invergent-ai/surogate holds a health index of 60 out of 100, placing it in the Moderate band. It scores highest on Vitality (85/100) and lowest on Community & Adoption (31/100). It was last updated today. A single contributor accounts for most of its recent work.
60
overall / 100
Moderate
Software health index
Metrics are grouped into weighted categories on one standardized 1–100 scale. Overall starts as their weighted mean; when public evidence triggers the High-Risk Jurisdiction Policy, the rating is adjusted and receives an At risk ceiling of 49. AI Readiness sits outside the overall score.
60
Excellent85-100Exemplary; meets essentially all checked criteria
Good70-84Healthy; minor gaps
Moderate50-69Acceptable with notable gaps; review recommended
At risk30-49Significant weaknesses; adoption warrants caution
Critical1-29Severe problems (abandoned, single-maintainer, no hygiene)
Score profile
Each axis is a category. The shape matters more than the average — a healthy subject fills the whole shape, while a spike-and-crater profile means strength in one dimension is masking risk in another.
617 stars in 2 days, 322× the baseline2026-06-03 → 2026-06-04Star concentration — nearly all growth on a handful of daysNo decay — the burst stopped deadPopularity & adoption: stars and forks discounted by 40%
Based on the timing of public star and fork events. It describes a pattern that organic attention does not produce; it does not establish that attention was purchased, or that the maintainers were involved.
Inorganic Growth Policy discounts the stars and forks components by 40%. The finding describes the timing of public star and fork events; it does not establish that attention was purchased, or that the maintainers were involved.
How well is the repo equipped to be developed and maintained with AI coding agents? An independent, experimental badge — weight 0.0, so it is surfaced on its own and does not affect the overall health score.
Could not fetch pypi package 'surogate' from its registry
GitHub dependency-graph SBOM unavailable (404); the dependency graph may be disabled for this repository
More detail
Star and fork history 806 ★ / 6 ⇿
806Stars
6Forks
44Releases
When each star and fork was added, collected from GitHub and bucketed by day. Cumulative growth sits directly above the daily additions it is made of, so the two read against each other: steady organic accretion looks nothing like an abrupt, short-lived burst. Where that difference is measurable, it is reported as growth authenticity.
Independent, tool-agnostic security assessment from the open-source OpenSSF Scorecard. Each check rewards a security practice, not a specific vendor's tool. Checks Scorecard could not determine are marked n/a and excluded from the security score (never counted as zero).Scorecard v5.5.0 · 2026-07-21 15:50 UTC
The resolved dependency set could not be collected for this report: GitHub dependency-graph SBOM unavailable (404); the dependency graph may be disabled for this repository
Raw JSON report machine-readable
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"body": "Merge pull request #54 from invergent-ai/feature/dispatch-pp-planner-…",
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"headline": "Merge pull request #55 from invergent-ai/main",
"author_name": "Flavius Burca",
"author_login": "flaviusburca",
"committed_at": "2026-06-27T05:00:09Z",
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"oid": "a04e61e36791b170c61b5cacc7575cf3dd72f5d1",
"body": "…phase0\n\nDispatch Pipeline Parallelism (dispatch-PP): train models too large for one GPU on PCIe-only boxes",
"is_bot": false,
"headline": "Merge pull request #54 from invergent-ai/feature/dispatch-pp-planner-…",
"author_name": "Flavius Burca",
"author_login": "flaviusburca",
"committed_at": "2026-06-22T13:26:35Z",
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"oid": "7b1164c91785deb3c2afc402387172058b3c0e8e",
"body": "New guide docs/guides/dispatch-pp.md documenting the model-parallel mode for training models whose\nbase weights do not fit on a single GPU, on PCIe-only boxes (no NVLink/P2P): round-robin stages,\nper-stage weight streaming from pinned CPU (offload_master), host-staged boundaries, the FP8 weight\nstre\n[…]\n-PP) Options\"\nsection to the config reference (parallelism / offload_master / recipe + the microbatch math and\nSUROGATE_DISPATCH_STAGE_BLOCKS).\n\nCo-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>",
"is_bot": false,
"headline": "docs: add Dispatch Pipeline Parallelism (dispatch-PP) guide",
"author_name": "flaviusburca",
"author_login": null,
"committed_at": "2026-06-22T13:23:09Z",
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"oid": "1c2d4d38b60feb9ee2b146087c628d0613d62638",
"body": "…udaMalloc/cudaFree)\n\napply_named_inject cudaMalloc-ed a device buffer per injected boundary tensor and\nclear_inject_named cudaFree-d it, once per dispatch stage -> ~hundreds of cudaMalloc/cudaFree\nper step, each a device-wide sync that serialized the pipeline. Boundary tensors are all\n[B,T,H] bf16 \n[…]\n~3380 -> ~3270 ms (~3-4%), loss parity (4.72->3.57).\nFirst of the boundary-handoff optimizations; pinned host buffers + async overlap are next.\n\nCo-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>",
"is_bot": false,
"headline": "perf(dispatch-pp): pool the cross-stage inject buffers (no per-call c…",
"author_name": "flaviusburca",
"author_login": null,
"committed_at": "2026-06-22T11:42:15Z",
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"oid": "e1be37dbfa6641b11c7f94c43fffc5e08045f647",
"body": "The cost-based planner (train/dispatch_pp: planner.py / profile.py / types.py) was never wired\ninto the live path -- _dispatch_pp_plan emits uniform aligned stages sized by\nSUROGATE_DISPATCH_STAGE_BLOCKS, and nothing imports the package except its own unit tests.\n\nMeasured whether wiring it would pa\n[…]\ntep) are untouched -- they exercise the C++ binding\ndirectly, not this package. (If a cost planner is ever wanted, it is recoverable from git.)\n\nCo-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>",
"is_bot": false,
