Text Generation
PEFT
Safetensors
English
ai-control
deception-detection
scheming-detection
monitoring
knowledge-distillation
lora
research-artifact
Eval Results (legacy)
Instructions to use Jazhyc/Gleipnir-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Jazhyc/Gleipnir-9B with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3.5-9B") model = PeftModel.from_pretrained(base_model, "Jazhyc/Gleipnir-9B") - Notebooks
- Google Colab
- Kaggle
Download training_summary.json from Jazhyc/Gleipnir-9B: direct link, hf CLI and curl.
- Browser
- Download file 1.7 kB
-
https://huggingface.co/Jazhyc/Gleipnir-9B/resolve/main/training_summary.json
- Command line
-
hf download hf://Jazhyc/Gleipnir-9B/training_summary.json
-
curl -L -o training_summary.json https://huggingface.co/Jazhyc/Gleipnir-9B/resolve/main/training_summary.json
1.7 kB
| { | |
| "adapter": { | |
| "causal_master": { | |
| "bytes": 931170120, | |
| "dtypes": [ | |
| "torch.float32" | |
| ], | |
| "sha256": "277b27e43ac90a893a03e789232a154d1bc7f84c057eb85ec9cc4e5eb60d7cdf", | |
| "tensor_count": 256 | |
| }, | |
| "serving_copy": { | |
| "bytes": 931173960, | |
| "dtypes": [ | |
| "torch.float32" | |
| ], | |
| "sha256": "32aabf31a87935a156676807cc132fb99879d20f66d89b6b39148d5176cb25af", | |
| "tensor_count": 256 | |
| } | |
| }, | |
| "base_model": { | |
| "id": "Qwen/Qwen3.5-9B", | |
| "revision": "c202236235762e1c871ad0ccb60c8ee5ba337b9a" | |
| }, | |
| "evaluation": { | |
| "benchmark": "six-source strict-OOD action-only tool-trajectory suite", | |
| "mean_ood_auroc": 0.9215356108161963, | |
| "mean_ood_pauroc_at_20": 0.8211832158363769, | |
| "pooled_auroc": 0.9355070171618869, | |
| "pooled_pauroc_at_20": 0.7836630889831644, | |
| "primary_metric": "unweighted six-source Mean-OOD raw normalized pAUROC@20", | |
| "rows": 6395, | |
| "sources": [ | |
| "AgentDojo", | |
| "CUA-SHADE", | |
| "IaC", | |
| "MLE-Sabotage", | |
| "Rogue Eval", | |
| "SHADE-Arena" | |
| ] | |
| }, | |
| "source_commit": "f408b2ae5c45b743b342e45a0d63a952b30debea", | |
| "training": { | |
| "data_rows": 21837, | |
| "deception_rows": 13149, | |
| "effective_batch_size": 32, | |
| "epochs": 1, | |
| "learning_rate": 5e-05, | |
| "lora_alpha": 256, | |
| "max_length": 29696, | |
| "objective": "binary soft BCE at selected literal 0|1 logits", | |
| "peak_cuda_memory_allocated_bytes": 44427638784, | |
| "quantization": "NF4 double-quantized QLoRA with BF16 compute", | |
| "rank": 128, | |
| "runtime_seconds": 23946.5855, | |
| "seed": 0, | |
| "teacher": "moonshotai/kimi-k3", | |
| "tool_trajectory_rows": 8688, | |
| "trainable_parameters": 232783872 | |
| } | |
| } | |