Instructions to use macmacmacmac/CLEF-DualScreen with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LiteRT
How to use macmacmacmac/CLEF-DualScreen with LiteRT:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
CLEF dual-task proper-protocol FT (seed 20261101, epoch 3) from jev-retrain MI355X run 2026-11-04
073de96 verified |
Download README.md from macmacmacmac/CLEF-DualScreen: direct link, hf CLI and curl.
- Browser
- Download file 1.94 kB
-
https://huggingface.co/macmacmacmac/CLEF-DualScreen/resolve/main/README.md
- Command line
-
hf download hf://macmacmacmac/CLEF-DualScreen/README.md
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curl -L -o README.md https://huggingface.co/macmacmacmac/CLEF-DualScreen/resolve/main/README.md
1.94 kB
| language: | |
| - en | |
| license: other | |
| tags: | |
| - security | |
| - prompt-injection | |
| - litert | |
| # CLEF-DualScreen | |
| Dual-task redo fine-tune of the CLEF-Flash JointSchemaHead | |
| (121,762,820 parameters, 122 tensors) on frozen Cloudflare | |
| CLEF-Flash LiteRT backbone hidden states. Only the head is trained. | |
| Repository root is a complete CLEF LiteRT checkpoint directory. | |
| ## Training | |
| - Compute: DigitalOcean droplet jev-retrain (AMD Instinct MI355X), | |
| finished 2026-11-04 21:36 UTC (training proper; see truncation note). | |
| - Method: ft_clef.py original small AgentDojo protocol, 3 epochs, | |
| batch 16, lr 1e-4, pos_weight 3.0, seed 20261101. | |
| - Data: train 569 / dev 149 / test 220 rows (workspace+travel | |
| train/dev, banking+slack test), both tasks mixed. | |
| - Selected checkpoint: epoch 3 (saved_best by combined dev AUC). | |
| - Full log: redo-training-log.jsonl. Hashes: redo-provenance.json. | |
| ## Evaluation (read before citing) | |
| Dev is tiny (filter 65 rows, guard 84 rows) — treat all numbers as | |
| noisy development measurements. | |
| | split | tool_output_filter AUC | action_authorization_guard AUC | | |
| |---|---|---| | |
| | Dev epoch 0 (untrained) | 0.9781 | 0.9704 | | |
| | Dev epoch 3 (training log) | 0.9829 | 0.9846 | | |
| | Test 220 rows (independent local LiteRT rescore) | 1.0000 | 0.7731 | | |
| Guard test detail (local rescore): TPR/FPR @0.35 = 0.983/0.600, | |
| @0.5 = 0.967/0.517, @0.9 = 0.833/0.367. The guard task collapses | |
| across domains (dev FPR@0.5 0.089 vs test 0.517) while the filter | |
| task separates perfectly on this small test. Score statistic matches | |
| training: filter P("true"), guard P("2") + 0.5*P("1"). Per-row test | |
| scores: redo-test-scores-local.jsonl. | |
| Truncation note: the droplet process was killed by SSH session-scope | |
| deactivation during its own test-eval phase, after epoch-3 training | |
| and checkpoint save completed. Test numbers above are therefore an | |
| independent local rescore of the saved head, not the droplet log. | |
| No frozen-chain replication exists yet. | |