"headline": "chore(dispatch-pp): remove the unused cost-based stage planner",
"author_name": "flaviusburca",
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"committed_at": "2026-06-22T11:00:40Z",
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{
"oid": "2de705cee56ec82c50c8ebeb4c81a64155547d75",
"body": "min_stages / upper_threshold / vram_budget_gb / recompute_grain were inputs for the cost-based\nstage planner (train/dispatch_pp/planner.py), which is not wired: the live planner emits uniform\naligned stages sized by SUROGATE_DISPATCH_STAGE_BLOCKS. The validator only setdefaulted these\ninto a dict th\n[…]\nig) for\nwhen it is wired (roadmap). Verified: clean config trains with loss parity; a config still\ncarrying the sub-block warns and ignores it.\n\nCo-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>",
"is_bot": false,
"headline": "refactor(dispatch-pp): drop the vestigial dispatch_pp sub-block params",
"author_name": "flaviusburca",
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"committed_at": "2026-06-22T10:42:32Z",
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"oid": "e51bf084061c405d0a0ba2fc516cf5cd2f1fcd76",
"body": "…gradient_accumulation_steps)\n\nThe chunks env predated grad-accum folding: back when dispatch ignored GA, it was the only way\nto raise the microbatch count M. Now that GA folds in, chunks and GA are identical multipliers\n(M = gpus*chunks*GA), so keeping both is redundant and a footgun (setting both \n[…]\nGA only.\n\nVerified: 0.8B dispatch + fp8, GA=4 with the old env set -> warns, ignores the env, M = gpus*GA\n= 8 (unchanged training vs the fold).\n\nCo-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>",
"is_bot": false,
"headline": "refactor(dispatch-pp): drop SUROGATE_DISPATCH_MICROBATCH_CHUNKS (use …",
"author_name": "flaviusburca",
"author_login": null,
"committed_at": "2026-06-22T10:31:03Z",
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{
"oid": "6d61d9ff887a70f2de356f2b98a1416ddf91e462",
"body": "…icrobatch count)\n\ndispatch-PP ignored gradient_accumulation_steps: the fused step ran M = gpus*chunks\nmicrobatches into one optimizer step and GA only mis-sized the epoch guard. Fold GA into the\nmicrobatch count for LoRA: M = gpus * SUROGATE_DISPATCH_MICROBATCH_CHUNKS * GA, all accumulated\ninto one\n[…]\nks*GA = 8, effective batch 8\";\ntrains smoothly (lower-variance loss/norm), tps up vs M=2 as the weight stream amortizes over\nmore microbatches.\n\nCo-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>",
"is_bot": false,
"headline": "feat(dispatch-pp): honor gradient_accumulation_steps (fold into the m…",
"author_name": "flaviusburca",
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"committed_at": "2026-06-22T10:26:46Z",
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"oid": "f7a44508832984b20da5edfa9cc877ac3f22810b",
"body": "The 27B dispatch-PP step is transfer-bound: each stage streams its frozen base weights from\npinned host over PCIe. Store + stream the frozen matmul block weights as FP8-E4M3 instead of\nBF16 -> half the bytes on the wire (and half the pin RAM), fed straight to the FP8 GEMM.\n\nHow:\n- At load (finalize_\n[…]\n8 noise.\n- 27B 4-GPU gated-delta LoRA: ~3.33s/step vs ~4.8s BF16 dispatch (~1.45x), loss parity\n (4.72->3.57), sensible norms, no OOM, no NaN.\n\nCo-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>",
"is_bot": false,
"headline": "feat(dispatch-pp): FP8 weight streaming (half the PCIe bytes per stage)",
"author_name": "flaviusburca",
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"committed_at": "2026-06-22T10:05:17Z",
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"oid": "0faf63d365c1cd0ebb026375967a501549d151da",
"body": "Lift the BF16-only guard for dispatch_pp to also accept recipe=fp8_hybrid (NVFP4 stays\ndeferred). With this, the streamed (still BF16) stage weights are quantized to FP8 on the\ndevice per call and fed to the FP8 GEMM -- FP8 compute, but the PCIe transfer is still BF16.\nThis is the foundation for FP8\n[…]\nmpute-bound shapes.\n\nVerified: 0.8B dispatch LoRA + fp8_hybrid runs with loss/norm parity vs the BF16 recipe\n(1.36/1.09/1.50/2.12, norm ~4-10).\n\nCo-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>",
"is_bot": false,
"headline": "feat(dispatch-pp): allow the fp8_hybrid recipe under dispatch-PP",
"author_name": "flaviusburca",
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"committed_at": "2026-06-22T08:18:32Z",
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"oid": "7f946cba915655ccf7ec3597d4146002882e6dd7",
"body": "The multi-GPU dispatch backward skips reduce_loss (the DP all-reduce would deadlock waiting\non idle GPUs), so ValidTokenCount was never populated and both the displayed loss and the\noptimizer grad-norm fell back to total-token (B*T*GradAccumSteps*world_size, incl padding)\nnormalization. On padded da\n[…]\neference (~1.0-2.0 / ~5-10); 27B 4-GPU gated-delta LoRA converges (loss 4.7->3.6) with sensible\nnorms (~5-16), no OOM, no step-time regression.\n\nCo-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>",
"is_bot": false,
"headline": "fix(dispatch-pp): valid-token normalization for the loss + grad norm",
"author_name": "flaviusburca",
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"committed_at": "2026-06-22T08:03:50Z",
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"oid": "a1b06b581ce05a5d5023c863af16dc6d23e69603",
"body": "This reverts commit 4b749c8fd89a1c133fa3800c20bdd04e009e463e.",
"is_bot": false,
"headline": "Revert \"feat(dispatch-pp): show the real grad norm in the loss display\"",
"author_name": "flaviusburca",
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"committed_at": "2026-06-22T07:43:30Z",
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"oid": "4b749c8fd89a1c133fa3800c20bdd04e009e463e",
"body": "The dispatch-PP step hardcoded norm=0.0 in the display, even though the optimizer already\ncomputes the gradient norm for clipping. Surface it: dispatch_pp_apply_optimizer now returns\nget_norm() (the same value the non-dispatch path shows), the multi-GPU trainer stashes it\n(mDispatchPpLastGradNorm) d\n[…]\ncolumn now reports real values (~1.5e3), loss parity unchanged.\nThe extra cost is one device->host read of an already-computed scalar per step.\n\nCo-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>",
