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docs(v0.72.0): list layer streaming in the README docs index and commands reference 

Two spots the release missed: the README's own docs-index row for
Performance & quantization (docs/README.md's equivalent row was already
updated), and docs/commands.md, which lists config-driven training
features in the same style as LISA and Spectrum.

Docs-only — no version bump.
This commit is contained in:
Alpamys 2026-07-27 01:06:53 +05:00
parent 72be45db85
commit 4dae34a6a4
2 changed files with 2 additions and 1 deletions
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README.md
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@ -230,7 +230,7 @@ The full feature reference lives in [`docs/`](docs/). Start here:
|---|---|
| [Training tasks & methods](docs/training.md) | SFT, DPO/GRPO/PPO/KTO/ORPO/SimPO/IPO/BCO, tool-calling, PRM, pre-training, distillation, classification, vision/audio/TTS, unlearning, RAFT/RA-DIT, loop-hardening detectors |
| [PEFT, long context & efficiency](docs/peft-and-efficiency.md) | DoRA, LoRA+, rsLoRA, VeRA, OLoRA, NEFTune, PiSSA, ReLoRA, optimizer & PEFT zoo, LLaMA Pro, GaLore, YaRN/LongLoRA, packing, curriculum, auto-tuning |
| [Performance & quantization](docs/performance-and-quantization.md) | QAT, FP8, Quant Menu (I + II), KV-cache, NVFP4, save formats, Cut Cross-Entropy, gradient checkpointing, kernels, activation offloading, multi-GPU / DeepSpeed / FSDP |
| [Performance & quantization](docs/performance-and-quantization.md) | QAT, FP8, Quant Menu (I + II), KV-cache, NVFP4, save formats, Cut Cross-Entropy, gradient checkpointing, kernels, activation offloading, layer streaming, multi-GPU / DeepSpeed / FSDP |
| [Data engineering](docs/data.md) | Formats, the Axolotl/LF-parity pipeline, data tools, synthetic generation & forge, quality scorecards, trace tooling, remote datasets, mixing, recipe DAGs |
| [Evaluation & probes](docs/evaluation.md) | Eval design/gate, eval-gated training, benchmarks, NLG metrics, calibration, Elo arena, diagnose, post-train X-ray probes, A/B, drift, tunability, `soup advise` |
| [Serving & export](docs/serving-and-export.md) | OpenAI-compatible server, batch inference, benchmarking, merge/export, Anthropic Messages endpoint, speculative decoding (train + measure your own draft), deploy autopilot, Web UI, Agent Forge |

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docs/commands.md
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@ -30,6 +30,7 @@ soup merge-sharded-fsdp-weights ./shards -o merged.safetensors Consolidate FSDP
soup delinearize-llama4 ./src --target ./out [--num-experts N] [--plan-only] Live Llama-4 fused-expert reshape [E*din,dout] -> [E,din,dout] + sidecar copy (v0.71.21)
soup spectrum scan --model <id|path> --top-percent 50 [--modules mlp,attn] [-o patch.yaml] Spectrum SNR scan (no model load) -> training.unfrozen_parameters YAML patch (v0.71.23)
soup train --config sft.yaml # training.lisa_enabled: true [lisa_num_layers lisa_interval_steps] LISA layerwise importance sampling — full-FT quality at LoRA-like memory (sft/transformers/text/quantization=none) (v0.71.34)
soup train --config sft.yaml # training.stream_layers: true [stream_source stream_buffers] BETA layer streaming — the frozen base streams from CPU RAM one decoder layer at a time, so peak VRAM is bounded by ONE layer (sft/transformers/text/quantization=none, batch 1) (v0.72.0)
soup export --model ./output --format gguf Export to GGUF (Ollama)
soup export --model ./output --deploy ollama Export GGUF + auto-deploy to Ollama
soup export --model ./output --format onnx Export to ONNX