Text Classification
Transformers
Safetensors
English
qwen3_5_text
text-generation
system-one
system-two
blocks-of-experts
typed-decisions
decision-model
calibrated-probabilities
knowledge-distillation
jev
noul
choice
score
lora
qwen3_5
dual-head
vllm
multimodal
vision
computer-use
robotics
Eval Results (legacy)
Instructions to use autotrust/JEV-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use autotrust/JEV-9B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="autotrust/JEV-9B")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("autotrust/JEV-9B") model = AutoModelForCausalLM.from_pretrained("autotrust/JEV-9B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,007 Bytes
c3ee780 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 | {
"version": 1,
"per_kind": {
"noul": 1.0022178236691426,
"choice": 0.9840233476014968,
"score": 1.012184473350984
},
"per_kind_family": {},
"fit": {
"noul": {
"n": 4363,
"T": 1.0022178236691426,
"kl_before": 0.004595278762280941,
"kl_after": 0.0045948573388159275
},
"choice": {
"n": 3418,
"T": 0.9840233476014968,
"kl_before": 0.04468083754181862,
"kl_after": 0.04462483152747154
},
"score": {
"n": 3173,
"T": 1.012184473350984,
"kl_before": 0.022512147203087807,
"kl_after": 0.022472143173217773
}
},
"diagnostic_calibration_split": {
"raw": {
"kl": 0.02229316346347332,
"ece": 0.0014972193181917403,
"mce": 0.0034518837928771973
},
"calibrated": {
"kl": 0.02226393297314644,
"ece": 0.0013502779446837457,
"mce": 0.005257606506347656
}
},
"source": "checkpoints/s2_9b_epoch1/best",
"fit_rows": 10954,
"d1_excluded": true
} |