Text Generation
PEFT
Safetensors
English
ai-control
agent-monitoring
sabotage-detection
tool-use
monitoring
knowledge-distillation
lora
research-artifact
Eval Results (legacy)
Instructions to use Jazhyc/Gleipnir-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Jazhyc/Gleipnir-4B with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3.5-4B") model = PeftModel.from_pretrained(base_model, "Jazhyc/Gleipnir-4B") - Notebooks
- Google Colab
- Kaggle
Download training_summary.json from Jazhyc/Gleipnir-4B: direct link, hf CLI and curl.
- Browser
- Download file 2.86 kB
-
https://huggingface.co/Jazhyc/Gleipnir-4B/resolve/main/training_summary.json
- Command line
-
hf download hf://Jazhyc/Gleipnir-4B/training_summary.json
-
curl -L -o training_summary.json https://huggingface.co/Jazhyc/Gleipnir-4B/resolve/main/training_summary.json
2.86 kB
| { | |
| "adapter": { | |
| "causal_master": { | |
| "bytes": 2038466576, | |
| "dtypes": [ | |
| "torch.float32" | |
| ], | |
| "sha256": "0716efc123175c2714ebc24a16d5348a4e357050036f99d8568293d74d6ad579", | |
| "tensor_count": 256 | |
| }, | |
| "serving_copy": { | |
| "bytes": 2038470416, | |
| "dtypes": [ | |
| "torch.float32" | |
| ], | |
| "sha256": "4eec7b62b9af53f144bf727bb77c204bcaa71c770766e97b360bb4536a1fd775", | |
| "tensor_count": 256 | |
| } | |
| }, | |
| "base_model": { | |
| "id": "Qwen/Qwen3.5-4B", | |
| "revision": "851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a" | |
| }, | |
| "evaluation": { | |
| "benchmark": "six-source strict-OOD action-only tool-trajectory suite", | |
| "bf16_serving": { | |
| "id_macro_auroc": 0.9782143570726712, | |
| "id_macro_pauroc_at_20": 0.9331950861436367, | |
| "mean_ood_auroc": 0.9210723661622273, | |
| "mean_ood_pauroc_at_20": 0.803876420508685, | |
| "pooled_auroc": 0.9383858013950402, | |
| "pooled_pauroc_at_20": 0.7670527336884218 | |
| }, | |
| "id_macro_auroc": 0.9710830300280053, | |
| "id_macro_pauroc_at_20": 0.9116742836759423, | |
| "id_rows": 3012, | |
| "mean_ood_auroc": 0.9137947103322152, | |
| "mean_ood_pauroc_at_20": 0.770274594973614, | |
| "pooled_auroc": 0.9173121618351605, | |
| "pooled_pauroc_at_20": 0.7004981300464136, | |
| "primary_metric": "unweighted six-source Mean-OOD raw normalized pAUROC@20", | |
| "primary_serving": "optimized (FP8 attention projections, FP4 MLP/GDN, MXFP8 attention)", | |
| "rows": 6395, | |
| "source": "results/qad-v02-optimized01 and results/qad-v02-bf16-01", | |
| "sources": [ | |
| "AgentDojo", | |
| "CUA-SHADE", | |
| "IaC", | |
| "MLE-Sabotage", | |
| "Rogue Eval", | |
| "SHADE-Arena" | |
| ] | |
| }, | |
| "source_commit": "67c9ba502d8e02761177ad0a255932bdb8c71ca4", | |
| "training": { | |
| "data_filter": "strict teacher/source-label agreement on the 8,688 tool-trajectory rows", | |
| "data_rows": 7836, | |
| "deception_rows": 0, | |
| "effective_batch_size": 32, | |
| "epochs": 1, | |
| "learning_rate": 5e-05, | |
| "lora_alpha": 768, | |
| "max_length": 29696, | |
| "objective": "binary soft BCE at selected literal 0|1 logits", | |
| "peak_cuda_memory_allocated_bytes": null, | |
| "quantization": "QAD through NVFP4 MLP/GDN, FP8 attention projections and MXFP8 attention; FP32 master LoRA", | |
| "rank": 384, | |
| "recipe": "v0.2 (exact three-seed average) followed by same-model quantization-aware distillation: 245 updates, KL T=1, AdamW 1e-5, distilling v0.2's own BF16 scores on the 7,836 training rows through the optimized-serving quantization", | |
| "runtime_seconds": 3859.4864804700046, | |
| "seed": [ | |
| 0, | |
| 1, | |
| 2 | |
| ], | |
| "teacher": "moonshotai/kimi-k3", | |
| "tool_trajectory_rows": 7836, | |
| "trainable_parameters": null, | |
| "weight_average": "rank-384 LoRA stacking the three rank-128 seed adapters (A stacked, B/3, alpha 768); exactly the mean update, max relative error 2.2e-8" | |
| } | |
| } | |