- shard the Muon Newton-Schulz orthogonalization over the FSDP mesh instead of partial local slices
- import HF checkpoints faithfully: per-head RoPE permutation for q/k projections and qk-norm, qwen3, shared experts, and qk-norm before RoPE (changes numerics for existing use_qk_norm checkpoints)
- make preprocessing and resume self-contained: backfill realigned bucket keys by semantics (masks ones, rest zeros) and snapshot tokenizer files into every checkpoint
- keep RL consistent: sync the offline GRPO old_model each optimizer step and validate online strategies through a public one-off-rollout hook that leaves the replay cache untouched
- fix streaming serving: withhold partial tool-call prefixes with a stream-end flush, stream tool-call arguments from the raw source span, and terminate SSE frames with a blank line
- fix sampling semantics: capture logprobs before top-k/top-p mutate logits in place and detect greedy pipelines polymorphically instead of isinstance bookkeeping
- replace the O(batch*vocab) count materialization and boolean-mask/unique indexing in FrequencyPenaltyStrategy with a flat-bucket where + index_add_ + elementwise subtraction that never forces a device-host synchronization
- the hidden nonzero syncs inside masked indexing and torch.unique dominated the old path under GPU contention, not the scatter itself
- semantics unchanged (per-row penalties, padding mask, zero-penalty skip); all 27 sampling tests pass
- Benchmark: L20 SM89, batch 8 vocab 100k, penalty overhead 6.3ms to 47us and full sampling pipeline 7196us to 969us.
- reject prompts that encode to zero tokens in add_task instead of admitting a task whose prefill can never run, and surface empty-id run_batch calls as prompt_empty errors
- deliver the STOP stream callback when cancelling a live task so clients observe termination instead of hanging until socket timeout
- strip the torch.compile _orig_mod. prefix at every unwrap_model site and when loading checkpoints so FSDP state dicts and saved weights no longer leak the wrapper name into downstream keys
- reject online_* train strategies with nprocs > 1 at config validation time, explaining the NCCL all-gather deadlock they would otherwise hit mid-run
- apply the frequency penalty before temperature scaling (OpenAI semantics) so the penalty survives temperature=0 instead of being annihilated by the 1e8 logit blowup, and exclude penalty pipelines from the greedy fast path
- return logprobs from the raw pre-strategy distribution so they match training-side policy logprobs for PPO/GRPO importance ratios
- Eliminate core/ directory into cache/, runtime/, network/ subpackages plus flat modules
- Split cache.py (647 lines) into cache/{buffer,strategy,pool}.py by layer
- Add explicit ContiguousStrategy, make AllocationStrategy a real ABC
- Move TaskCacheState to cache/strategy.py, drop string forward references
- Rename api/ to network/, server.py to app.py
- Move sample.py into runtime/ alongside executor and graph
- Simplify TaskCacheManager.__init__ to single pool param
- Expose pool.strategy and pool.req_pool as public properties
- Fix KVCache import in attention_backend.py (TYPE_CHECKING guard)
- Fix steady-state decode reading uninitialized position_ids on first step
ViperEkura