Text Classification
MLX
Safetensors
jev-style
qwen3_5
decision-model
decision-making
system-one
calibration
long-context
qwen3.5
apple-silicon
on-device
llm-routing
guardrails
Instructions to use chaoliangUNSW/Jev-Style-2B-Decision-v3-MLX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use chaoliangUNSW/Jev-Style-2B-Decision-v3-MLX with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download chaoliangUNSW/Jev-Style-2B-Decision-v3-MLX --local-dir Jev-Style-2B-Decision-v3-MLX
- jev-style
How to use chaoliangUNSW/Jev-Style-2B-Decision-v3-MLX with jev-style:
# Apple silicon pip install "jev-style[mlx]"
from jev_style import JevStyle, noul, choice js = JevStyle.from_pretrained("chaoliangUNSW/Jev-Style-2B-Decision-v3-MLX") 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"]) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
Download figures/jevbench.data.json from chaoliangUNSW/Jev-Style-2B-Decision-v3-MLX: direct link, hf CLI and curl.
- Browser
- Download file 2.39 kB
-
https://huggingface.co/chaoliangUNSW/Jev-Style-2B-Decision-v3-MLX/resolve/main/figures/jevbench.data.json
- Command line
-
hf download hf://chaoliangUNSW/Jev-Style-2B-Decision-v3-MLX/figures/jevbench.data.json
-
curl -L -o jevbench.data.json https://huggingface.co/chaoliangUNSW/Jev-Style-2B-Decision-v3-MLX/resolve/main/figures/jevbench.data.json
2.39 kB
| { | |
| "chart": "jevbench", | |
| "metric": "JevBench v1.4.1 public accuracy (231 items)", | |
| "rows": [ | |
| { | |
| "label": "Jev-Style 2B v3 (this model)", | |
| "accuracy": 0.7359307359307359, | |
| "accuracy_pct_1dp": 73.6, | |
| "source": "https://huggingface.co/chaoliangUNSW/Jev-Style-2B-Decision-v3/blob/main/validation/benchmarks/jevbench_v1.4.1_results.json :: public_accuracy (170/231)" | |
| }, | |
| { | |
| "label": "decider-2b (Mapika, Qwen3.5-2B-Base)", | |
| "accuracy": 0.70995670995671, | |
| "accuracy_pct_1dp": 71.0, | |
| "source": "https://github.com/fstandhartinger/jevbench/blob/v1.4.1/results/v1.4.1/jevbench-v1.4.1-results.json :: systems[decider-2b]" | |
| }, | |
| { | |
| "label": "Open-Jev 2B (Zefan Cai, Qwen3.5-2B + LoRA)", | |
| "accuracy": 0.645021645021645, | |
| "accuracy_pct_1dp": 64.5, | |
| "source": "https://github.com/fstandhartinger/jevbench/blob/v1.4.1/results/v1.4.1/jevbench-v1.4.1-results.json :: systems[open-jev-zefan-2b]" | |
| }, | |
| { | |
| "label": "Jev-Style 0.8B v3", | |
| "accuracy": 0.6406926406926406, | |
| "accuracy_pct_1dp": 64.1, | |
| "source": "https://huggingface.co/chaoliangUNSW/Jev-Style-0.8B-Decision-v3/blob/main/figures/jevbench.data.json (0.8B v3 card; row Jev-Style 0.8B v3, 148/231)" | |
| }, | |
| { | |
| "label": "Laya (ModernBERT-large, 421M)", | |
| "accuracy": 0.5844155844155844, | |
| "accuracy_pct_1dp": 58.4, | |
| "source": "https://github.com/fstandhartinger/jevbench/blob/v1.4.1/results/v1.4.1/jevbench-v1.4.1-results.json :: systems[laya]" | |
| } | |
| ], | |
| "reference_line": { | |
| "label": "Jev 1.13.0", | |
| "accuracy": 0.8658008658008658, | |
| "source": "https://github.com/fstandhartinger/jevbench/blob/v1.4.1/results/v1.4.1/jevbench-v1.4.1-results.json :: systems[jev-1.13.0]" | |
| }, | |
| "ci95_wilson_2b": [ | |
| 0.6755578394149232, | |
| 0.7885850787366094 | |
| ], | |
| "board_systems_higher_than_2b": 42, | |
| "board_systems": 82, | |
| "board_sha256": "e6754863056503fe2b010410fc7111df884ac1f9ce4449aa369aab61d98092cd", | |
| "footnote": "JevBench v1.4.1, 231 public items. 2B v3: self-run once with the official harness (commit 24b9b5c), GGUF F16 engine, one global temperature, not an official board entry; 95% CI 67.6-78.9% (Wilson), so its lead over decider-2b (164/231) is inside the CI; training-pool contamination scan: 0 hits. Other rows: public accuracy as published in the board's v1.4.1 results file. Shown: the Qwen3.5-2B-family systems on the board, our 0.8B v3, Laya and Jev; 42 of the 82 board systems score higher than 73.6%." | |
| } |