"is_bot": false,
"headline": "feat(dispatch-pp): show the real grad norm in the loss display",
"author_name": "flaviusburca",
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"committed_at": "2026-06-22T07:36:44Z",
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"oid": "8654218149fd64f9c34eaf92b8609f72fae308ea",
"body": "…uire (+ pool foundation)\n\nMigrate every get-or-create of a persistent saved tensor (make_persistent_tensor chokepoint\nused by ~12 ops, the executor SaveForBwd persist paths, mamba, moe_permute) to\nSavedTensorCache::acquire(). allocate_moe_saved now returns null-on-arena-miss so the cache\nowns the f\n[…]\nupts the backward), and the win is marginal on the\ntransfer-bound 27B anyway. Documented in reset_saved_cache + the design doc for future work.\n\nCo-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>",
"is_bot": false,
"headline": "refactor(executor): route saved tensors through SavedTensorCache::acq…",
"author_name": "flaviusburca",
"author_login": null,
"committed_at": "2026-06-22T07:30:06Z",
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"oid": "08680dce145cdcfa46a50981ad58096290a02d73",
"body": "…FwdStack + SaveForBwd)\n\nrecompute:false sizes the per-layer activation arenas for the WHOLE model (FwdStack ~19GB,\nSaveForBwd ~16GB at seq 1024 on the 27B) -> OOM. But dispatch runs the backward one stage at\na time per GPU (sequential on a GPU; concurrent stages are on separate GPUs/arenas), so onl\n[…]\ncache, now\nSavedTensorCache) made dispatch-aware -- see 2026-06-22-dispatch-pp-recompute-false.md; the\nexample stays recompute:true until then.\n\nCo-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>",
"is_bot": false,
"headline": "feat(dispatch-pp): cyclic activation sectioning for recompute:false (…",
"author_name": "flaviusburca",
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"committed_at": "2026-06-22T06:44:06Z",
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"oid": "1afe4cee3531f4e8eda91a33a4b4d6f27af1b8bd",
"body": "…d tensors\n\nThe persistent saved-tensor buffers (gated-delta recurrent states, rope/qk-norm caches,\nMoE expert bookkeeping, and the SaveForBwd persist fallback) lived as three loose maps\n(mMoeSavedBuffers/Sizes/ArenaBacked) + a bump offset, mutated directly by ~18 call sites\neach with its own cudaMa\n[…]\n Qwen3.5-0.8B (recompute on/off) and Qwen3.6-27B (recompute\non), dispatch-PP. Sets up the recompute:false per-stage cache reset as a one-liner.\n\nCo-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>",
"is_bot": false,
"headline": "refactor(executor): SavedTensorCache -- one owner for persistent save…",
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"committed_at": "2026-06-22T06:43:32Z",
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"oid": "8988d574bcb505469dfa52c84bf307d945b21ccc",
"body": "…> ~3x faster load\n\nStartup was ~130-156s because each of the N per-GPU weight managers pins its OWN copy of\nthe frozen base in host memory: N x cudaHostAlloc of the full 52GB model (~208GB pinned at\nN=4, serialized on the kernel page-lock path). The base is read-only (LoRA), so all GPUs\ncan DMA-str\n[…]\n\nhost RAM 208GB+ -> 93GB. Loss parity preserved (0.8B and 27B). Only active for\noffload_master + frozen base (LoRA); all other paths unchanged.\n\nCo-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>",
"is_bot": false,
"headline": "perf(dispatch-pp): share frozen base across GPUs + register-on-anon -…",
"author_name": "flaviusburca",
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"committed_at": "2026-06-22T05:17:33Z",
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"oid": "e71dfcf012fb8ed1e95359bca23416143b6017c1",
"body": "…ions fit)\n\nStop force-enabling recompute for dispatch-PP. With it off, each stage backward saves its\nactivations during the re-forward instead of recomputing per block -- ~30% faster on\nmodels that fit (verified on 0.8B LoRA, loss parity). It still OOMs the 27B today because\nthe phase-arena sizer s\n[…]\nloc pinning+zeroing the 52GB host region (CPU/kernel-bound,\ndisk idle then a 1.4GB/s burst), not the mapped flag. Left the allocator unchanged.\n\nCo-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>",
"is_bot": false,
"headline": "feat(dispatch-pp): allow recompute:false (honored where stage activat…",
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"committed_at": "2026-06-21T22:36:27Z",
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"oid": "91d04471058fb94ee2fbf25440f349a7844e4afd",
"body": "…stream\n\nThe per-step weight stream is fixed (each small stage streams once and all microbatches\nreuse it), so running more microbatches per step amortizes that fixed cost over more\ntokens and keeps the cross-stage pipeline fuller (fewer bubbles) -- at no extra peak\nmemory, since microbatches run se\n[…]\n, seq 1024: chunks=1 -> 4096 tok / 5.3s = 773 tok/s;\nchunks=2 -> 8192 tok / 8.6s = 952 tok/s (~+23 percent). Loss parity preserved at chunks=1.\n\nCo-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>",
"is_bot": false,
"headline": "feat(dispatch-pp): microbatches-per-step knob to amortize the weight …",
"author_name": "flaviusburca",
"author_login": null,
"committed_at": "2026-06-21T21:35:29Z",
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"oid": "9075e2c667fe831010eedee7b249ff266ef453b9",
"body": "…oss-stage pipeline\n\nReplace the microbatch-diagonal wavefront with RoundPipe's actual schedule. Each layer\nrange is now a SMALL stage (~4 blocks, _dispatch_pp_plan: num_stages > gpus) dispatched\nround-robin to GPU s%N, where its weights are held resident across all M microbatches\n(enlarged streamin\n[…]\n on 4x32 GB -- 16 stages of 4 blocks, ~5 GB resident\nbase/GPU. The earlier '27B doesn't fit' was the big-stage (num_stages=gpus) bug, now gone.\n\nCo-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>",
