AutoModel`, `AutoRegressiveLM`, `EmbeddingEncoder` | Model-type dynamic loading |
+| **Model Registry** | `ModelFactory`, `AutoRegressiveLM`, `EmbeddingEncoder` | Model-type dynamic loading |

 ## Core Relationships

@@ -1439,10 +1445,10 @@ classDiagram
 4. **Executor Selection**: `ExecutorFactory.create(cfg.parallel_mode, grad_accum_steps=cfg.grad_accum_steps, **cfg.executor_kwargs)` → `NoneExecutor` / `DDPExecutor` / `FSDPExecutor`
 5. **Inference Flow**: `InferenceEngine` → `InferenceScheduler` → `AutoRegressiveLM`, backed by `PagePool` + `KVCache` + `SamplingPipeline`. Attention backend selected via `attn_backend()` context manager (`TorchNativeBackend` default, `CudaBackend` for CUDA kernels). Rotary embedding auto-dispatches to CUDA kernel when available (inference mode), else torch complex multiply (training).
 6. **Distributed**: `spawn_parallel_fn` + `setup_parallel` for multi-process DDP
-7. **Dataset Loading**: `DatasetFactory` creates datasets, `Store` (H5Store/MmapStore/JsonlStore) loads data with explicit `_length` and multi-segment `_data`
-8. **Checkpoint**: `Checkpoint` saves/loads safetensors + metadata (rank-0 only), extra state saved as `{key}.pt`
+7. **Dataset Loading**: `DatasetFactory` creates datasets, `Store` (`MmapStore`/`JsonlStore`) loads data with explicit `_length` and multi-segment `_data`
+8. **Checkpoint**: `Checkpoint` saves/loads safetensors + metadata; `CheckpointCallback` performs rank-0 training saves, with extra state saved as `{key}.pt`
 9. **Scheduler**: `SchedulerFactory` creates `CosineScheduler`/`SGDRScheduler`/`WSDScheduler`
 10. **AutoModel**: `from_pretrained()` loads `config.json` + `model.safetensors`, `_disable_random_init` replaces `nn.init.*` with no-ops
 11. **Protocols**: `OptimizerProtocol` / `SchedulerProtocol` — structural subtyping for `AccumOptimizer` / `AccumScheduler` wrappers

-> Document Update Time: 2026-07-31
+> Document Update Time: 2026-08-02
@@ -14,26 +14,30 @@ This document describes the data pipeline: from raw text to model input tensors.
 ## Overview

 ```
-JSONL Lines → Pipeline (mask builder) → Tokenized Tensors
-                                              ↓
-                                      .h5 or .bin storage
-                                              ↓
-                                      Store.load()
+JSON / JSONL Records → Pipeline (mask builder) → Tokenized Tensors
+                                               ↓
+                                           .bin storage
+                                               ↓
+                                       Store.load()
                                              ↓
                                      Store.fetch(begin, end, keys)
                                              ↓
-                                      BaseDataset.__getitem__(idx)
-                                              ↓
-                                      Sampler → DataLoader → Training / Inference
+                                       Dataset.__getitem__(idx)
+                                               ↓
+                                       RDSampler → DataLoader → Training
 ```

 ## Data Preparation

-Raw text is tokenized via `AutoTokenizer.encode()` and saved as HDF5 (`.h5`) or binary (`.bin` + `meta.json`) files with keyed tensor groups.
+The offline `Pipeline` accepts `.jsonl` records and `.json` files containing one
+object or a list of objects. It tokenizes them and writes binary shards (`.bin`
+plus `meta.json`) with keyed tensor groups. Binary is the only registered output
+writer; the pipeline cannot emit JSONL.

 ### Tokenization

-The `Pipeline` reads JSONL lines, applies the mask builder (see [Preprocessing](../guides/preprocessing.md)), and produces flat token sequences:
+The `Pipeline` reads JSON/JSONL records, applies the mask builder (see
+[Preprocessing](../guides/preprocessing.md)), and produces token sequences:

 ```python
 # Per JSONL line: messages → chat template → token IDs + loss mask
@@ -42,84 +46,114 @@ loss_mask = [0, 0, 0, 1, 1, 1, 1, 1, 1]        # 0=masked, 1=train
 # Stored as flat tensors, packed with other lines by packing strategy
 ```

-The output `meta.json` records the storage format, key names, dtype, total token count, and tensor shapes for each shard.
+For default single-output preprocessing, the stored keys are `sequence` and
+`position_ids`, plus `loss_mask` when masking is required. Packing is supported
+for single-output data with a `sequence` key. Shard flushing counts the primary
+flat sequence for each record: `sequence` in single-output mode, otherwise the
+first flat source output.
+
+The exact shard `meta.json` schema is a top-level mapping from key to tensor
+metadata. It does not contain a storage-format or total-token field:
+
+```json
+{
+  "sequence": {"shape": [123456], "dtype": "int32"},
+  "loss_mask": {"shape": [123456], "dtype": "bool"},
+  "position_ids": {"shape": [123456], "dtype": "int32"}
+}
+```
+
+Record-aware binary data may also include `"offsets": [0, ...]` inside a key's
+metadata, but the preprocessing `BinWriter` currently does not write offsets.

 ### Format Detection

 `detect_format(load_path)` inspects the path:

- If `load_path` is a file: checks suffix — `.h5`/`.hdf5` → `"h5"`, `.jsonl` → `"jsonl"`, unknown suffix raises `ValueError`
- If `load_path` is a directory: recursively globs for `*.h5`/`*.hdf5` files → `"h5"`, `*.bin` + `**/meta.json` → `"bin"`, or `*.jsonl` + `dataset_config.json` → `"jsonl"`
+- If `load_path` is a file: `.jsonl` selects `"jsonl"`; other suffixes raise `ValueError`.
+- If `load_path` is a directory: any recursive `*.bin` plus a `meta.json` selects `"bin"`; otherwise any recursive `*.jsonl` selects `"jsonl"`.
+- Detection does not require `dataset_config.json`; configuration is selected later when `JsonlStore.load()` chooses a transform.

 ### Store Backends

 Storage format is auto-detected by `detect_format()`; backends are dispatched via registry:

 ```
-StoreFactory.create("h5")    → H5Store
 StoreFactory.create("bin")   → MmapStore
 StoreFactory.create("jsonl") → JsonlStore
 ```

-All three inherit `Store` (base, owns `_data`/`_cum`/`_offsets`/`_normalize`) plus the `Streamable` and `Recordable` mixins, so every backend supports both `fetch(begin, end, keys)` (stream) and `fetch_record(index, keys)` (record) APIs.
-
-**H5Store**: Reads HDF5 files. Tensors are loaded into host memory and normalized into segmented storage. `segments_are_records=True` — each `data_i` dataset is one record.
+Both stores inherit `Store` and compose the `Streamable` and `Recordable`
+access methods.

 **MmapStore**: Memory-maps `.bin` files. OS page cache sharing is native — no explicit `share_memory_()` needed. Uses `torch.from_numpy(np.memmap(...))`. `segments_are_records=False` — bin segments are contiguous streams; record access is driven by `_offsets` (written when `save_bin(..., record_keys=...)` was used at preprocessing time).

-**JsonlStore**: On-the-fly tokenization of raw JSONL files at load time. Requires a `dataset_config.json` alongside the `.jsonl` files following the same `PipelineConfig` schema with an additional `tokenizer_path` field. Two modes: eager (default, applies `TokenizeTransform` to all records at load) and lazy (`processor=fn` given, defers tokenisation to `fetch_record` — used by DPO/GRPO).
+**JsonlStore**: Reads a `.jsonl` file or the sorted top-level `*.jsonl` files in
+a directory. Eager transform selection uses the first available route:

-All backends normalise tensors into `Store._data[Dict[str, List[Tensor]]]` + `Store._cum[Dict[str, List[int]]]` (cumulative lengths for bisect-based stream indexing) + `Store._offsets[Dict[str, List[int]]]` (per-record offsets for record-mode indexing). Nested keys (GRPO `responses`/`masks` as `List[List[Tensor]]`) are stored as-is and excluded from both bookkeepings — they are only accessed record-by-record.
+1. An explicit `transform=` argument.
+2. `dataset_config.json` in the JSONL directory. It follows `PipelineConfig` and may add `tokenizer_path`; when omitted, the config directory is used.
+3. The built-in `messages` transform when `tokenizer_path=` is supplied. It masks system/user turns, trains assistant turns, and emits document-reset position IDs.
+
+Only DPO gets an automatic lazy route from `DatasetFactory`: raw JSONL plus
+`tokenizer_path` installs `dpo_processor` and tokenizes each record in
+`fetch_record`. GRPO does not currently have an automatic lazy processor.
+
+Eager-loaded stores normalize tensors into `Store._data[Dict[str, List[Tensor]]]` + `Store._cum[Dict[str, List[int]]]` (cumulative lengths for stream indexing) + `Store._offsets[Dict[str, List[int]]]` (per-record offsets for record indexing). Nested JSONL keys such as GRPO `responses`/`masks` are kept as record values and excluded from stream bookkeeping. Lazy DPO instead retains raw records and processes them in `fetch_record`.

