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")# 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/design_table.data.json from chaoliangUNSW/Jev-Style-0.8B-Decision-v3: direct link, hf CLI and curl.
- Browser
- Download file 5.31 kB
-
https://huggingface.co/chaoliangUNSW/Jev-Style-0.8B-Decision-v3/resolve/main/figures/design_table.data.json
- Command line
-
hf download hf://chaoliangUNSW/Jev-Style-0.8B-Decision-v3/figures/design_table.data.json
-
curl -L -o design_table.data.json https://huggingface.co/chaoliangUNSW/Jev-Style-0.8B-Decision-v3/resolve/main/figures/design_table.data.json
5.31 kB
| { | |
| "chart": "design_table", | |
| "columns": [ | |
| "Jev-Style 2B v1", | |
| "Jev-Style 2B v2", | |
| "Jev-Style 0.8B v3" | |
| ], | |
| "rows": [ | |
| { | |
| "row": "Parameters", | |
| "Jev-Style 2B v1": { | |
| "main": "2B", | |
| "sub": "Qwen3.5-2B-Base" | |
| }, | |
| "Jev-Style 2B v2": { | |
| "main": "2B", | |
| "sub": "continued from v1" | |
| }, | |
| "Jev-Style 0.8B v3": { | |
| "main": "0.8B", | |
| "sub": "752M text-model params" | |
| } | |
| }, | |
| { | |
| "row": "Training", | |
| "Jev-Style 2B v1": { | |
| "main": "LoRA rank 16", | |
| "sub": "all linear layers" | |
| }, | |
| "Jev-Style 2B v2": { | |
| "main": "LoRA rank 32", | |
| "sub": "33.6M trainable params" | |
| }, | |
| "Jev-Style 0.8B v3": { | |
| "main": "Full fine-tune", | |
| "sub": "every weight trained" | |
| } | |
| }, | |
| { | |
| "row": "Readout", | |
| "Jev-Style 2B v1": { | |
| "main": "Option-letter token", | |
| "sub": "one letter per option" | |
| }, | |
| "Jev-Style 2B v2": { | |
| "main": "Option-letter token", | |
| "sub": "' A' ... ' Z'" | |
| }, | |
| "Jev-Style 0.8B v3": { | |
| "main": "Verdict slot per option", | |
| "sub": "every option scored, one pass" | |
| } | |
| }, | |
| { | |
| "row": "Options per decision", | |
| "Jev-Style 2B v1": { | |
| "main": "Up to 26", | |
| "sub": "20 via top_logprobs" | |
| }, | |
| "Jev-Style 2B v2": { | |
| "main": "2-26", | |
| "sub": "letter-capped" | |
| }, | |
| "Jev-Style 0.8B v3": { | |
| "main": "No letter cap", | |
| "sub": "tested with 77 options" | |
| } | |
| }, | |
| { | |
| "row": "Context", | |
| "Jev-Style 2B v1": { | |
| "main": "Not stated", | |
| "sub": "quickstart: server default" | |
| }, | |
| "Jev-Style 2B v2": { | |
| "main": "1,024-token prompt", | |
| "sub": "quickstart runs -c 2048" | |
| }, | |
| "Jev-Style 0.8B v3": { | |
| "main": "25,600 tokens", | |
| "sub": "preregistered 25K claim passed" | |
| } | |
| }, | |
| { | |
| "row": "Languages", | |
| "Jev-Style 2B v1": { | |
| "main": "English", | |
| "sub": "five English task families" | |
| }, | |
| "Jev-Style 2B v2": { | |
| "main": "English", | |
| "sub": "English state required" | |
| }, | |
| "Jev-Style 0.8B v3": { | |
| "main": "51 evaluated", | |
| "sub": "MASSIVE locales; 19 in fine-tuning" | |
| } | |
| }, | |
| { | |
| "row": "Questions per state read", | |
| "Jev-Style 2B v1": { | |
| "main": "1", | |
| "sub": "one question per prompt" | |
| }, | |
| "Jev-Style 2B v2": { | |
| "main": "1", | |
| "sub": "one question per prompt" | |
| }, | |
| "Jev-Style 0.8B v3": { | |
| "main": "Many", | |
| "sub": "all questions in one call" | |
| } | |