"is_bot": false,
"headline": "feat(dispatch-pp): stage-level dispatch -- small resident stages + cr…",
"author_name": "flaviusburca",
"author_login": null,
"committed_at": "2026-06-21T21:13:20Z",
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"oid": "576511e9a0638849df6cde44a1df18f16230cb37",
"body": "…oundation)\n\nMake the streaming prefetch slot count a runtime value (mNumPrefetchBuffers, arrays ->\nvectors) instead of a fixed 2. Default stays 2 (per-block double-buffer, byte-identical);\ndispatch-PP raises it via env SUROGATE_DISPATCH_PREFETCH_BLOCKS so a whole small stage's\nblocks stay cached ac\n[…]\ns (stream a stage once/step, reuse for all M microbatches). Foundation for\nthe stage-level-dispatch rewrite; no behavior change at the default.\n\nCo-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>",
"is_bot": false,
"headline": "feat(dispatch-pp): configurable prefetch-slot count (stage-resident f…",
"author_name": "flaviusburca",
"author_login": null,
"committed_at": "2026-06-21T20:27:07Z",
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"oid": "3799c47c348f52b1f57b50de583952ab9ef29321",
"body": "…vel rewrite plan\n\nUnder offload_master, embedding/lm_head masters previously stayed resident on the GPU\n(~13 GB for a large-vocab 27B), since offload_master only offloaded block weights. For\nLoRA the non-block base is FROZEN -- its master is read once to populate the bf16 work\ncopy, then never agai\n[…]\n correction:\nthe 27B fits on 4x32 GB (235B fits on one 24 GB 4090); my earlier 'doesn't fit' was a\nbig-stage (num_stages=gpus) bug, not a wall.\n\nCo-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>",
"is_bot": false,
"headline": "feat(dispatch-pp): offload frozen non-block masters (LoRA) + stage-le…",
"author_name": "flaviusburca",
"author_login": null,
"committed_at": "2026-06-21T20:17:06Z",
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"oid": "deffd4cb314e50da34d5b8eb6cd208ae8e937821",
"body": "…(~2.2x on 27B)\n\nReplace the sequential stage walk with a diagonal wavefront over (stage, microbatch)\ntasks, dispatched async (dispatch_async) with a per-wave barrier. Stage s runs on GPU\ns%N; in wave w, microbatch m runs stage s=w-m (forward) / the mirror (backward), so a\nwave's tasks land on disti\n[…]\ntage-resident weights every task re-streams its stage. Stage-resident weight\ngathering (next) removes that M-fold re-stream for the larger win.\n\nCo-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>",
"is_bot": false,
"headline": "feat(dispatch-pp): wavefront scheduler -- overlap stages across GPUs …",
"author_name": "flaviusburca",
"author_login": null,
"committed_at": "2026-06-21T19:21:56Z",
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"oid": "b249fae3803da1732773dd0b419904891d9beaba",
"body": "…ulated)\n\ndispatch_pp_train_step_multigpu now processes M microbatches per step: each stage runs\nall M microbatches before the next stage, re-forwarding each (stage, microbatch) from a\nper-microbatch input boundary and grad-accumulating across them (start_micro_step(m, M),\nGradAccumSteps=M for the o\n[…]\n\ngathering lands -- the next piece. The machinery (and the async primitive from fb3879be)\nare the foundation the wavefront scheduler builds on.\n\nCo-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>",
"is_bot": false,
"headline": "feat(dispatch-pp): microbatch machinery in the stage step (grad-accum…",
"author_name": "flaviusburca",
"author_login": null,
"committed_at": "2026-06-21T18:58:07Z",
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"oid": "fb3879be015128fcb90e67a853458833594212d6",
"body": "…tion)\n\nAdd dispatch_async(work, gpu) / wait_gpu(gpu): launch work on one GPU without the\nglobal barrier of run_work, and wait per-GPU later. This is the foundation for the\npipelined stage scheduler, which must run different stages/microbatches on different\nGPUs concurrently (the current synchronous\n[…]\n yet (the primitive is not wired into the step). Verified the\nresident LoRA dispatch path is byte-identical (loss 0.3945/0.2835/0.5158/0.2920).\n\nCo-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>",
"is_bot": false,
"headline": "feat(dispatch-pp): async per-GPU dispatch primitive (scheduler founda…",
"author_name": "flaviusburca",
"author_login": null,
"committed_at": "2026-06-21T18:24:43Z",
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"oid": "b703b3dcf99282ad4f906a835e0d267d9914aa53",
"body": "Run the round-robin stage scheduler with offload_master too, instead of falling back\nto the normal streaming loop. With offload_master the base weights live in pinned CPU\nand the force-linear stage execution streams each block to the GPU on demand\n(gather_block / release_block via handle_layer_start\n[…]\nsible to hold resident --\n4 stages over 64 layers, loss descending, adapter saved. Also unchanged: resident\n0.8B LoRA/FFT (offload_master off).\n\nCo-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>",
"is_bot": false,
"headline": "feat(dispatch-pp): per-stage weight streaming -- 27B LoRA on 32GB GPUs",
"author_name": "flaviusburca",
"author_login": null,
"committed_at": "2026-06-21T17:32:20Z",
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"oid": "5dd28f6fbdb410b42ecb00bd7fb1d7d1c948b8ec",
"body": "The dispatch step previously rejected LoRA (BF16 full-FT only). Wire it through:\n\n- forward_stage / backward_stage no longer reject LoRA; they call\n ensure_lora_run_state and (backward) lora_grads().start_micro_step so the per-block\n adapters train on this GPU. The cross-GPU grad reduce stays skip\n[…]\n2 GPU) trains to completion at\nseq 512 and seq 2048, ~20x faster per step than FFT (only the small adapters are\ncollected/optimized/broadcast).\n\nCo-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>",
"is_bot": false,
"headline": "feat(dispatch-pp): LoRA support in the resident stage scheduler",
"author_name": "flaviusburca",
"author_login": null,
"committed_at": "2026-06-21T17:15:43Z",
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{
"oid": "e68c6e29c7467f403b92a0bae743fc39e1f3306d",