 ## Data Keys by Training Type

 | Type | Storage Keys | Access Mode |
 |------|-------------|-------------|
-| `seq` | `sequence` (→ input_ids, target_ids via offset-by-1) | stream (`fetch`) |
+| `seq` | `sequence`, `position_ids` by default (`SEQDataset` consumes only `sequence`) | stream (`fetch`) |
 | `sft` | `sequence`, `loss_mask`, `position_ids` | stream (`fetch`) |
 | `dpo` | `chosen`, `rejected`, `chosen_mask`, `rejected_mask` | record (`fetch_record`) |
 | `grpo` | `prompts`, `responses`, `masks`, `rewards` | record (`fetch_record`) |

+Offline `.bin` output from DPO/GRPO preprocessing is not currently loadable for
+training. DPO shards are written without record offsets, while GRPO response
+groups are flattened without preserving record/group boundaries. Supported raw
+routes are eager JSONL for SEQ/SFT and automatic lazy JSONL for DPO. GRPO
+requires a caller-built, already-loaded record store.
+
 ## Dataset Architecture

 ```
-DatasetFactory.load(
-    train_type, load_path=None, window_size=0, stride=None,
-    storage_type=None, tokenizer_path=None,
-    max_len=2048, store=None
-)
-  → BaseDataset.load(load_path, storage_type=None)
-    → detect_format(load_path)
-    → StoreFactory.create(storage_type)
-    → Store.load(load_path)
-      → _normalize(raw)  # base Store, shared by both backends
-        → Store._data[Dict[str, List[Tensor]]]
-          + _cum[Dict[str, List[int]]]   (stream mode)
-          + _offsets[Dict[str, List[int]]]  (record mode)
+DatasetFactory.load(...)
+  → detect_format(load_path)
+  → optionally build dpo_processor for raw JSONL
+  → StoreFactory.create(storage_type, window_size, stride)
+  → Store.load(load_path, transform=... or processor=...)
+  → DatasetFactory.create(train_type, store=store)

 Stream datasets (SEQ/SFT):
  BaseDataset.__getitem__(idx)
-    → get_index(idx) → [begin, end)
+    → Store.sample_window(idx) → [begin, end)
    → Store.fetch(begin, end, keys) → Tensor / Dict[str, Tensor]

-Record datasets (DPO/GRPO via RecordDataset):
-  RecordDataset.__getitem__(idx)
+Record datasets (DPO/GRPO):
+  DPODataset/GRPODataset.__getitem__(idx)
    → Store.fetch_record(idx, keys) → Tensor / Dict[str, Tensor]
 ```

-Class hierarchy: `BaseDataset` ← `SEQDataset` / `SFTDataset` (stream); `BaseDataset` ← `RecordDataset` ← `DPODataset` / `GRPODataset` (record).
+Class hierarchy: `BaseDataset` is the direct base of `SEQDataset`, `SFTDataset`,
+`DPODataset`, and `GRPODataset`. There is no `RecordDataset` class.

 `window_size` = max input length, `stride` = step between consecutive samples (defaults to `window_size`, optional). Only meaningful for stream datasets — record datasets ignore both. `storage_type` defaults to `None` (auto-detect via `detect_format`).

-`tokenizer_path` triggers lazy on-the-fly tokenisation for record datasets on raw JSONL (DPO builds a `dpo_processor`; SEQ/SFT/pre-tokenised backends ignore it). `store` (pre-built `Store`) bypasses `load_path`/`storage_type`/`tokenizer_path` entirely — the caller controls Store construction.
+For raw JSONL, `tokenizer_path` builds the lazy processor only for DPO. For
+SEQ/SFT it is forwarded to `JsonlStore` so the built-in eager `messages`
+transform can be selected when no `dataset_config.json` exists. GRPO receives no
+automatic processor. A pre-built `store` bypasses path, format, tokenizer,
+window, and stride setup entirely.

 `Store.fetch(begin, end, keys)` (stream mode, on `Streamable`): accepts a single key (`str`) returning a `Tensor`, or a list of keys returning `Dict[str, Tensor]`. Internally uses `bisect` across multi-segment tensors. Raises `RuntimeError("Store not loaded")` if called before `load()`.

-`Store.fetch_record(index, keys)` (record mode, on `Recordable`): same key API. Uses `_offsets[key]` when present (bin layout with per-record offsets), otherwise indexes `_data[key]` directly (H5/JSONL where each segment is one record).
+`Store.fetch_record(index, keys)` (record mode, on `Recordable`): same key API. Uses `_offsets[key]` when present for binary record layouts; otherwise it indexes per-record JSONL tensors directly.

 ## Sampler

-`ResumableDistributedSampler` supports checkpoint-aware distributed sampling:
+`RDSampler` supports checkpoint-aware distributed sampling:

 - Tracks `start_epoch` / `start_iter` for resume
 - Shuffle via `torch.Generator(seed + epoch)`
@@ -41,7 +41,12 @@ RoPE embeds position into Q/K vectors via complex rotation:

 $$ q_i = R_i W_q x_i, \quad k_j = R_j W_k x_j, \quad q_i^T k_j = x_i^T W_q^T R_{i-j} W_k x_j $$

-`RotaryEmbedding` pre-computes `cos_table` and `sin_table` (f32, `[max_len, dim/2]`). `forward()` returns a `(cos, sin)` tuple indexed by `position_ids`. `apply_rotary_emb` applies the rotation: during training it uses torch complex multiply (autograd-compatible); during inference it auto-dispatches to a fused CUDA kernel when available. The key property is that the dot product $q_i^T k_j$ depends only on the relative position $i - j$, not the absolute positions.
+`RotaryEmbedding` pre-computes a complex `freqs_cis` buffer. `forward()` returns
+a tensor indexed by `position_ids`. `apply_rotary_emb` applies the rotation:
+during training it uses torch complex multiply (autograd-compatible); during
+inference it auto-dispatches to a fused CUDA kernel when available. The key
+property is that the dot product $q_i^T k_j$ depends only on the relative
+position $i - j$, not the absolute positions.

 **Critical for inference**: RoPE is applied **before** KV cache write, not after. If applied after caching, position encoding drift occurs because cached K/V would have stale rotation factors.

@@ -51,13 +56,13 @@ $$ q_i = R_i W_q x_i, \quad k_j = R_j W_k x_j, \quad q_i^T k_j = x_i^T W_q^T R_{

 Next-token cross-entropy with optional label smoothing:

-$$ L_{\text{PT}} = -\sum_{t=1}^{T} \log P(x_t \mid x_{\lt t}; \theta) $$
+$$ L_{\text{PT}} = -\frac{1}{T}\sum_{t=1}^{T} \log P(x_t \mid x_{\lt t}; \theta) $$

 ### SFT (Supervised Fine-Tuning)

 Masked cross-entropy (`ignore_index=-100`) over response tokens only:

-$$ L_{\text{SFT}} = -\sum_{t=P+1}^{P+L} \log P(s_t \mid s_{\lt t}; \theta) $$
+$$ L_{\text{SFT}} = -\frac{1}{L}\sum_{t=P+1}^{P+L} \log P(s_t \mid s_{\lt t}; \theta) $$

 Prompt tokens are masked out via `loss_mask`; only response tokens contribute to the loss.

