Instructions to use apus-ailab/APUS-OpenJev-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use apus-ailab/APUS-OpenJev-v1 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("apus-ailab/APUS-OpenJev-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download 4B-5949/training.md from apus-ailab/APUS-OpenJev-v1: direct link, hf CLI and curl.
- Browser
- Download file 2.69 kB
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https://huggingface.co/apus-ailab/APUS-OpenJev-v1/resolve/main/4B-5949/training.md
- Command line
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hf download hf://apus-ailab/APUS-OpenJev-v1/4B-5949/training.md
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curl -L -o training.md https://huggingface.co/apus-ailab/APUS-OpenJev-v1/resolve/main/4B-5949/training.md
2.69 kB
| # This release: 4B, step 5949 | |
| This is the completed 5949-step SFT endpoint. | |
| # Training provenance | |
| Both families use the registered 5949-record SFT curriculum (3898 parent groups), with 5949 maximum steps and one epoch. Each checkpoint's recorded training step, epoch, and original trainer-state hash are preserved in the separate [LoRA archive manifest](https://huggingface.co/gump2049/xDAN-openJet-LoRA-Checkpoints/blob/1e5557f923746031f8187b7daf299b9bee41cb3c/manifest.json) at fixed revision `1e5557f923746031f8187b7daf299b9bee41cb3c` (repository access required). This merged package's `release-manifest.json` inventories inference artifacts and does not contain that per-checkpoint trainer-state record; intermediate checkpoints did not finish the whole schedule. Randomized loader order means the step number alone is not a verified count of unique examples seen at an intermediate checkpoint. | |
| | Source | Scheduled records | Parent groups | | |
| |---|---:|---:| | |
| | Mind2Web browser Choice | 1798 | 671 | | |
| | Mind2Web browser TYPE | 158 | 125 | | |
| | HelpSteer3 principle | 1300 | 1300 | | |
| | SGD | 513 | 10 | | |
| | GoEmotions independent-attribute Score | 500 | 297 | | |
| | BoolQ | 600 | 600 | | |
| | MNLI | 1000 | 1000 | | |
| | Local counterfactual | 80 | 20 | | |
| Parent groups can overlap across browser Choice/TYPE. The 4B run initialized from a 427-record pilot adapter (weight SHA256 `0047e5f1f0c98f17da93def94032041997592e1a609ddcab58de41f4d30e3a38`), while the inspected 9B config records no origin adapter. Do not add pilot records to the registered schedule as if all were independent. | |
| Decision objective: `0.5 CE(low) + 0.5 CE(high) + 0.1 KL(P_high.detach || P_low)`. TYPE examples use full-depth text cross-entropy. LoRA r=8, alpha=16, dropout=0; learning rate 1e-4; batch size 1; seed 20260920; training max length 6144. The recorded compiled schedule maximum is 5845 tokens. Training settings and data/schedule hashes are retained in the separate [LoRA checkpoint depth configuration](https://huggingface.co/gump2049/xDAN-openJet-LoRA-Checkpoints/blob/1e5557f923746031f8187b7daf299b9bee41cb3c/4b/checkpoint-5949/depth_config.json) at fixed archive revision `1e5557f923746031f8187b7daf299b9bee41cb3c`. The merged package's `depth_config.json` contains only portable inference and identity metadata; it is not the full training configuration. | |
| This is SFT with a within-model distillation term; it does not prove RLCD, online reinforcement learning or teacher-model OPD occurred. The declared public sources contain multiple licensing regimes (including CC-BY, CC-BY-SA and mixed-source material); separate provenance and redistribution review remains necessary. Raw datasets are not part of this upload. | |