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 validation/parity/compare_mlx_affine8-g64_base_vs_block.json from chaoliangUNSW/Jev-Style-2B-Decision-v3-MLX: direct link, hf CLI and curl.
- Browser
- Download file 7.7 kB
-
https://huggingface.co/chaoliangUNSW/Jev-Style-2B-Decision-v3-MLX/resolve/11ce5d718e22412b38624bb863a1eee73c0a5934/validation/parity/compare_mlx_affine8-g64_base_vs_block.json
- Command line
-
hf download hf://chaoliangUNSW/Jev-Style-2B-Decision-v3-MLX@11ce5d718e22412b38624bb863a1eee73c0a5934/validation/parity/compare_mlx_affine8-g64_base_vs_block.json
-
curl -L -o compare_mlx_affine8-g64_base_vs_block.json https://huggingface.co/chaoliangUNSW/Jev-Style-2B-Decision-v3-MLX/resolve/11ce5d718e22412b38624bb863a1eee73c0a5934/validation/parity/compare_mlx_affine8-g64_base_vs_block.json
7.7 kB
| { | |
| "format": "8bit", | |
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| "overall": { | |
| "n": 43, | |
| "top1_agreement": 1.0, | |
| "top1_flips": 0, | |
| "max_abs_dscore": 0.4653351306915283, | |
| "mean_abs_dscore": 0.023077200647273945, | |
| "mean_kl": 0.0006975644872166873, | |
| "n_gold": 43, | |
| "nll_ref": 0.670433307194272, | |
| "nll_cand": 0.6868139442506376, | |
| "dnll": 0.01638063705636561, | |
| "mean_abs_row_dnll": 0.030139399526949388, | |
| "acc_ref": 0.7209302325581395, | |
| "acc_cand": 0.7209302325581395, | |
| "acc_drop_pp": 0.0 | |
| }, | |
| "per_category": { | |
| "catalogue_overflow": { | |
| "n": 3, | |
| "top1_agreement": 1.0, | |
| "top1_flips": 0, | |
| "max_abs_dscore": 0.2735496163368225, | |
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| "mean_kl": 0.0012829640019479873, | |
| "n_gold": 3, | |
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| "nll_cand": 0.46349011750257935, | |
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| "mean_abs_row_dnll": 0.039379824994875905, | |
| "acc_ref": 0.6666666666666666, | |
| "acc_cand": 0.6666666666666666, | |
| "acc_drop_pp": 0.0 | |
| }, | |
| "catalogue_overflow_long_state": { | |
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| "multi_block_3k": { | |
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| "multi_block_5k": { | |
| "n": 2, | |
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| "acc_cand": 1.0, | |
| "acc_drop_pp": 0.0 | |
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| "multi_block_9k": { | |
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| "acc_ref": 0.5, | |
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| }, | |
| "multi_question": { | |
| "n": 10, | |
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| "acc_ref": 0.7, | |
| "acc_cand": 0.7, | |
| "acc_drop_pp": 0.0 | |
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| "prefix_boundary": { | |
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| "qtype_choice": { | |
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| "max_abs_dscore": 0.039629340171813965, | |
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| "acc_ref": 0.0, | |
| "acc_cand": 0.0, | |
| "acc_drop_pp": 0.0 | |
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| "qtype_noul": { | |
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| "qtype_score": { | |
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| "mean_kl": 1.4547309373278143e-05, | |
| "n_gold": 1, | |
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| "acc_ref": 0.0, | |
| "acc_cand": 0.0, | |
| "acc_drop_pp": 0.0 | |
| }, | |
| "short_sb": { | |
| "n": 6, | |
| "top1_agreement": 1.0, | |
| "top1_flips": 0, | |
| "max_abs_dscore": 0.12482678890228271, | |
| "mean_abs_dscore": 0.01820988009964663, | |
| "mean_kl": 0.0005014431591724873, | |
| "n_gold": 6, | |
| "nll_ref": 0.11428482960768895, | |
| "nll_cand": 0.123207743102961, | |
| "dnll": 0.00892291349527205, | |
| "mean_abs_row_dnll": 0.008996485578208901, | |
| "acc_ref": 1.0, | |
| "acc_cand": 1.0, | |
| "acc_drop_pp": 0.0 | |
| } | |
| }, | |
| "gates": { | |
| "coverage_finite_tokens": { | |
| "pass": true, | |
| "problems": {}, | |
| "reference_missing_requests": 0 | |
| }, | |
| "top1": { | |
| "threshold": 0.98, | |
| "value": 1.0, | |
| "pass": true | |
| }, | |
| "dnll": { | |
| "threshold": 0.02, | |
| "value": 0.01638063705636561, | |
| "pass": true | |
| }, | |
| "acc_drop_pp": { | |
| "threshold": 0.3, | |
| "value": 0.0, | |
| "resolution_pp": 2.3255813953488373, | |
| "pass": null, | |
| "note": "UNRESOLVED: 43 gold questions -> one flip = 2.33pp > 0.3pp; this fixture cannot resolve the gate (use the gate fixture with >= 334 rows; statistical power needs far more)" | |
| } | |
| }, | |
| "verdict": "INCONCLUSIVE", | |
| "resolution_pp_per_flip": 2.3255813953488373, | |
| "note": "small fixtures cannot resolve 0.3pp accuracy gates; accuracy on a parity fixture is only indicative (synthetic long rows carry the base row's gold label).", | |
| "ref": "runs/macjev/runtime_v2_dev/reference_trained/ref_block_fp32.jsonl", | |
| "cand": "runs/macjev/release_2b/parity/pred_mlx_affine8-g64_base.jsonl", | |
| "fixture": "runs/macjev/runtime_v2_dev/reference_base/fixture.jsonl" | |
| } |