@@ -98,8 +103,8 @@ on_train_begin
  model.train()
  on_epoch_begin
    for batch in dataloader:
-      on_batch_begin
      with executor.accumulate(model):
+        on_batch_begin
        loss_output = strategy(batch)
        context.loss = loss_output["loss"].item()
        context.metrics = loss_output["metrics"]
@@ -113,6 +118,7 @@ on_train_begin
        if executor.sync_gradients:
          on_optimizer_step
          optimizer.step()
+          strategy.on_optimizer_step()
          optimizer.zero_grad()
          if scheduler:
            scheduler.step()
@@ -121,21 +127,23 @@ on_train_end
 ```

 The loss is divided by `grad_accum_steps` before `backward()`, so accumulated gradients sum to the correct mean.
+Strategy metrics are detached and converted to Python `float` values before the
+`LossOutput` is returned; only `LossOutput.loss` remains a differentiable tensor.

 ## Callback Lifecycle

 | Hook | Fires | Default callback |
 |------|-------|-----------------|
-| `on_train_begin` | Before training starts | `GradientCheckpointingCallback` |
+| `on_train_begin` | Before training starts | `GradientCheckpointingCallback`, `CheckpointCallback`, `MetricCallback` |
 | `on_epoch_begin` | Start of each epoch | `ProgressBarCallback` |
 | `on_batch_begin` | Every batch | — |
-| `on_optimizer_step` | Every accumulation window | `GradientClippingCallback`, `MetricCallback`, `ProgressBarCallback` |
+| `on_optimizer_step` | Every accumulation window | `MetricCallback`, `ProgressBarCallback`, `GradientClippingCallback` |
 | `on_batch_end` | Every batch | `CheckpointCallback` |
 | `on_epoch_end` | End of each epoch | `MetricCallback`, `ProgressBarCallback` |
 | `on_error` | On exception during training | `CheckpointCallback`, `MetricCallback` |
-| `on_train_end` | Training ends (always via finally) | `CheckpointCallback`, `MetricCallback`, `GradientCheckpointingCallback` |
+| `on_train_end` | Training exits after `on_train_begin` completes (via `finally`) | `GradientCheckpointingCallback`, `CheckpointCallback`, `MetricCallback` |

-Default callbacks (in order): `gradient_checkpointing` (activation checkpointing, optional), `checkpoint` (safetensors, rank-0), `metric` (JSONL + validation, rank-0), `progress_bar` (tqdm), `gradient_clipping` (always registered; computes grad norm, clips only when `max_grad_norm` is not `None`).
+Default callbacks (in order): `gradient_checkpointing` (activation checkpointing, optional), `checkpoint` (safetensors, rank-0), `metric` (JSONL + validation, rank-0), `progress_bar` (tqdm, rank-0), `gradient_clipping`. The gradient-clipping callback is always registered and always calls `executor.clip_grad_norm()` with the numeric `max_grad_norm` value.

 ## KV Cache Mathematics

@@ -160,7 +168,7 @@ Three-layer separation (SGLang-inspired):
 - **ReqToTokenPool**: Index table `[req_idx, pos] → physical token slot`, shared across all layers.
 - **Allocator + PrefixCache**: Paged-mode slot allocation with ref-counting, LRU eviction, and hash-based prefix sharing.

-`PagePool` orchestrates all three. In contiguous mode (default), `req_to_token` is a trivial linear mapping. In paged mode, slots are allocated on demand with prefix caching support. `bind_tasks()` returns a `KVCache` dataclass with precomputed `page_table` and `decode_mask` fields (computed once per decode step, shared across all layers). Attention layers access buffers directly — no methods, no abstraction.
+`PagePool` orchestrates all three. In contiguous mode (default), `req_to_token` is a trivial linear mapping. In paged mode, slots are allocated on demand with prefix caching support. `bind_tasks()` returns a `KVCache` dataclass with `kv_indptr`, a prefix-sum index over sequence lengths computed once per step and shared across layers. Attention layers access buffers directly — no methods, no abstraction.

 ### Attention Backend

@@ -239,4 +247,4 @@ total_steps          = (batches_per_replica // grad_accum_steps) * n_epoch

 This accounts for data-parallel sharding — each rank processes `1/nprocs` of the dataset.

-> Document Update Time: 2026-07-31
+> Document Update Time: 2026-08-02
@@ -2,11 +2,23 @@

 This guide walks you through installing AstrAI, downloading a model, running inference, preprocessing data, and launching your first training job.

+## Contents
+
+- [Prerequisites](#prerequisites)
+- [1. Install](#1-install)
+- [2. Download Model Weights](#2-download-model-weights)
+- [3. Run Inference](#3-run-inference)
+- [4. Preprocess Data](#4-preprocess-data)
+- [5. Train](#5-train)
+- [6. Evaluate](#6-evaluate)
+- [7. Docker](#7-docker)
+- [Next Steps](#next-steps)
+
 ## Prerequisites

 - **Python 3.12+**
- **PyTorch 2.11+** (CUDA 12.8 recommended for GPU support)
- NVIDIA GPU with CUDA (optional but recommended; CPU works for inference)
+- **PyTorch 2.11.0** (the exact version pinned by AstrAI; CUDA 12.8 build recommended for GPU support)
+- NVIDIA GPU with CUDA for training, `scripts/tools/generate.py`, generation evaluations, and demos. The HTTP server and direct-scoring evaluations can run on CPU where their CLI exposes a CPU device.

 ## 1. Install

@@ -55,7 +67,7 @@ python scripts/demo/stream_chat.py
 # Type your message after >>, type !exit to quit
 ```

-This starts a multi-turn interactive chat session with streaming output.
+This starts a single-turn interactive prompt loop with streaming output. Each prompt is independent; conversation history is not retained.

 ### Start an HTTP Server

@@ -192,6 +204,13 @@ See [Training Guide](guides/training.md) for loss formulas and strategies. See [

 ## 6. Evaluate

+HumanEval and MMLU download their benchmark data through HuggingFace
+`datasets`, which is not part of the base install:
+
+```bash
+pip install datasets
+```
+
 ```bash
 # HumanEval (code generation, auto-downloads dataset)
 python scripts/eval/evaluate_humaneval.py --param_path ./params --num_samples 20
@@ -2,6 +2,18 @@

 AstrAI supports three parallel modes: **single GPU** (`none`), **Data Parallel** (`ddp`), and **Fully Sharded Data Parallel** (`fsdp`). This guide covers when to use each, how to launch multi-GPU training, and how gradient accumulation works.

+## Contents
+
+- [Quick Start](#quick-start)
+- [Parallel Modes](#parallel-modes)
+- [Gradient Accumulation](#gradient-accumulation)
+- [Process Launching](#process-launching)
+- [NCCL Troubleshooting](#nccl-troubleshooting)
+- [Checkpoint Saving](#checkpoint-saving)
+- [Total Steps Calculation](#total-steps-calculation)
+- [Real Examples](#real-examples)
+- [CLI Parameters](#cli-parameters)
+
 ## Quick Start

 ### Single GPU
@@ -21,9 +33,6 @@ python scripts/tools/train.py \

 ```bash
 export CUDA_VISIBLE_DEVICES=0,1,2,3
-export NCCL_P2P_DISABLE=1
-export NCCL_NET_GDR_LEVEL=0
-
 python scripts/tools/train.py \
    --train_type=sft \
    --param_path ./params \
@@ -38,9 +47,6 @@ python scripts/tools/train.py \

 ```bash
 export CUDA_VISIBLE_DEVICES=0,1,2,3
-export NCCL_P2P_DISABLE=1
-export NCCL_NET_GDR_LEVEL=0
-
 python scripts/tools/train.py \
    --train_type=sft \
    --param_path ./params \
@@ -110,7 +116,7 @@ AstrAI auto-detects the launch method:

 | Detection | Strategy | Use Case |
 |-----------|----------|----------|
-| `torchelastic` / `torchrun` env vars | `TorchrunStrategy` | External orchestrator (torchrun, SLURM, K8s) |
+| `torchelastic` / `torchrun` env vars | `TorchrunStrategy` | External orchestrator (`torchrun`, K8s) |
 | `RANK` + `WORLD_SIZE` env vars | `TorchrunStrategy` | External launch |
 | Neither | `LocalStrategy` | `python scripts/tools/train.py` (in-process spawn) |

@@ -126,23 +132,28 @@ For multi-node or SLURM environments:
 torchrun --nproc_per_node=4 scripts/tools/train.py \
    --train_type=sft \
    --parallel_mode=ddp \
+    --nprocs=4 \
    --param_path ./params \
    --data_root_path ./dataset \
    --batch_per_device=4
 ```

-When launched via torchrun, AstrAI reads `RANK`, `WORLD_SIZE`, `LOCAL_RANK` from the environment and uses `TorchrunStrategy`. The `--nprocs` flag is ignored (the orchestrator controls process count).
+When launched via `torchrun`, the launcher creates the worker processes. AstrAI reads `RANK`, `WORLD_SIZE`, and `LOCAL_RANK` from the environment and uses `TorchrunStrategy`; `--nprocs` does not control process creation in this mode.

-## NCCL Environment Variables
+The current training CLI still uses `--nprocs` when calculating scheduler `total_steps`. Set it to the global `WORLD_SIZE` so the step count reflects data-parallel sharding, including multi-node runs.

