Features
- FSDP parallel backend with zero-redundancy sharded training
- LoRA fine-tuning module with low-rank adapter injection and persistence
- NTK-Aware RoPE dynamic scaling, extending context window limit
- MMLU evaluation script for standardized model knowledge assessment
- load_json/load_safetensors broadcast mechanism for cross-node distributed loading
Refactors
- Storage layer refactored to Store pattern, removed Fetcher layer, supporting multi-segment data with explicit length
- Training backend refactored to Executor pattern (none/ddp/fsdp), decoupling parallel logic
- Inference protocol layer refactored to Strategy/Builder pattern with independent OpenAI/Anthropic responders
- Unified serialization layer, eliminating scattered I/O paths
- Removed JSONStore from data pipeline, unified to H5/Bin dual format
- Simplified _disable_random_init, moved scheduler into sync block
- Removed -> None return annotations, split FSDP parameters
Fixes
- Disabled DDP static_graph to prevent no_sync/backward conflict under PyTorch 2.7.1
- Checkpoint resume restores optimizer/scheduler state and sampler remaining length
- Unwrap DDP/FSDP on checkpoint save to avoid module. prefix
- start_epoch/start_batch determined by user args, no longer overridden by checkpoint
- Left padding in perplexity.py causing incorrect PPL with batch>1
- Storage multi-segment bug, switched JSON to JSONL
- Early abort on task_extend failure after decode, notify waiting tasks on scheduler crash
Docs
- Synced architecture/training/inference/dataflow/params docs to actual code
Tests
- Completed inference protocol layer unit test coverage
- Added LoRA module tests
- Filled storage layer test gaps
ViperEkura
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