Text Classification
Transformers
Safetensors
modernbert
agent-safety
tool-calling
long-context
distillation
Eval Results (legacy)
text-embeddings-inference
Instructions to use ProCreations/auto-0.4b-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ProCreations/auto-0.4b-2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ProCreations/auto-0.4b-2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ProCreations/auto-0.4b-2") model = AutoModelForSequenceClassification.from_pretrained("ProCreations/auto-0.4b-2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_plan.json from ProCreations/auto-0.4b-2: direct link, hf CLI and curl.
- Browser
- Download file 3.54 kB
-
https://huggingface.co/ProCreations/auto-0.4b-2/resolve/main/training_plan.json
- Command line
-
hf download hf://ProCreations/auto-0.4b-2/training_plan.json
-
curl -L -o training_plan.json https://huggingface.co/ProCreations/auto-0.4b-2/resolve/main/training_plan.json
3.54 kB
| { | |
| "experiment": "auto04-v3", | |
| "authorization": "User requested improving ProCreations/auto-0.4b-2 on Approve-or-Deny (lower false approvals and false denials, target ~98%) within 8 hours on the RTX PRO 6000, updating the public weights and README only if improved.", | |
| "initialization": "Pinned published auto-0.4b-2 (revision 456e153d), full parameters.", | |
| "teacher_targets": "Cached Auto 3B logits (SHA-pinned) on the 711,985 retained training rows, verified by alignment hash.", | |
| "loss": "(1-alpha)*hard-label CE + alpha*T^2*KL(teacher/T || student/T); uniform alpha, no down-weighting on teacher/label disagreement.", | |
| "runs": [ | |
| { | |
| "name": "kd_a", | |
| "epochs": 1, | |
| "lr": 1e-05, | |
| "alpha": 0.8, | |
| "temperature": 2.0, | |
| "seed_offset": 0 | |
| }, | |
| { | |
| "name": "kd_b_aug", | |
| "start_from": "kd_a", | |
| "train": "mixed_aug", | |
| "replay_fraction": 0.25, | |
| "max_replay_length": 4096, | |
| "epochs": 1, | |
| "lr": 5e-06, | |
| "alpha": 0.8, | |
| "temperature": 2.0, | |
| "seed_offset": 3, | |
| "evals_per_epoch": 4 | |
| }, | |
| { | |
| "name": "kd_c", | |
| "epochs": 1, | |
| "lr": 1.2e-05, | |
| "alpha": 0.8, | |
| "temperature": 2.0, | |
| "seed_offset": 11, | |
| "subsample": 0.5, | |
| "evals_per_epoch": 4, | |
| "deny_weight": 1.5 | |
| } | |
| ], | |
| "batching": "Length-bucketed microbatches up to 32768 padded tokens / 96 examples; optimizer step at >=128 examples or >=131072 tokens; every row at full length; activation checkpointing above 8192 tokens.", | |
| "schedule": "3% warmup, cosine to 10% of peak.", | |
| "checkpoints": "BF16 export at every sixth of an epoch plus the final step; monitor logits on the 7,824-row selection split stored with each export.", | |
| "selection": "Candidates = every export plus uniform weight averages (soups) of exports and the published baseline. Ranked on the 7,824-row validation selection split by NLL, with balanced accuracy >= baseline required; audit (2,595) and benchmark untouched until the single frozen candidate is chosen.", | |
| "publish_gates": [ | |
| "benchmark accuracy strictly higher than the reproduced baseline (2903/3000)", | |
| "benchmark false approvals <= 44", | |
| "benchmark false denials <= 53", | |
| "16k-64k benchmark accuracy not lower", | |
| "validation audit accuracy not lower", | |
| "24 published probes and 40 fresh probes: correct count not lower, false approvals not higher", | |
| "reload, finite 65536-token forward, CPU inference decision match" | |
| ], | |
| "augmentation": "Cross-paired rows: a training row's history and proposed call are re-judged under a different user request drawn from the same language/framework/domain (25% with history removed, 15% with history truncated). Labels are the Auto 3B teacher's logits only; no synthetic hard labels. Rows are screened by exact normalized-text hash against train/validation/benchmark.", | |
| "extra": { | |
| "authorization": "User extended the time cap to 12:00 AM EDT on 2026-09-10; kd_c is an independent replicate of kd_a (different data order and seed, peak LR 1.2e-5) from the pinned published student, added as a model-soup member. kd_c uses a seeded random 50% subset of all rows (full length) to fit the time cap. After a user-requested benchmark preview of kd_a's final export (96.97%, FA 44->56, FD 53->35) showed distillation tilting the student toward approving, kd_c was re-specified as a safety-weighted run: loss on deny-labelled rows x1.5.", | |
| "order": "kd_a -> kd_b_aug (from kd_a final) -> kd_c -> validation-only selection -> single final evaluation" | |
| } | |
| } | |