-For multi-GPU training, you **must** set these environment variables:
+Raw Slurm variables such as `SLURM_PROCID`, `SLURM_NTASKS`, and `SLURM_LOCALID` are not recognized automatically. Launch through `torchrun`, or map the scheduler's variables to `RANK`, `WORLD_SIZE`, `LOCAL_RANK`, `MASTER_ADDR`, and `MASTER_PORT` before starting AstrAI. The same requirement applies to launchers that expose only OpenMPI-specific variables.
+
+## NCCL Troubleshooting
+
+The following variables are troubleshooting options for hardware or network configurations where NCCL hangs or fails. They are not general requirements and can reduce performance by disabling peer-to-peer or GPUDirect RDMA paths:

 ```bash
 export NCCL_P2P_DISABLE=1
 export NCCL_NET_GDR_LEVEL=0
 ```

-These are required on certain hardware configurations (see `AGENTS.md`). Without them, NCCL may hang or crash during collective operations. These are set in the training shell scripts (`train-seq.sh`, `train-sft.sh`, `train-dpo.sh`) but not in Python code — you must export them before launching.
+Apply them only after confirming the relevant NCCL transport is the source of the failure. AstrAI does not set them in Python.

 ## Checkpoint Saving

@@ -176,9 +187,6 @@ This ensures the LR schedule is correctly scaled regardless of the number of GPU

 ```bash
 export CUDA_VISIBLE_DEVICES=0,1,2,3
-export NCCL_P2P_DISABLE=1
-export NCCL_NET_GDR_LEVEL=0
-
 python scripts/tools/train.py \
    --train_type=seq \
    --param_path ./params \
@@ -240,7 +248,7 @@ python scripts/tools/train.py \

 | Parameter | Default | Description |
 |-----------|---------|-------------|
-| `--nprocs` | 1 | Number of GPUs / processes |
+| `--nprocs` | 1 | Local process count for AstrAI's launcher; under `torchrun`, set it to global `WORLD_SIZE` for total-step calculation |
 | `--parallel_mode` | `fsdp` | `none`, `ddp`, or `fsdp` |
 | `--start_method` | `spawn` | Multiprocessing start method (`spawn`, `fork`, `forkserver`) |
 | `--backend` | `nccl` | Distributed backend (`nccl`, `gloo`) |
@@ -248,8 +256,8 @@ python scripts/tools/train.py \
 | `--master_port` | `29500` | Master node port |
 | `--device_type` | `cuda` | Device type |

-> `--tp_size` is parsed but **not yet wired** — tensor parallelism is future work. `ColumnParallelLinear` / `RowParallelLinear` exist in `astrai/parallel/module.py` but are not used by the model.
+> `--tp_size` is accepted by the CLI but discarded before configuration. Tensor parallelism is not implemented, and there is no tensor-parallel module or model integration.

 Full parameter reference: [CLI Reference](params.md). Training loop and strategies: [Training Guide](training.md).

-> Document Update Time: 2026-07-30
+> Document Update Time: 2026-08-02
@@ -2,6 +2,29 @@

 AstrAI provides 7 evaluation scripts in `scripts/eval/` covering code generation, knowledge QA, perplexity, summarization, data quality, instruction following, and weight analysis.

+## Contents
+
+- [Prerequisites](#prerequisites)
+- [Overview](#overview)
+- [HumanEval](#humaneval-code-generation)
+- [MMLU](#mmlu-knowledge-qa)
+- [Perplexity](#perplexity-ppl)
+- [ROUGE](#rouge)
+- [IFD](#ifd-instruction-following-difficulty)
+- [IFEval](#ifeval-instruction-following)
+- [Weight Analysis](#weight-analysis)
+- [Tips](#tips)
+
+## Prerequisites
+
+HumanEval, MMLU, and IFEval import HuggingFace `datasets` to download their benchmark data. This package is not installed by AstrAI's base dependencies, so install it before running those scripts:
+
+```bash
+pip install datasets
+```
+
+The generation-based scripts require CUDA because they load the model on `cuda` with `bfloat16`. Direct-scoring and metric scripts support the devices shown below.
+
 ## Overview

 | Script | Metric | Model Invocation | External Dataset |
@@ -18,7 +41,15 @@ Two invocation patterns exist:
 - **Generation benchmarks** (HumanEval, IFEval): use `InferenceEngine` to generate responses, then score them.
 - **Scoring benchmarks** (MMLU, PPL, IFD): call `model()` directly under `torch.inference_mode()` for log-likelihood computation.

-Common defaults: `--param_path` defaults to `./params`; dtype defaults to `bfloat16` on CUDA, `float32` on CPU.
+| Script | Device support |
+|--------|----------------|
+| HumanEval | CUDA for generation; `--test_only` can score existing completions without loading a model |
+| IFEval | CUDA only |
+| MMLU | CUDA or CPU via `--device`; auto-selects CUDA when available |
+| PPL | CUDA or CPU via `--device`; auto-selects CUDA when available |
+| IFD | CUDA or CPU via `--device`; auto-selects CUDA when available |
+| ROUGE | CPU-only metric computation; no model is loaded |
+| Weight analysis | CUDA by default; CPU supported via `--device cpu` |

 ---

@@ -30,7 +61,7 @@ Generates completions for 164 programming problems, executes them against hidden
 python scripts/eval/evaluate_humaneval.py \
    --param_path ./params \
    --num_samples 20 \
-    --batch_size 32 \
+    --batch_size 64 \
    --max_tokens 512 \
    --output results/humaneval.json
 ```
@@ -47,7 +78,8 @@ python scripts/eval/evaluate_humaneval.py \
 | `--temperature` | 0.8 | Sampling temperature |
 | `--top_p` | 0.95 | Nucleus sampling threshold |
 | `--top_k` | 50 | Top-k sampling |
-| `--batch_size` | 32 | Generation batch size |
+| `--batch_size` | 64 | Generation batch size |
+| `--max_seq_len` | 4096 | KV cache sequence length |
 | `--test_workers` | 8 | ProcessPoolExecutor workers for test execution |
 | `--test_timeout` | 3.0 | Per-subprocess timeout (seconds) |
 | `--problems` | None | Restrict to specific problem indices |
@@ -66,7 +98,7 @@ python scripts/eval/evaluate_humaneval.py \
 python scripts/eval/evaluate_mmlu.py \
    --param_path ./params \
    --n_shot 5 \
-    --subjects math_algebra history_us \
+    --subjects abstract_algebra high_school_us_history \
    --output results/mmlu.json
 ```

@@ -82,12 +114,13 @@ python scripts/eval/evaluate_mmlu.py \
 | `--device` | auto | Device (`cuda` / `cpu`) |
 | `--dtype` | auto | `bfloat16` on CUDA, `float32` on CPU |
 | `--seed` | 0 | Seed for option permutation (0 = enabled, -1 = disabled) |
+| `--batch_size` | 4 | Questions per batch; each question produces four choice rows |

 **How it works**: For each question, builds a prompt with n-shot examples, then scores each choice (A/B/C/D) by computing the summed log-likelihood of the choice token given the context. The choice with the highest log-prob is the prediction.

 **Output**: stdout prints per-subject accuracy and overall. With `--output`, writes per-subject `{accuracy, correct, total}` + `_overall` aggregate.

-**Data**: Auto-downloads `cais/mmlu` from HuggingFace. Stored as per-subject CSVs in `<data_dir>/<split>/` and `<data_dir>/dev/` (for few-shot).
+**Data**: Auto-downloads `cais/mmlu` from HuggingFace. Stored as per-subject CSVs in `<data_dir>/<split>/` and `<data_dir>/dev/` (for few-shot). `--subjects` accepts canonical MMLU names such as `abstract_algebra`, `college_computer_science`, `high_school_us_history`, and `world_religions`.

 ---

@@ -100,7 +133,7 @@ python scripts/eval/evaluate_ppl.py \
    --param_path ./params \
    --input_path data.jsonl \
    --output_dir ppl_results/ \
-    --batch_size 4 \
+    --batch_size 64 \
    --max_length 2048
 ```

@@ -110,7 +143,7 @@ python scripts/eval/evaluate_ppl.py \
 | `--input_path` | required | Input file, glob, or directory |
 | `--output_dir` | required | Output directory for `summary.json` + token JSONL |
 | `--text_key` | `text` | Key for the text field in input data |
-| `--batch_size` | 4 | Batch size |
+| `--batch_size` | 64 | Batch size |
 | `--max_length` | 2048 | Max sequence length (tokens) |
 | `--token_level` | False | Store per-token log_probs + token-type analysis |
 | `--max_samples` | None | Random subsample per file |
@@ -119,7 +152,7 @@ python scripts/eval/evaluate_ppl.py \

 **Input**: JSONL or JSON files. Each item must have a field named by `--text_key` (default `text`). If `--input_path` is a directory, recursively collects `*.jsonl` and `*.json`.