"body": "…leak)\n\nThe dispatch sub-range forwards/backwards run with skip_finalize so boundary tensors\nand saves survive the cross-GPU reads, but that leaves them resident on the\nbump-allocated compute stack. Nothing reset the stack between steps, so saves piled up\nstep over step (observed: ~1947 live allocat\n[…]\n\nVerified: surogate sft on Qwen3.5-0.8B (2 GPU) now runs 30 steps at seq 512 (FFT and\nLoRA) and 15 steps at seq 2048 without the stack growing.\n\nCo-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>",
"is_bot": false,
"headline": "fix(dispatch-pp): reset the compute stack each step (stop cross-step …",
"author_name": "flaviusburca",
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"committed_at": "2026-06-21T17:15:42Z",
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"body": "Two bugs made multi-stage dispatch converge worse than single-stage and, worse,\nnon-deterministically (same seed/data -> final loss varied 0.65..3.9 run to run):\n\n1. offload_residual raced the cross-stage forward handoff. The pipelined forward\n reads block hi's residual by name to hand it to the n\n[…]\n the 64-token overfit, vs 0.77 before) and is deterministic across runs.\nsurogate sft on Qwen3.5-0.8B trains to completion at seq 512 and 2048.\n\nCo-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>",
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"body": "… stage)\n\nThe dispatch step previously re-ran a WHOLE forward per stage to provide the\nbackward's activations, holding every block's saved input on the compute stack on\ntop of the per-block gated-delta backward temps -- which overflowed the stack at\nlonger sequence lengths (cannot save / std::bad_al\n[…]\nns to\ncompletion at seq 512 (both packing modes) AND seq 2048 -- previously OOMed at\nseq 512. Single-stage memory now scales to long sequences.\n\nCo-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>",
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"body": "…-end)\n\nrun_training_loop now routes parallelism=dispatch_pp (weights resident) through\ndispatch_pp_train_step_multigpu: builds a contiguous block->stage partition over the\nGPU pool, loads one micro-batch, and runs the round-robin fused step. With\noffload_master set (large models that can't fit resi\n[…]\nerflows the compute\nstack in the gated-delta backward (whole-forward-per-stage design) -- next: pipelined\nforward + per-stage weight streaming.\n\nCo-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>",
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"headline": "feat(dispatch-pp): wire the stage scheduler into surogate sft (end-to…",
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"body": "After weight streaming fixed the weight OOM, the next OOM was the save-for-backward\nactivation arena (per-layer residuals across all blocks). Dispatch-PP now forces\nrecompute + offload_residual so activation memory is independent of network depth\n(design 1.1) -- both weights and activations are bounded, so deep models (e.g.\nQwen3.6-27B / 64 layers) fit. LoRA keeps grads/optimizer on-GPU; FFT also offloads grads.\n\nCo-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>",
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"body": "…s load\n\nparallelism=dispatch_pp validated the config but never enabled the offload path it is\nbuilt on, so import_weights loaded every weight onto the GPU and OOM'd on any model big\nenough to need dispatch-PP (e.g. Qwen3.6-27B: 30 GB of weights resident). Mirror\ncpu_training's mapping: set offload_master (weights stream from pinned CPU per block)\nand, for full fine-tune, offload_grads. LoRA keeps its small grads/optimizer on-GPU.\n\nCo-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>",
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"body": "dispatch_pp_train_step_multigpu(..., stale=True) defers each step's optimizer update\nby one: this step's grads are collected and stashed while the *previous* step's grads\nare applied, so every step trains on weights one update behind -- the RoundPipe v1\nstaleness. dispatch_pp_flush_pending applies t\n[…]\ned update with compute needs the CPU-master + streaming\nintegration (documented). Test: test_phase3_train_step_multigpu.py::...stale_converges.\n\nCo-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>",
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"body": "The -pp example was a plain LoRA config missing the parallelism setting. Add\nparallelism: dispatch_pp + the dispatch_pp planner block (min_stages, upper_threshold,\nvram_budget_gb, recompute_grain) with explanatory comments. Validates through\nSFTConfig._validate_dispatch_pp_config (bf16+LoRA, CUDA graphs auto-disabled).\n\nCo-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>",
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"body": "…n hooks\n\nThese executor controls (op-range / layer-range selection, named cross-GPU inject,\npreserve-layer, skip-grad-reduce, stage-base restore, grad-norm / hidden readback)\nstarted as Phase-0 parity instrumentation but are now the load-bearing dispatch-PP\nexecution mechanism -- so the debug_/mDbg\n[…]\nose debug hooks (set_debug_dump_fn, debug_print_backward,\ndebug_tensors, mDebugDump*). No behavior change; full suite green (50 + 7 multi-GPU).\n\nCo-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>",
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"headline": "refactor(dispatch-pp): drop debug_/mDbg from the dispatch-PP executio…",
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"body": "Route the backward's trailing embedding op (layer<0, after all blocks) to the lowest\nstage and the leading loss/lm-head ops to the loss-owning stage, by op-index position\nrelative to the block-op span. Grad collection routes embedding from the lowest stage\nand lm_head/final_norm from the loss stage, so every parameter now trains (previously\nthe embedding was frozen). 2-GPU convergence unchanged (17.99 -> ... -> 0.95).\n\nCo-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>",
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"headline": "feat(dispatch-pp): unfreeze embedding in the multi-GPU dispatch step",
