Text Classification
jev-style
Safetensors
Transformers
qwen3_5_text
text-generation
decision-model
system-one
calibration
classification
long-context
multilingual
qwen3.5
on-device
llm-routing
guardrails
Instructions to use chaoliangUNSW/Jev-Style-0.8B-Decision-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- jev-style
How to use chaoliangUNSW/Jev-Style-0.8B-Decision-v3 with jev-style:
pip install "jev-style[torch]"
from jev_style import JevStyle, noul, choice js = JevStyle.from_pretrained("chaoliangUNSW/Jev-Style-0.8B-Decision-v3") out = js.decide("I was charged twice for one order.", { "billing": noul("This message is about billing."), "team": choice("Which team should handle it?", ["billing", "shipping", "tech"]), }) print(out["answers"]["team"]["choice"]) - Transformers
How to use chaoliangUNSW/Jev-Style-0.8B-Decision-v3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="chaoliangUNSW/Jev-Style-0.8B-Decision-v3")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("chaoliangUNSW/Jev-Style-0.8B-Decision-v3") model = AutoModelForCausalLM.from_pretrained("chaoliangUNSW/Jev-Style-0.8B-Decision-v3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download figures/beyond_laya.json from chaoliangUNSW/Jev-Style-0.8B-Decision-v3: direct link, hf CLI and curl.
- Browser
- Download file 4.36 kB
-
https://huggingface.co/chaoliangUNSW/Jev-Style-0.8B-Decision-v3/resolve/656ca59be52fd6100eb5dba7bc9e79cddcd09251/figures/beyond_laya.json
- Command line
-
hf download hf://chaoliangUNSW/Jev-Style-0.8B-Decision-v3@656ca59be52fd6100eb5dba7bc9e79cddcd09251/figures/beyond_laya.json
-
curl -L -o beyond_laya.json https://huggingface.co/chaoliangUNSW/Jev-Style-0.8B-Decision-v3/resolve/656ca59be52fd6100eb5dba7bc9e79cddcd09251/figures/beyond_laya.json
4.36 kB
| { | |
| "chart": "beyond_laya", | |
| "unit": "percent (value x 100)", | |
| "source": "runs/macjev/report_r2/scoreboard/scoreboard.json via hf_staging/v3_card/chart_data.json", | |
| "delta_ci_method": "case-cluster bootstrap within suites, 2000 resamples (scoreboard n_boot)", | |
| "rows": [ | |
| { | |
| "task": "Banking77", | |
| "metric": "accuracy, 77 intents", | |
| "v3": 0.6825, | |
| "best_laya": 0.4925, | |
| "best_laya_checkpoint": "typed", | |
| "delta_pts": 19.0, | |
| "delta_ci95_pts": [ | |
| 13.99, | |
| 24.0 | |
| ], | |
| "n": 400, | |
| "v3_entry": "banking77.v3", | |
| "laya_entry": "banking77.laya_best", | |
| "scoreboard_field": "metrics['banking77']", | |
| "protocol_label": "v3: held-out (banking77 never trained; CLINC150/HWU64 intents are) / Laya: held-out. Laya numbers: official checkpoints re-run by us on identical rows, shipped temperatures." | |
| }, | |
| { | |
| "task": "Jailbreak", | |
| "metric": "balanced accuracy", | |
| "v3": 0.9036238842064085, | |
| "best_laya": 0.8311462000782389, | |
| "best_laya_checkpoint": "multilingual", | |
| "delta_pts": 7.25, | |
| "delta_ci95_pts": [ | |
| 2.99, | |
| 11.43 | |
| ], | |
| "n": 400, | |
| "v3_entry": "theme.guardrails_jailbreak.balanced_accuracy.v3", | |
| "laya_entry": "theme.guardrails_jailbreak.balanced_accuracy.laya_best", | |
| "scoreboard_field": "metrics['theme.guardrails_jailbreak.balanced_accuracy']", | |
| "protocol_label": "v3: held-out source (other permissive jailbreak sets + teacher) / Laya: held-out. Laya numbers: official checkpoints re-run by us on identical rows, shipped temperatures." | |
| }, | |
| { | |
| "task": "Toxicity", | |
| "metric": "macro-F1", | |
| "v3": 0.7089254055198327, | |
| "best_laya": 0.41360068097985436, | |
| "best_laya_checkpoint": "multilingual", | |
| "delta_pts": 29.53, | |
| "delta_ci95_pts": [ | |
| 24.65, | |
| 34.37 | |
| ], | |
| "n": 400, | |
| "v3_entry": "theme.moderation_toxicity.macro_f1.v3", | |
| "laya_entry": "theme.moderation_toxicity.macro_f1.laya_best", | |
| "scoreboard_field": "metrics['theme.moderation_toxicity.macro_f1']", | |
| "protocol_label": "v3: held-out source (civil_comments + teacher; toxic-chat eval-only) / Laya: held-out. Laya numbers: official checkpoints re-run by us on identical rows, shipped temperatures." | |
| }, | |
| { | |
| "task": "Model routing", | |
| "metric": "accuracy", | |
| "v3": 0.9624060150375939, | |
| "best_laya": 0.6591478696741855, | |
| "best_laya_checkpoint": "typed", | |
| "delta_pts": 30.33, | |
| "delta_ci95_pts": [ | |
| 26.06, | |
| 35.09 | |
| ], | |
| "n": 399, | |
| "v3_entry": "theme.model_routing_domain.v3", | |
| "laya_entry": "theme.model_routing_domain.laya_best", | |
| "scoreboard_field": "metrics['theme.model_routing_domain']", | |
| "protocol_label": "v3: near-domain (teacher-written; gsm8k/mbpp/AG rows eval-only) / Laya: held-out. Laya numbers: official checkpoints re-run by us on identical rows, shipped temperatures." | |
| }, | |
| { | |
| "task": "MASSIVE intent", | |
| "metric": "37 held-out locales", | |
| "v3": 0.6551351351351351, | |
| "best_laya": 0.36108108108108106, | |
| "best_laya_checkpoint": "multilingual", | |
| "delta_pts": 29.41, | |
| "delta_ci95_pts": [ | |
| 27.54, | |
| 31.3 | |
| ], | |
| "n": 3700, | |
| "v3_entry": "massive51.unseen37.macro_accuracy.v3", | |
| "laya_entry": "massive51.unseen37.macro_accuracy.laya_best", | |
| "scoreboard_field": "metrics['massive51.unseen37.macro_accuracy']", | |
| "protocol_label": "v3: held-out (37 languages never trained) / Laya: held-out. Laya numbers: official checkpoints re-run by us on identical rows, shipped temperatures." | |
| } | |
| ], | |
| "footnote": "Laya numbers: official checkpoints (English, typed-decisions, multilingual) re-run by us on identical rows with their shipped temperatures and\ndefault token budgets; the best of the three is shown per task. v3 trained on same-kind tasks from other datasets, never on these eval rows:\nintent (CLINC150/HWU64; Banking77 never trained), jailbreak (other permissive sets + teacher), toxicity (civil_comments + teacher; toxic-chat\neval-only), routing (teacher-written; gsm8k/mbpp/AG rows eval-only), MASSIVE in 14 other locales (no MASSIVE rows in these 37).\nn = 400 / 400 / 400 / 399 / 3,700 (37 x 100). Every gap's paired 95% bootstrap CI excludes zero. Plotted values: figures/beyond_laya.json." | |
| } |