-**Output**: `summary.json` with per-file stats (tokens, mean/median loss, perplexity, p50/p90/p95/p99). With `--token_level`, also writes per-token JSONL with token IDs and log-probs.
+**Output**: `summary.json` with per-file token count, mean loss, perplexity, and p50/p90/p95/p99 loss. Median loss is included only with `--token_level`; that mode also writes per-token JSONL with token IDs and log-probs.

 ---

@@ -210,7 +243,8 @@ python scripts/eval/evaluate_ifeval.py \
 | `--top_p` | 0.95 | Top-p sampling |
 | `--top_k` | 50 | Top-k sampling |
 | `--num_samples` | 1 | Samples per problem (best-of-n scoring) |
-| `--batch_size` | 1 | Inference batch size |
+| `--batch_size` | 64 | Inference batch size |
+| `--max_seq_len` | 4096 | KV cache sequence length |
 | `--limit` | None | Limit to first N problems (quick testing) |
 | `--dump_responses` | None | Path to dump raw responses as JSONL |

@@ -232,7 +266,7 @@ python scripts/eval/analyze_weights.py \

 | Parameter | Default | Description |
 |-----------|---------|-------------|
-| `--ckpt_dir` | required | Checkpoint dir with `model.safetensors` + `config.json` |
+| `--ckpt_dir` | required | Checkpoint directory containing `model.safetensors` |
 | `--compare` | None | Additional checkpoint dirs to compare |
 | `--no_svd` | False | Skip SVD; show only weight stats (faster) |
 | `--output` | None | Save results as JSON |
@@ -245,8 +279,8 @@ python scripts/eval/analyze_weights.py \
 ## Tips

 - **Quick test**: Use `--limit` (IFEval) or `--problems` (HumanEval) to run on a small subset first.
- **Auto-download**: HumanEval, MMLU, and IFEval auto-download their datasets on first run. The other scripts expect user-provided data.
+- **Auto-download**: After installing `datasets`, HumanEval, MMLU, and IFEval auto-download their datasets on first run. The other scripts expect user-provided data.
 - **Output formats**: `--output` writes a single JSON for most scripts. PPL and IFD write an `--output_dir` containing `summary.json` plus per-file artifacts.
- **CPU mode**: All scripts auto-detect CUDA. To force CPU, use `--device cpu --dtype float32`.
+- **CPU mode**: MMLU, PPL, and IFD support `--device cpu --dtype float32`; weight analysis supports `--device cpu`. HumanEval generation and IFEval are CUDA-only.

 > Document Update Time: 2026-07-30
@@ -49,8 +49,7 @@ KVCache
  ├── seq_lens               [batch_size]
  ├── out_cache_loc          [batch, seq_len] — write indices for this forward
  ├── max_len                int — max(seq_lens), avoids GPU sync in decode
-  ├── page_table             [batch, max_len] — precomputed gather indices for decode (None for prefill)
-  └── decode_mask            [batch, max_len] bool — precomputed position validity mask (None for single-batch decode)
+  └── kv_indptr              [batch + 1] int32 — prefix sum of seq_lens, precomputed once per step
 ```

 Attention layers do raw buffer indexing: `k_buffer[layer_id, out_cache_loc] = k` to write, `k_buffer[layer_id, indices]` to gather.
@@ -87,7 +86,9 @@ Rotary embedding is applied via `apply_rotary_emb` in `astrai/extension/rotary_b
 - **CUDA kernel** (`rotary_emb.cu`): fused cos/sin lookup + rotation in a single kernel, used when the kernel is available, input is on CUDA, and `torch.is_grad_enabled()` is `False` (inference mode)
 - **Torch fallback**: complex multiply path (`torch.view_as_complex` → `torch.complex` multiply → `torch.view_as_real`), used during training (supports autograd backward) or when the CUDA kernel is not available

-`RotaryEmbedding` stores `cos_table`/`sin_table` as f32 buffers and returns a `(cos, sin)` tuple from `forward()`. Both attention backends share the same rotary dispatch — it is backend-agnostic.
+`RotaryEmbedding` stores a complex `freqs_cis` buffer and returns a tensor
+from `forward()`. Both attention backends share the same rotary dispatch — it
+is backend-agnostic.

 ## Continuous Batching

@@ -183,22 +184,58 @@ curl -X POST http://localhost:8000/v1/messages \
  -d '{"model":"astrai","system":"You are helpful.","messages":[{"role":"user","content":"Hello"}],"max_tokens":512}'
 ```

-Supports `stop_sequences` and streaming via `event: content_block_delta`.
+Supports `stop_sequences` and streaming via `event: content_block_delta`. Anthropic streams also end with the shared `data: [DONE]` sentinel after `event: message_stop`.

-### GenerationRequest Parameters
+### Request Parameters
+
+The HTTP protocols and direct engine API have distinct request models and defaults.
+
+**OpenAI** (`ChatCompletionRequest`):

 | Param | Type | Default | Description |
 |-------|------|---------|-------------|
+| `model` | str | `"astrai"` | Model name returned in responses |
 | `messages` | List[dict] | required | Chat messages (role, content) |
-| `top_k` | int | 50 | Top-k count |
-| `top_p` | float | 1.0 | Nucleus threshold |
-| `temperature` | float | 1.0 | Sampling temperature (> 0.0) |
-| `max_tokens` | Optional[int] | None | Max generation length |
-| `stream` | bool | False | Stream output |
+| `temperature` | Optional[float] | 1.0 | Sampling temperature (0.0-2.0) |
+| `top_p` | Optional[float] | 1.0 | Nucleus threshold (0.0-1.0) |
+| `top_k` | Optional[int] | 50 | Top-k count |
+| `max_tokens` | Optional[int] | 2048 | Max generation length |
+| `stream` | Optional[bool] | False | Stream output |
 | `stop` | Optional[Union[str, List[str]]] | None | Stop sequences |
-| `frequency_penalty` | float | 0.0 | Frequency penalty |
-| `tools` | Optional[List[dict]] | None | Tool definitions for function calling |
-| `tool_choice` | Optional[str] | None | Tool selection mode |
+| `n` | Optional[int] | 1 | Number of choices requested |
+| `presence_penalty` | Optional[float] | 0.0 | Presence penalty (-2.0 to 2.0) |
+| `frequency_penalty` | Optional[float] | 0.0 | Frequency penalty (-2.0 to 2.0) |
+| `logit_bias` | Optional[Dict[int, float]] | None | Per-token logit bias |
+| `user` | Optional[str] | None | End-user identifier |
+| `tools` | Optional[List[ToolDef]] | None | Tool definitions for function calling |
+| `tool_choice` | Optional[Union[str, Dict[str, Any]]] | `"auto"` | Tool selection mode or explicit tool choice |
+
+**Anthropic** (`MessagesRequest`):
+
+| Param | Type | Default | Description |
+|-------|------|---------|-------------|
+| `model` | str | `"astrai"` | Model name returned in responses |
+| `messages` | List[AnthropicMessage] | required | User/assistant messages |
+| `system` | Optional[str] | None | System prompt |
+| `max_tokens` | int | 1024 | Max generation length |
+| `temperature` | Optional[float] | 1.0 | Sampling temperature (0.0-2.0) |
+| `top_p` | Optional[float] | 1.0 | Nucleus threshold (0.0-1.0) |
+| `top_k` | Optional[int] | 50 | Top-k count |
+| `stream` | Optional[bool] | False | Stream output |
+| `stop_sequences` | Optional[List[str]] | None | Stop sequences |
+
+**Engine** (`GenerationRequest`):
+
+| Param | Type | Default | Description |
+|-------|------|---------|-------------|
+| `messages` | List[Dict[str, str]] | required | Messages to format before generation |
+| `top_k` | int | 50 | Top-k count; 0 disables filtering |
+| `top_p` | float | 1.0 | Nucleus threshold |
+| `temperature` | float | 1.0 | Sampling temperature; 0 enables greedy decoding |
+| `max_tokens` | Optional[int] | None | Max generation length |
+| `frequency_penalty` | float | 0.0 | Frequency penalty (-2.0 to 2.0) |
+| `rep_window` | int | 64 | Recent-token window used by the frequency penalty |
+| `stream` | bool | False | Stream output |