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"body": "…p debug from names\n\nMulti-GPU fused step (dispatch_pp_train_step_multigpu): backward dispatch round-robin\nacross the pool (stages on different GPUs, boundary grads handed GPU->host->GPU) ->\ncollect every stage's grads onto the master GPU by name -> optimizer there ->\nbroadcast updated weights to ev\n[…]\n-PP entry points, not throwaway debug probes.\n\nTests: test_phase3_train_step_multigpu (2-GPU convergence); full suite green\n(50 + 7 multi-GPU).\n\nCo-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>",
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"headline": "feat(dispatch-pp): multi-GPU fused training step that converges + dro…",
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"body": "…rges\n\ndispatch_pp_debug_train_step chains the sub-range executor's forward (loss) ->\nbackward (grad store) -> optimizer update into one real training step. Repeated on a\nfixed batch it drives the loss down monotonically (19.19 -> ... -> 1.03 over 15 steps)\nand matches the stream-driven trainer step\n[…]\nsize==1 keeps reduce_loss (populates ValidTokenCount for get_loss)\na safe local no-op. Test: tests/train/dispatch_pp/test_phase3_train_step.py.\n\nCo-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>",
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"headline": "feat(dispatch-pp): fused single-GPU dispatch training step that conve…",
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"body": "…+ AsyncStaleAdamW)\n\nAsyncOptimizer is a worker thread draining a depth-1 queue of update closures:\nsubmit(u_N) fences on u_{N-1} completing, enqueues u_N, and returns immediately --\nso u_N overlaps the caller and at most one update is ever in flight, which is exactly\nthe one-step staleness RoundPip\n[…]\n\nRemaining: wire the worker into a fused multi-GPU dispatch step() (per-layer\nparam/grad release) + the converges-on-a-real-run staleness test.\n\nCo-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>",
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"headline": "feat(dispatch-pp): async 1-step-stale optimizer core (AsyncOptimizer …",
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"committed_at": "2026-06-21T12:26:59Z",
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"body": "…g test\n\nExpose DslWeightManager::gpu_prefetch_buffer_bytes / prefetch_slot_count and the\nMultiGPUPyTrainer::dispatch_pp_debug_weight_residency snapshot (total device-resident\nweight bytes, streaming block double-buffer footprint, slot count).\n\nQuantitatively pins the dispatch-PP memory invariant: w\n[…]\nger) report no slots. Test: tests/train/dispatch_pp/\ntest_phase2_memory.py (resident => 0 slots; streaming => slot_count blocks, < NUM_LAYERS).\n\nCo-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>",
"is_bot": false,
"headline": "feat(dispatch-pp): GPU weight-residency introspection + memory-scalin…",
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"body": "…rad handoff\n\nBackward stages run in reverse order (loss-owning stage first), one GPU per stage\non the full batch, handing boundary gradients GPU->host->GPU with no NCCL.\n\nStage ops are selected by their owning block layer [lo..hi], not an op-index range:\nboundary view ops (d_blocks[L].mlp_down -> .\n[…]\ng), and\nreduce_loss_on_completion on the request. The debug forward/backward also force the\neager (non-stream-driven) path for the same reason.\n\nCo-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>",
"is_bot": false,
"headline": "feat(dispatch-pp): multi-GPU backward dispatch parity via cross-GPU g…",
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"committed_at": "2026-06-21T12:01:56Z",
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"oid": "df83a9130eecff5cd2a80364ad1a9bc99720fb42",
"body": "…ry handoff\n\nComplete the cross-GPU activation handoff: the fused-residual block carries two\ntensors across a stage boundary -- blocks[hi].res_att (residual after attention)\nand blocks[hi].mlp_down (x). Read both by name on the sending GPU (kept live by a\npreserve-last-block hook) and bind them by n\n[…]\nnce for 2-stage and round-robin-wrap; parity test no longer xfail.\n\nRemoves the superseded get_residual/BlockHOut inject hooks (wrong buffers).\n\nCo-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>",
"is_bot": false,
"headline": "feat(dispatch-pp): multi-GPU forward dispatch parity via named bounda…",
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"committed_at": "2026-06-21T10:56:05Z",
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"oid": "b307f0cf6bd44e3edaad6c6faeac740b5f993406",
"body": "…erve hook)\n\nAdd set_debug_preserve_layer to keep a stage's last block's stack live past its\nlayer-end, so the carried x (prev block's MLP output, BlockMLPDown) survives for\nthe cross-GPU boundary read; align the x read/inject to the BlockHOut->MLPDown->\nResidualAtt fallback chain. Residual accumula\n[…]\nvia a StackedBlocks\ncarried tid not materialized on a fresh executor, so full parity stays xfail with\nthe precise remaining blocker documented.\n\nCo-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>",
"is_bot": false,
"headline": "feat(dispatch-pp): sender-side x capture for multi-GPU boundary (pres…",
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"committed_at": "2026-06-21T10:29:35Z",
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"oid": "7bc5b705287930347cdf478be616f9224409ec6c",
"body": "…andoff)\n\nAdd the C++ runtime that dispatches contiguous block stages round-robin across the\nstateless GPU pool (stage i -> GPU i%ngpu) via run_work, handing the boundary state\nGPU->host->GPU: MultiGPUPyTrainer::dispatch_pp_debug_forward_hidden_multigpu, plus\nCompiledExecutor residual/block-output i\n[…]\nous block's output) is a transient slot not resident on a fresh executor,\nso it needs a boundary-materialization hook. Test gated on free GPUs.\n\nCo-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>",
"is_bot": false,
"headline": "feat(dispatch-pp): multi-GPU round-robin forward dispatch (residual h…",