| }, | |
| { | |
| "row": "Q4_K_M file", | |
| "Jev-Style 2B v1": { | |
| "main": "1.3 GB", | |
| "sub": "as reported on the v1 GGUF card" | |
| }, | |
| "Jev-Style 2B v2": { | |
| "main": "1.27 GB", | |
| "sub": "as reported on the v2 GGUF card" | |
| }, | |
| "Jev-Style 0.8B v3": { | |
| "main": "0.53 GB", | |
| "sub": "matches FP32 on 240/240 parity rows" | |
| } | |
| }, | |
| { | |
| "row": "Typed decisions, teacher agreement", | |
| "Jev-Style 2B v1": { | |
| "main": "53.35%", | |
| "sub": "2,000 decisions / 400 states" | |
| }, | |
| "Jev-Style 2B v2": { | |
| "main": "73.45%", | |
| "sub": "same 2,000 decisions" | |
| }, | |
| "Jev-Style 0.8B v3": { | |
| "main": "79.15%", | |
| "sub": "same 2,000 \u00b7 1,583 correct" | |
| } | |
| } | |
| ], | |
| "numbers": { | |
| "v1_q4_k_m_agreement": 0.944, | |
| "v2_q4_k_m_agreement": 0.914, | |
| "v3_q4_k_m_agreement": { | |
| "agree": 240, | |
| "n": 240, | |
| "value": 1.0 | |
| }, | |
| "typed_teacher_agreement": { | |
| "v1": 0.5335, | |
| "v2": 0.7345, | |
| "v3": 0.7915, | |
| "v3_correct": 1583, | |
| "n": 2000, | |
| "states": 400 | |
| }, | |
| "params": { | |
| "v1": "2B", | |
| "v2": "2B", | |
| "v3_text_model": 752393024 | |
| }, | |
| "size_ratio_v3_over_2b": 0.4, | |
| "context_ratio_v3_over_v2_prompt": 25.0 | |
| }, | |
| "sources": { | |
| "v1": "https://huggingface.co/chaoliangUNSW/Jev-Style-Qwen3.5-2B-Decision-GGUF/raw/main/README.md (file table 'Same decision as bf16' Q4_K_M 94.4%; 'LoRA rank 16 on all linear layers'; 'Up to 26 options (20 when ... top_logprobs)'; 'Trained on five English task families'; quickstart llama-server without -c; prompt has one [Question])", | |
| "v2": "https://huggingface.co/chaoliangUNSW/Jev-Style-Qwen3.5-2B-Decision-v2-GGUF/raw/main/README.md (Q4_K_M 91.4% choice agreement vs CUDA BF16 on frozen 500-decision subset; '2-26 unique options, within a 1,024-token prompt'; 'English state'; quickstart -c 2048; rank-32 LoRA, 33,638,400 trainable; continued from v1; ' A' through ' Z' readout; typed-decisions 53.35% v1 / 73.45% v2, 2,000 decisions from 400 states)", | |
| "v3": "chart_data.json: design.generations (export_manifest.json parameters_text_model=752393024; config.resolved.json readout=verdict, no LoRA keys; jevbench/results.json scorer.max_len=25600; apps.jsonl banking77_full 400 rows x 77 options; scoreboard long_grid_plus claim_25k_ok=true), quant.v3 gguf-q4_k_m 240/240 (export_r2/main/parity/report.json), typed.accuracy.v3 1583/2000 (typed_test.jsonl, 400 group_ids)" | |
| }, | |
| "footnote": "v1/v2: as reported on their public Hugging Face cards (v1 GGUF card; v2 and v2-GGUF cards; v1's typed-decisions number is reported on the v2 card). v3: release manifest, training config and eval files; Q4_K_M size = exported file (GB = 10^9 bytes), parity rows drawn from the training pool. Typed decisions: same 2,000 decisions from 400 states; v1/v2 scored by the v2 card's harness, v3 by ours. In-domain for v3; v1 not trained on typed decisions; v2's pool included typed workflow decisions. '51 evaluated' = MASSIVE locales (14 trained, 37 held out); fine-tuning covers 19 languages. 25,600 tokens = the runtime's whole-input limit; 25x = vs v2's 1,024-token prompt." | |
| } |