 ### SSE Streaming Format

@@ -240,6 +277,8 @@ data: {"type":"message_delta","delta":{"stop_reason":"end_turn","stop_sequence":

 event: message_stop
 data: {"type":"message_stop"}
+
+data: [DONE]
 ```

 ### Error Responses
@@ -13,9 +13,11 @@

 | Parameter | Description | Default |
 |-----------|-------------|---------|
+| `--config`, `-c` | YAML config file; explicit CLI options override YAML values | None |
 | `--train_type` | Training type (`seq`, `sft`, `dpo`, `grpo`, `online_grpo`, `online_dpo`) | required |
 | `--data_root_path` | Dataset root directory | required |
 | `--param_path` | Model parameters or checkpoint path | required |
+| `--resume` | Resume training from `--param_path` | False |
 | `--n_epoch` | Total training epochs | 1 |
 | `--batch_per_device` | Batch size per device | 1 |
 | `--grad_accum_steps` | Gradient accumulation steps between optimizer steps | 1 |
@@ -26,7 +28,7 @@
 |-----------|-------------|---------|
 | `--warmup_ratio` | Fraction of total steps used for LR warmup | 0.05 |
 | `--max_lr` | Maximum learning rate (cosine decay after warmup) | 3e-4 |
-| `--max_grad_norm` | Maximum gradient norm for clipping (None disables) | 1.0 |
+| `--max_grad_norm` | Maximum gradient norm for clipping; the current CLI requires a positive number | 1.0 |

 ### Optimizer

@@ -36,9 +38,9 @@ non-matrix parameters through **AdamW** (`fused=True`).
 | Parameter | Description | Default |
 |-----------|-------------|---------|
 | `--optimizer` | Built-in optimizer (`muon_adamw`, `nora_nadamw`, `mano_adamw`) | `muon_adamw` |
-| `--weight_decay` | Weight decay (applied to Muon matrix params; non-matrix use 0) | 0.1 |
+| `--weight_decay` | Weight decay for optimizer parameter groups that are eligible for decay | 0.1 |
 | `--muon_momentum` | Muon momentum factor | 0.95 |
-| `--muon_nesterov` | Enable Nesterov momentum for Muon | True |
+| `--muon_nesterov`, `--no-muon_nesterov` | Enable or disable Nesterov momentum for Muon | enabled |
 | `--muon_ns_steps` | Newton-Schulz iteration steps for Muon | 5 |
 | `--muon_adjust_lr` | Muon LR adjustment strategy (`original`, `match_rms_adamw`) | `match_rms_adamw` |

@@ -56,15 +58,18 @@ under DTensor sharding and rejects layouts sharded along the last dimension.
 | `--nora_weight_decay` | Nora matrix weight decay | 0.0 |

 `mano_adamw` routes internal `Linear.weight` matrices to **Mano** (manifold
-normalized optimizer) and the remaining parameters to **NAdamW**. Mano projects
+normalized optimizer) and the remaining parameters to **AdamW**. Mano projects
 the momentum onto the tangent space of the Oblique manifold and normalizes it,
 alternating the projection axis (row/column) each step — replacing Muon's
 Newton-Schulz iteration with a cheaper normalization.

 | Parameter | Description | Default |
 |-----------|-------------|---------|
-| `--mano_momentum` | Mano momentum factor | 0.95 |
-| `--mano_nesterov` | Enable Nesterov momentum for Mano | True |
+| `--mano_momentum` | Accepted by the CLI but currently ignored by optimizer construction | 0.95 |
+| `--mano_nesterov`, `--no-mano_nesterov` | Accepted by the CLI but currently ignored by optimizer construction | enabled |
+
+The two Mano-specific flags are reserved for future wiring; do not rely on them
+to change optimizer behavior in the current release.

 Optimizer identity and hyperparameters are saved in checkpoint metadata. Optimizer
 states are intentionally not interchangeable: resume older MuonAdamW checkpoints
@@ -78,7 +83,7 @@ with `--optimizer=muon_adamw`.
 | `--stride` | Stride for sliding window over sequences | None |
 | `--random_seed` | Random seed for reproducibility | 3407 |
 | `--num_workers` | DataLoader worker processes | 4 |
-| `--no_pin_memory` | Disable pin_memory (enabled by default) | (flag) |
+| `--pin_memory`, `--no-pin_memory` | Enable or disable DataLoader pinned memory | enabled |

 ### Checkpoint & Resume

@@ -100,14 +105,20 @@ with `--optimizer=muon_adamw`.

 | Parameter | Description | Default |
 |-----------|-------------|---------|
-| `--log_dir` | Directory for metric logs | checkpoint/logs |
-| `--metrics` | Metrics to log (e.g. --metrics loss lr val_loss) | ["loss", "lr", "grad_norm"] |
+| `--metrics` | Repeatable metric option (for example, `--metrics loss --metrics lr --metrics val_loss`) | `loss`, `lr`, `grad_norm`, `grad_snr` |

 ### Gradient Checkpointing

 | Parameter | Description | Default |
 |-----------|-------------|---------|
-| `--gradient_checkpointing` | Enable activation checkpointing for DecoderBlock modules | False |
+| `--gradient_checkpointing`, `--no-gradient_checkpointing` | Enable or disable activation checkpointing for DecoderBlock modules | disabled |
+
+### Miscellaneous
+
+| Parameter | Description | Default |
+|-----------|-------------|---------|
+| `--compile` | Enable `torch.compile` with mode `default`, `reduce-overhead`, or `max-autotune`; omit to disable | None |
+| `--dry-run` | Validate the merged configuration and print the training plan without training | False |

 ### Distributed Training

@@ -120,21 +131,25 @@ with `--optimizer=muon_adamw`.
 | `--backend` | Distributed training backend | nccl |
 | `--master_addr` | Master node address | localhost |
 | `--master_port` | Master node port | 29500 |
+| `--tp_size` | Reserved tensor-parallel size; accepted but currently ignored | None |

 ### Strategy-specific

 | Parameter | Description | Default | Used by |
 |-----------|-------------|---------|---------|
-| `--dpo_beta` | DPO beta value | 0.1 | `dpo` |
+| `--dpo_beta` | DPO beta value | 0.1 | `dpo`, `online_dpo` |
 | `--label_smoothing` | Label smoothing for cross-entropy loss | 0.0 | `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` |
+| `--group_size` | GRPO/rollout group size | 4 | `grpo`, `online_grpo`, `online_dpo` |
+| `--grpo_clip_eps` | GRPO clipping epsilon | 0.2 | `grpo`, `online_grpo` |
+| `--grpo_kl_coef` | GRPO KL penalty coefficient | 0.01 | `grpo`, `online_grpo` |
 | `--neftune_alpha` | NEFTune noise alpha (0=disabled, typical: 5.0) | 0.0 | `sft` |

 ### Online Rollout

-These options apply to `online_grpo` and `online_dpo`. Online strategies require
+`online_grpo` and `online_dpo` are factory aliases for the existing `grpo` and
+`dpo` strategy classes; online behavior is enabled by rollout components rather
+than separate strategy subclasses. These options apply to the online aliases.
+Online strategies require
 a `BaseRewardModel` factory in `TrainConfig`; `train.py` does not currently
 provide a command-line option for configuring one.

@@ -151,7 +166,7 @@ provide a command-line option for configuring one.
 | Parameter | Description | Default |
 |-----------|-------------|---------|
 | `--schedule_type` | LR scheduler type (`cosine`, `sgdr`, `wsd`) | cosine |
-| `--min_rate` | Minimum LR as fraction of base LR | None (scheduler default: 0.05 for cosine/SGDR, 0.0 for WSD) |
+| `--min_rate` | Minimum LR as fraction of base LR | None (all current schedulers use their effective default of 0.01) |
 | `--cycle_length` | SGDR first cycle length in steps | None (total_steps - warmup_steps) |
 | `--t_mult` | SGDR cycle length multiplier per restart | 2 |
 | `--stable_steps` | WSD stable plateau steps | None (80% of post-warmup steps) |
@@ -204,14 +219,6 @@ python scripts/tools/server.py --param_path ./params --device cuda --dtype bfloa

 See [Inference Guide](inference.md) for HTTP API documentation.

-# Preprocess
-
-```bash
-python scripts/tools/preprocess.py data/*.jsonl -o output/ -c config.json
-```
-
-See [Preprocessing Guide](preprocessing.md) for config file format and examples.
-
 ## Generate (`generate.py`)

 | Parameter | Type | Default | Description |
@@ -221,13 +228,12 @@ See [Preprocessing Guide](preprocessing.md) for config file format and examples.
 | `--output_json_file` | str | required | Path to the output JSONL file |
 | `--question_key` | str | `question` | Key for the question in input JSON |
 | `--response_key` | str | `response` | Key for the response in output JSON |
-| `--temperature` | float | `0.60` | Sampling temperature |
-| `--top_k` | int | `30` | Top-k filtering |
+| `--temperature` | float | `0.8` | Sampling temperature |
+| `--top_k` | int | `50` | Top-k filtering |
 | `--top_p` | float | `0.95` | Nucleus sampling threshold |
 | `--batch_size` | int | `1` | Batch size for generation |
 | `--num_samples` | int | `1` | Responses per prompt |
-| `--max_tokens` | int | model config `max_position_embeddings` | Maximum tokens to generate |
-| `--cache_len` | int | `2048` | KV cache length |
+| `--max_seq_len` | int | `2048` | KV cache sequence length |
 | `--frequency_penalty` | float | `0.0` | Frequency penalty |
 | `--rep_window` | int | `64` | Window size for frequency penalty |

@@ -243,14 +249,15 @@ python scripts/tools/generate.py \

 | Parameter | Type | Default | Description |
 |-----------|------|---------|-------------|
-| `input_files` | path(s) | required | Input JSONL file(s), supports glob (`data/*.jsonl`) |
+| `input_files` | path(s) | required | One or more existing `.jsonl` or `.json` paths. Wildcards work only when expanded by the invoking shell; the CLI does not expand globs itself. |
 | `--output_dir`, `-o` | path | required | Output directory for processed data |
 | `--config`, `-c` | path | required | Preprocessing pipeline config (JSON) |
 | `--tokenizer_path` | str | `params` | Path to tokenizer directory |
+| `--batch_size` | int | config value (`256` by default) | Override records processed per batch; must be at least 1 |

 Usage:
 ```bash
-python scripts/tools/preprocess.py data/*.jsonl -o output/ -c sft.json
+python scripts/tools/preprocess.py data/part-000.jsonl data/part-001.jsonl -o output/ -c sft.json
 ```

 See [Preprocessing Guide](preprocessing.md) for config file format and examples.
@@ -10,6 +10,7 @@ Declarative JSON-driven data preprocessing. `MaskBuilderFactory` supports three
 - [Configuration Reference](#configuration-reference) — all fields
 - [Mask Algorithm](#mask-algorithm)
 - [Output Layout](#output-layout)
+- [Training Compatibility](#training-compatibility)
 - [CLI](#cli)
 - [Python API](#python-api)

@@ -40,7 +41,7 @@ A single config file captures the entire pipeline, reusable and version-controll
 | Field | Type | Default | Description |
 |-------|------|---------|-------------|
 | `field` | str | -- | JSONL key to read |
-| `action` | str | -- | `"train"` / `"mask"` / `"$role"` |
+| `action` | str | -- | `"train"` / `"mask"` / `"$role"` / `"value"`; `"value"` copies raw values without tokenization |
 | `template` | bool | `false` | Apply `chat_template` per message |
 | `add_special_tokens` | bool | `true` for first non-template section | Add special tokens during encode |

@@ -89,7 +90,7 @@ Config:
 }
 ```