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"committed_at": "2026-06-21T10:16:51Z",
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"body": "…model)\n\nDerive real BlockProfiles from a checkpoint: per-block work-weight bytes from the\nsafetensors header, activation working-set from model dims + runtime shape,\nsize-proportional fwd/bwd times. plan_for_model() produces a NUMA-placed StagePlan\nwith operating-envelope warnings; resolve_vram_budget_bytes() handles the\nvram_budget_gb/auto resolution. Validated end-to-end vs Qwen3-0.6B (28 blocks).\n\nCo-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>",
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"headline": "feat(dispatch-pp): planner->model integration (profile.py + plan_for_…",
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"committed_at": "2026-06-21T09:43:27Z",
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"oid": "808e1eba0cd27992782d8536c06183f5e40943c1",
"body": "Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>",
"is_bot": false,
"headline": "docs(dispatch-pp): record Phase-1 single-GPU streaming verdict (PASS)",
"author_name": "flaviusburca",
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"body": "Weights streamed per block from pinned CPU (offload_master) produce bit-identical\nforward hidden states and per-block grad norms vs resident, through the dispatch-PP\nexecutor path. Reuses the existing DslWeightManager gather/release machinery; no new\nC++. Authors the Phase-1 plan.\n\nCo-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>",
"is_bot": false,
"headline": "feat(dispatch-pp): Phase-1 single-GPU weight-streaming parity test",
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"committed_at": "2026-06-21T09:34:32Z",
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"body": "Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>",
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"headline": "docs(dispatch-pp): record Phase-0 sub-range feasibility verdict (PASS)",
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"oid": "5a0b0ec5b7f460dc7ab68c664fff67d1a1f59820",
"body": "… gate\n\nAdd debug-only bounded op-range execution to the compiled executor (guarded,\ndefault-off) and GraphExecutor/DslModel/MultiGPUPyTrainer debug entry points.\nForward: two contiguous block sub-ranges share one executor state with the\nboundary residual round-tripped through host memory, matching \n[…]\nthe bounded forced-eager executor matches whole-graph per-block\ngrad norms (rtol 2e-2). Validated on Qwen3-0.6B/4-layer single GPU. Gate: PASS.\n\nCo-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>",
"is_bot": false,
"headline": "feat(dispatch-pp): Phase-0 GraphExecutor sub-range execution + parity…",
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"committed_at": "2026-06-21T09:23:14Z",
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"body": "Decision gate passes: transformer-block boundaries are cleanly separable;\nops[layer_start_indices[i], layer_end_indices[j]) is a contiguous sub-graph with\nonly the residual hidden state crossing boundaries. Documents the bounded\nop-range execution design for the sub-range parity spike.\n\nCo-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>",
"is_bot": false,
"headline": "feat(dispatch-pp): Phase-0 Step-1 findings header (gate verdict: PASS)",
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"committed_at": "2026-06-21T08:42:31Z",
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"body": "Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>",
"is_bot": false,
"headline": "feat(dispatch-pp): add parallelism=dispatch_pp config + v1 validations",
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"body": "…ation\n\nCo-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>",
"is_bot": false,
"headline": "feat(dispatch-pp): NUMA placement assignment + LoRA needs_grad propag…",
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"body": "Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>",
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"headline": "feat(dispatch-pp): PCIe token-threshold operating-envelope warning",
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"body": "…M ceiling\n\nCo-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>",
"is_bot": false,
"headline": "feat(dispatch-pp): warn when a single block exceeds the per-stage VRA…",
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"body": "Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>",
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"headline": "feat(dispatch-pp): cost-search plan assembly (fwd/fused-tail/bwd)",
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"body": "- src/tips.ts: 72 tips distilled from the surogate guides (precision/\n recipes, qlora, offloading, memory, optimizers, performance, moe,\n rl/grpo, long-context, multi-gpu, metrics), each context-tagged.\n- insights.ts: activeTags(state) derives live context (recipe, lora,\n qlora, moe, grpo, hot/lo\n[…]\nd ROTATES over time → live feed.\n- Splash tips now pull grounded general tips (rotated per launch); removed\n the terminal-specific tip.\n- Tests: 25 pass (tag filter + rotation + nvfp4/moe relevance).",
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{
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{
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{
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{
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{
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"code": "weights_renormalized",
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"components": [
{
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{
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}
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{
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"points": 0,
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"details": [
{
"code": "no_data",
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],
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{
