Instructions to use arcadia-impact/dispatch-models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use arcadia-impact/dispatch-models with PEFT:
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- Notebooks
- Google Colab
- Kaggle
Commit History
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glm45_air_1b/charter: training metadata + built mixes
Verified signatureOption C metadata for the 1B-presented charter row -- midtrain, dolci and the four AFT cells -- on the same terms as the other rows: loss curves, train logs, traces, provenance and the axolotl configs. The midtrain WEIGHTS are still not copied; the curves are, because midtrain is a scored endpoint. Replaces training/midtrain/trainer_state.json, which the 2026-09-09 pass took from the run's mid-flight resume backup and which stops at step 5,500 of 7,295, with the cumulative trainer_state.final.json. data/ carries the row's built mixes: the 250M charter cut (corpus.jsonl), leg_a and dolmino. Not included: followups/glm-aft-grid-8192-v1-1b-attempt1, which was still running when this was planned and publishes its LoRA weights to GCS rather than the Hub.df7a695
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training metadata (loss curves, logs, provenance, configs) + built training mixes (part 2)
Verified signatureOption C metadata for every stage INCLUDING midtrain -- the midtrain weights are deliberately not copied, but `midtrain_381` is a scored endpoint in all 13 grid rows, so the curves belong here. trainer_state.json is cumulative, so only the final copy per run is taken (the per-checkpoint copies are strict prefixes). debug.log and the run sentinels are excluded as orchestration exhaust. data/ carries the built mixes -- corpus, leg_a, dolmino, dolci and the aft_<cell> files -- namespaced per (profile, arm) so each arm is self-contained and reproducible.5870451 -
training metadata (loss curves, logs, provenance, configs) + built training mixes
Verified signatureOption C metadata for every stage INCLUDING midtrain -- the midtrain weights are deliberately not copied, but `midtrain_381` is a scored endpoint in all 13 grid rows, so the curves belong here. trainer_state.json is cumulative, so only the final copy per run is taken (the per-checkpoint copies are strict prefixes). debug.log and the run sentinels are excluded as orchestration exhaust. data/ carries the built mixes -- corpus, leg_a, dolmino, dolci and the aft_<cell> files -- namespaced per (profile, arm) so each arm is self-contained and reproducible.c00963a