-Output keys: `sequence` (int32), `loss_mask` (bool)
+Output keys: `sequence` (int32), `loss_mask` (bool), `position_ids` (int32)

 ### SFT Instruction

@@ -116,7 +117,7 @@ Config:
 }
 ```

-Output keys: `sequence`, `loss_mask`
+Output keys: `sequence`, `loss_mask`, `position_ids`

 ### Pretrain

@@ -142,7 +143,7 @@ Config:
 }
 ```

-Output keys: `sequence` (no `loss_mask` — all tokens trained)
+Output keys: `sequence`, `position_ids` (no `loss_mask` — all tokens trained)

 ### DPO

@@ -180,6 +181,11 @@ Config:

 Output keys: `chosen`, `chosen_mask`, `rejected`, `rejected_mask`

+The offline `Pipeline` can construct these keys, but its `.bin` output is not
+currently loadable for DPO training because the writer does not preserve
+per-record offsets. Train DPO directly from raw JSONL instead; see
+[Training Compatibility](#training-compatibility).
+
 ### GRPO

 Input JSONL:
@@ -228,6 +234,11 @@ Output keys: `prompts`, `prompts_mask`, `responses`, `masks`, `rewards` (float32
 - `mask_key: "masks"` — rename the auto-generated mask key (default: `responses_mask`)
 - `prompts_mask` is auto-generated (all masked) and unused by GRPOStrategy

+The offline `Pipeline` flattens GRPO response groups for `.bin` output without
+preserving their boundaries, and there is no automatic raw-JSONL GRPO processor
+in `DatasetFactory`. See
+[Training Compatibility](#training-compatibility) for the supported routes.
+
 ---

 ## Configuration Reference
@@ -257,7 +268,7 @@ When `sources` is set, `sections` is ignored.
 | `max_chars` | int | `2000000` | Skip text-mode items longer than this |
 | `max_items` | int or null | `null` | Stop after N documents |
 | `batch_size` | int | `256` | Records per tokenization batch |
-| `packing_strategy` | str | `"simple"` | Packing strategy: `"simple"`, `"bfd"`, `"bfd_split"` |
+| `packing_strategy` | str | `"simple"` | Packing is supported for single-output data with a `sequence` key: `"simple"`, `"bfd"`, or `"bfd_split"`. Multi-output DPO/GRPO data is not record-preserving packed output. |
 | `max_packed_len` | int | `8192` | Maximum length of a packed bin |
 | `truncation_mode` | str | `"keep_start"` | How to truncate sequences: `"keep_start"` or `"keep_end"` |

@@ -266,8 +277,8 @@ When `sources` is set, `sections` is ignored.
 | Field | Type | Default | Description |
 |-------|------|---------|-------------|
 | `domain_key` | str or null | `null` | JSONL key for domain grouping |
-| `storage_format` | str | `"bin"` | `"bin"` (mmap). Reading also supports `"jsonl"` for on-the-fly tokenization |
-| `max_tokens_per_shard` | int | `100000000` | Flush threshold in cumulative tokens |
+| `storage_format` | str | `"bin"` | Pipeline output format. Only `"bin"` has a registered writer; `"jsonl"` is accepted by config validation but cannot be emitted by `Pipeline`. |
+| `max_tokens_per_shard` | int | `100000000` | Flush threshold counted from each record's primary flat sequence: `sequence` for single-output data, otherwise the first flat source output |
 | `dtype` | dict[str, str] | `{}` | Per-key tensor dtype override (e.g. `{"loss_mask": "bool"}`) |
 | `position_ids_mode` | str | `"doc_reset"` | How to compute position_ids: `"none"`, `"doc_reset"`, `"continuous"` |

@@ -304,11 +315,13 @@ output/
      meta.json
      sequence.bin
      loss_mask.bin
+      position_ids.bin
  wiki/
    shard_0000/
      meta.json
      sequence.bin
      loss_mask.bin
+      position_ids.bin
 ```

 ### Multi-Shard (`bin`)
@@ -322,13 +335,44 @@ output/
      meta.json
      sequence.bin
      loss_mask.bin
+      position_ids.bin
    shard_0001/
      meta.json
      sequence.bin
      loss_mask.bin
+      position_ids.bin
 ```

-For `bin` format, `MmapStore` discovers all shards under the domain directory via `rglob("meta.json")`. For `h5` format, `H5Store` discovers `.h5`/`.hdf5` files via recursive glob.
+`MmapStore` discovers binary shards recursively through their `meta.json` files.
+Each shard's metadata is a top-level object keyed by tensor name:
+
+```json
+{
+  "sequence": {"shape": [123456], "dtype": "int32"},
+  "loss_mask": {"shape": [123456], "dtype": "bool"},
+  "position_ids": {"shape": [123456], "dtype": "int32"}
+}
+```
+
+An optional `offsets` array may appear for record-oriented binary data written
+through `save_bin(..., record_keys=...)`; the preprocessing `BinWriter` does not
+currently request those offsets.
+
+---
+
+## Training Compatibility
+
+| Training type | Supported input route |
+|---------------|-----------------------|
+| `seq` | Offline preprocessed `.bin`, or raw `.jsonl` eagerly transformed by `JsonlStore` using `dataset_config.json` or the built-in `messages` config |
+| `sft` | Offline preprocessed `.bin`, or raw `.jsonl` through the same eager transform routes |
+| `dpo` | Raw `.jsonl` through the automatic lazy DPO processor selected by `DatasetFactory` when `tokenizer_path` is supplied, or a caller-provided record store |
+| `grpo` | A caller-provided, already-loaded `Store` with record-shaped `prompts`, `responses`, `masks`, and `rewards`; no automatic raw-JSONL processor is currently wired |
+
+Offline DPO and GRPO preprocessing configs describe the intended token fields,
+but their `.bin` output is not currently loadable for training. DPO binary
+shards lack per-record offsets. GRPO response groups are flattened before the
+binary writer and their record/group boundaries are not preserved.