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{
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{
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],
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},
{
"key": "engineering",
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"metrics": [
{
"key": "engineering_practices",
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"note": null,
"notes": [],
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{
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{
"key": "tests_present",
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{
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{
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{
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{
"key": "openssf_scorecard_ci_tests",
"name": "OpenSSF Scorecard: CI-Tests",
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]
},
{
"key": "documentation",
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"inputs": {
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"components": [
{
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{
"key": "documentation_directory",
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{
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{
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]
}
],
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},
{
"key": "security",
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"weight": 0.16,
"metrics": [
{
"key": "security_posture",
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"note": null,
"notes": [],
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{
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{
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{
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"points": 0,
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{
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"points": 2.5,
"status": "met",
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{
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"status": "met",
"details": [],
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{
"key": "dependency_update_tool",
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"points": 0,
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{
"key": "fuzzing",
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{
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{
"key": "maintained",
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{
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"points": 5,
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"details": [],
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{
"key": "pinned_dependencies",
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"points": 0,
"status": "missed",
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{
"key": "sast",
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"points": 0,
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},
{
"key": "security_policy",
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"points": 0,
"status": "missed",
"details": [],
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},
{
"key": "signed_releases",
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"status": "missed",
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},
{
"key": "token_permissions",
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},
{
"key": "vulnerabilities",
"name": "Vulnerabilities",
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"points": 6.8,
"status": "partial",
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}
]
},
{
"key": "high_risk_jurisdiction_exposure",
"band": "excellent",
"name": "High-Risk Jurisdiction Exposure",
"note": "Only high-confidence self-published location evidence affects this multiplier. Ambiguous matches are review-only; country evidence is not proof of nationality, citizenship, legal registration, malicious intent, or sanctions status.",
"notes": [
{
"code": "jurisdiction_evidence_limits",
"params": {}
}
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"Iran",
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],
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},
{
"key": "ai_readiness",
"band": "moderate",
"name": "AI Readiness",
"value": 65,
"weight": 0,
"metrics": [
{
"key": "ai_agent_context",
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"note": null,
"notes": [],
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"inputs": {
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{
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"details": [],
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},
{
"key": "legible_commit_history",
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{
"key": "ai_verify_loop",
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{
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{
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"description": "How well is the repo equipped to be developed and maintained with AI coding agents? An independent, experimental badge — weight 0.0, so it is surfaced on its own and does not affect the overall health score."
}
],
"metrics_version": "1.13.0"
},
"warnings": [
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"GitHub dependency-graph SBOM unavailable (404); the dependency graph may be disabled for this repository"
],
"report_type": "repository",
"generated_at": "2026-07-21T15:50:26.018639Z",
"schema_version": "0.23.0",
"badge_url": "https://raw.githubusercontent.com/inspect-software/badges/main/v1/i/invergent-ai/surogate.svg",
"full_name": "invergent-ai/surogate",
"license_state": "standard",
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}
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Scores are signals, not warranties. They reflect publicly visible practices on GitHub — not a code audit, and not a security guarantee.
Missing data is excluded and weights renormalized, never scored as zero. Methodology is versioned and open: metrics v1.13.0, schema v0.23.0 — full methodology · metrics wiki.
