v0.3.1a3 (beta)

This release fixes the router masks and the lazy-expert decode. It also adds a document-level cluster objective, MLX multimodal training in all six stages, tool-call SFT records and tool-call parsing in chronos serve, a kernel-level MLX cache limit and progress-based ETAs.

Added

• cluster_locality_mode="segment": the cluster objective treats each document as one unit.
  • Documents are found from the tokens: <|endoftext|> separators, and long documents are cut every cluster_locality_window tokens. No labels or domain tags are used.
  • The loss is 1 − I(document; cluster) / log C, pooled over real tokens and all virtual layers.
  • The default stays "window", the 0.3.1a2 objective.
  • Torch and MLX give the same value and gradient. The option is on the Web UI config page (advanced) in four languages.
• MLX multimodal training. chronos.mlx.multimodal ports the image patch encoder, the audio conv encoder and the gated cross-attention fusion.
  • Pre-encoded *_embeds inputs and the per-modality and per-token masks work as in Torch.
  • Logits match Torch within 2e-4 in FP32 tests.
  • MLX checkpoints save and load the fusion weights, and freeze_modality_encoders works on MLX.
  • run_mlx_stage takes media in all six stages: pretrain, SFT, DPO, ORPO, GRPO and distill.
  • DPO and ORPO: the chosen and rejected answers condition on the prompt's media.
  • GRPO: every rollout conditions on the prompt's media. The rollout encodes the media once and reuses it at every decode step, with identical logits. The policy update re-encodes the media, so the encoders get gradients.
  • Distill: the student gets the media. A text-only teacher is accepted, as on PyTorch.
• Multimodal preference and GRPO data.
  • MultimodalStreamingDPODataset and preference_collate read chosen/rejected records that carry image, audio or *_embeds fields, the same fields SFT uses.
  • load_grpo_prompt_records keeps each prompt's record so the media can be resolved per step.
  • record_media is the shared resolver.
  • The Web UI selects these automatically when the dataset contains media.
  • The PyTorch DPO, ORPO and GRPO trainers are text-only. With media, auto picks MLX, and an explicit PyTorch backend raises an error instead of dropping the media.
• Tool calls in the chat template.
  • tools render as one <tools> system block.
  • The assistant's tool_calls render as <tool_call>{json}</tool_call> lines, and tool turns carry the results.
  • Conversations without tools render exactly as before.
• chronos.data.chat_records provides render_chat, encode_chat and write_sft_records.
  • Supervised tokens: the assistant text, its tool calls and <|im_end|>.
  • Context tokens: system and user turns, tool definitions and tool results.
  • Records are written as .bin/.idx/.plen/.lmask.
• SFTRecords reads .lmask (one uint8 per token). When present it replaces the .plen prompt rule. Older readers still use .plen.
• chronos serve supports tool calls end to end.
  • It passes tools and the assistant's tool_calls from /api/chat and /v1/chat/completions into the template.
  • When the request has tools, it parses <tool_call> blocks in the output into structured calls. /api/chat returns Ollama message.tool_calls. /v1/chat/completions returns OpenAI tool_calls with string arguments and finish_reason: "tool_calls".
  • Streaming parses the calls as they are generated, and no client sees part of a tag.
  • An invalid block stays in the content. Requests without tools are unchanged.
  • chronos.serving.tool_calls holds the parser.
• chronos.mlx.memory: every MLX model sets the MLX buffer-cache limit before it allocates weights.
  • The limit is min(RAM/8, 16 GiB).
  • A recipe (extra.mlx_cache_limit_gb) or CHRONOS_MLX_CACHE_LIMIT_GB can lower it. Larger values are clamped to the limit.
  • LongRun logs the limit and records it in runtime.json.
  • Only reclaimable cache is bounded. Weights, activations and gradients are not.
• Monitor and Web UI ETAs follow measured progress.
  • A phase ends at the first of three points: the wall deadline, the step total, or target_passes.
  • The pass rate comes from row timestamps over the last 30 minutes, so evaluation and checkpoint time count.
  • Each phase shows its estimated end and which of the three points sets it.
  • Later phases also get the current phase's unused time.
  • A stopped or failed run shows its remaining work and its state, but no end time.

Fixed

• Router auxiliary losses and routing telemetry counted right-padding in SFT batches. Balance, entropy, lookahead and cluster terms now use only real tokens. Packed pretraining is unchanged.
• Training telemetry now sums every micro-batch of a step. Before, it sampled only the last one.
• SFTRecords with .lmask: under NumPy 2, the -100 context labels of uint16 shards wrapped to 65436. Labels are now built in int32.
• MLX lazy-expert decode used the per-expert loop, which sums in a different order from the full-memory grouped kernel. In BF16 on the GPU this flipped near-tied greedy tokens: agreement was 31.25 % at every budget. The lazy path now runs the same grouped kernel over the live experts. Lazy and full greedy now agree at every budget (measured: 0.278, 0.5, 0.778 and 1.0 of experts, planned and window residency).

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Pre-release. Install with pip install --pre Project_Chronos==0.3.1a3. Kernel only; chronos-translate stays a repo demo. Training-data licences are unknown, so no weights are published.