Text Classification
Transformers
Safetensors
English
lfm2_vl
feature-extraction
decision-model
typed-decisions
system-one
shell
guardrail
command-risk
lfm2
liquid
lora
custom_code
Eval Results (legacy)
Instructions to use yannelli/d1-3b-cli-command-risk with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yannelli/d1-3b-cli-command-risk with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="yannelli/d1-3b-cli-command-risk", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("yannelli/d1-3b-cli-command-risk", trust_remote_code=True) model = AutoModel.from_pretrained("yannelli/d1-3b-cli-command-risk", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 693 Bytes
e1e980e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | {
"fitted_on": "yannelli/cli-command-risk decisions built by training/build_cli_decisions.py, validation split (472 cases)",
"apply": "p -> softmax(log(p) / T), per question",
"temperatures": {
"risk_level": 2.246,
"risky": 2.261,
"data_destruction": 1.677,
"device_overwrite": 2.318,
"overbroad_permissions": 1.796,
"privilege_escalation": 2.398,
"remote_code_execution": 0.236,
"unsafe_download_exec": 0.204,
"resource_exhaustion": 0.071,
"credential_exposure": 0.068,
"irreversible_config": 0.112,
"history_log_tampering": 1.361,
"network_disruption": 0.196,
"signal_kill": 0.136,
"shell_footgun": 2.116,
"reversible": 2.536,
"requires_root": 1.962
}
} |