Instructions to use macmacmacmac/Sev-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use macmacmacmac/Sev-4B with PEFT:
from peft import PeftModel from transformers import AutoModel base_model = AutoModel.from_pretrained("Qwen/Qwen3.5-4B-Base") model = PeftModel.from_pretrained(base_model, "macmacmacmac/Sev-4B") - Notebooks
- Google Colab
- Kaggle
File size: 925 Bytes
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"rows_sha256": "026b68705b7ac76bc4361bc2fda1cbfe74265b436a807ea68bca77f87d3cf9da",
"partition": "calibration",
"question_counts_by_source": {
"agnews": 48,
"amazon": 16,
"banking77": 16,
"boolq": 16,
"compositional": 40,
"dbpedia14": 16,
"excytin_file": 80,
"excytin_parent_join": 80,
"excytin_process": 120,
"excytin_signin": 120,
"imdb": 16,
"legacy_policy": 40,
"mnli": 16,
"sev_guide_detector": 1071,
"sev_guide_triage": 169,
"sev_swarmtraces_response_policy": 140,
"sev_swarmtraces_static": 211,
"sev_sysmon_native_file": 72,
"sev_sysmon_native_join": 86,
"sev_sysmon_native_other_effect": 96,
"sev_sysmon_native_process": 69,
"sev_sysmon_native_registry": 72,
"sst5": 16,
"trec": 16,
"yelp": 32
},
"group_count": 465,
"cv_grouping": "Exact group IDs across source families; one constant source stratum"
}
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