 ---

@@ -336,15 +380,20 @@ For `bin` format, `MmapStore` discovers all shards under the domain directory vi

 ```bash
 # SFT
-python scripts/tools/preprocess.py data/sft/*.jsonl -o output/sft/ -c configs/sft_chat.json
+python scripts/tools/preprocess.py data/sft/part-000.jsonl -o output/sft/ -c configs/sft_chat.json --batch_size 128

 # DPO
-python scripts/tools/preprocess.py data/dpo/*.jsonl -o output/dpo/ -c configs/dpo.json --tokenizer_path params
+python scripts/tools/preprocess.py data/dpo/part-000.jsonl -o output/dpo/ -c configs/dpo.json --tokenizer_path params

 # GRPO
-python scripts/tools/preprocess.py data/grpo/*.jsonl -o output/grpo/ -c configs/grpo.json
+python scripts/tools/preprocess.py data/grpo/part-000.jsonl -o output/grpo/ -c configs/grpo.json
 ```

+Inputs may be `.jsonl` files or `.json` files containing one object or a list of
+objects. Each positional path must exist. A wildcard such as `data/*.jsonl`
+works only when the invoking shell expands it before Click receives the
+arguments; otherwise pass the files explicitly.
+
 ---

 ## Python API
@@ -41,7 +41,10 @@ RoPE embeds position into Q/K vectors via complex rotation:

 $$ q_i = R_i W_q x_i, \quad k_j = R_j W_k x_j, \quad q_i^T k_j = x_i^T W_q^T R_{i-j} W_k x_j $$

-`RotaryEmbedding` pre-computes `cos_table` and `sin_table` (f32, `[max_len, dim/2]`). `forward()` returns a `(cos, sin)` tuple indexed by `position_ids`. `apply_rotary_emb` applies the rotation: during training it uses torch complex multiply (autograd-compatible); during inference it auto-dispatches to a fused CUDA kernel when available.
+`RotaryEmbedding` pre-computes a complex `freqs_cis` buffer. `forward()` returns
+a tensor indexed by `position_ids`. `apply_rotary_emb` applies the rotation:
+during training it uses torch complex multiply (autograd-compatible); during
+inference it auto-dispatches to a fused CUDA kernel when available.

 ## Training Loop

@@ -52,8 +55,8 @@ on_train_begin
  model.train()
  on_epoch_begin
    for batch in dataloader:
-      on_batch_begin
      with executor.accumulate(model):
+        on_batch_begin
        loss_output = strategy(batch)
        context.loss = loss_output["loss"].item()
        context.metrics = loss_output["metrics"]
@@ -67,6 +70,7 @@ on_train_begin
        if executor.sync_gradients:
          on_optimizer_step
          optimizer.step()
+          strategy.on_optimizer_step()
          optimizer.zero_grad()
          if scheduler:
            scheduler.step()
@@ -78,16 +82,16 @@ on_train_end

 | Hook | Fires | Default callback |
 |------|-------|-----------------|
-| `on_train_begin` | Before training starts | `GradientCheckpointingCallback` |
+| `on_train_begin` | Before training starts | `GradientCheckpointingCallback`, `CheckpointCallback`, `MetricCallback` |
 | `on_epoch_begin` | Start of each epoch | `ProgressBarCallback` |
 | `on_batch_begin` | Every batch | — |
-| `on_optimizer_step` | Every accumulation window | `GradientClippingCallback`, `MetricCallback`, `ProgressBarCallback` |
+| `on_optimizer_step` | Every accumulation window | `MetricCallback`, `ProgressBarCallback`, `GradientClippingCallback` |
 | `on_batch_end` | Every batch | `CheckpointCallback` |
 | `on_epoch_end` | End of each epoch | `MetricCallback`, `ProgressBarCallback` |
 | `on_error` | On exception during training | `CheckpointCallback`, `MetricCallback` |
-| `on_train_end` | Training ends (always via finally) | `CheckpointCallback`, `MetricCallback`, `GradientCheckpointingCallback` |
+| `on_train_end` | Training exits after `on_train_begin` completes (via `finally`) | `GradientCheckpointingCallback`, `CheckpointCallback`, `MetricCallback` |

-Default callbacks (in order): `gradient_checkpointing` (activation checkpointing, optional), `checkpoint` (safetensors, rank-0), `metric` (JSONL + validation, rank-0), `progress_bar` (tqdm), `gradient_clipping` (always registered; computes grad norm, clips only when `max_grad_norm` is not `None`).
+Default callbacks (in order): `gradient_checkpointing` (activation checkpointing, optional), `checkpoint` (safetensors, rank-0), `metric` (JSONL + validation, rank-0), `progress_bar` (tqdm, rank-0), `gradient_clipping`. The gradient-clipping callback is always registered and always calls `executor.clip_grad_norm()` with the numeric `max_grad_norm` value.

 Strategies return `{"loss": Tensor, "metrics": Dict[str, float]}` when called by the trainer. Built-in metrics include the task-specific loss and, for MoE models, `moe_aux_loss` plus `moe_aux_loss_weighted`. Direct `compute_loss(batch)` calls continue to return a single loss tensor.

@@ -98,7 +102,7 @@ Strategies return `{"loss": Tensor, "metrics": Dict[str, float]}` when called by
 Next-token cross-entropy with optional label smoothing:

 $$
-L_{\text{PT}} = -\sum_{t=1}^{T} \log P(x_t \mid x_{\lt t}; \theta)
+L_{\text{PT}} = -\frac{1}{T}\sum_{t=1}^{T} \log P(x_t \mid x_{\lt t}; \theta)
 $$

 Keys: `input_ids`, `target_ids`. Optional: `label_smoothing`.
@@ -108,7 +112,7 @@ Keys: `input_ids`, `target_ids`. Optional: `label_smoothing`.
 Masked cross-entropy (`ignore_index=-100`) over response tokens:

 $$
-L_{\text{SFT}} = -\sum_{t=P+1}^{P+L} \log P(s_t \mid s_{\lt t}; \theta)
+L_{\text{SFT}} = -\frac{1}{L}\sum_{t=P+1}^{P+L} \log P(s_t \mid s_{\lt t}; \theta)
 $$

 Keys: `input_ids`, `target_ids`, `loss_mask`, `position_ids`. Optional: `label_smoothing`.
@@ -168,9 +172,9 @@ model factory.
 |------|-------|-------------|
 | Cosine | `CosineScheduler` | Linear warmup → cosine decay to `min_rate` |
 | SGDR | `SGDRScheduler` | Cosine annealing with warm restarts (`t_mult=2`) |
-| WSD | `WSDScheduler` | Warmup-Stable-Decay with sqrt cooldown |
+| WSD | `WSDScheduler` | Warmup-Stable-Decay with quadratic decay |

-Created by `SchedulerFactory.create(schedule_type, optimizer, **kwargs)`. Valid types: `"cosine"`, `"sgdr"`, `"wsd"`. Omit to use no scheduler.
+Created by `SchedulerFactory.create(schedule_type, optimizer, **kwargs)`. Valid types: `"cosine"`, `"sgdr"`, `"wsd"`. The training CLI always creates a scheduler and defaults `--schedule_type` to `"cosine"`.

 ## Gradient Checkpointing

@@ -188,10 +192,12 @@ Callback wraps each `DecoderBlock.forward` with `torch.utils.checkpoint.checkpoi

 ```
 Checkpoint(state_dict, epoch, consumed_samples, extra, meta, config)
-  ├── save(save_dir)    rank-0 only: meta.json (epoch/consumed_samples/timestamp) + config.json (model config) + model.safetensors + optional {key}.pt (optimizer.pt, scheduler.pt)
+  ├── save(save_dir)    meta.json (epoch/consumed_samples/timestamp) + config.json (model config) + model.safetensors + optional {key}.pt (optimizer.pt, scheduler.pt)
  └── load(save_dir, broadcast=False)    loads from local disk; set broadcast=True to broadcast metadata from rank-0
 ```

+`Checkpoint.save()` writes whenever it is called. During training, `CheckpointCallback` uses the executor checkpoint context so only rank 0 receives a state dict and calls `save()`.
+
 Optimizer/scheduler state persisted by default via `Checkpoint.extra`.  
 Model config (`context.model_config`) saved into `config.json` during training via `CheckpointCallback`.

@@ -235,4 +241,4 @@ nohup python scripts/tools/train.py \

 Full parameter reference at [params.md](params.md).

-> Document Update Time: 2026-07-31
+> Document Update Time: 2026-08-02