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 NOTICE from chaoliangUNSW/Jev-Style-0.8B-Decision-v3: direct link, hf CLI and curl.
- Browser
- Download file 1.97 kB
-
https://huggingface.co/chaoliangUNSW/Jev-Style-0.8B-Decision-v3/resolve/656ca59be52fd6100eb5dba7bc9e79cddcd09251/NOTICE
- Command line
-
hf download hf://chaoliangUNSW/Jev-Style-0.8B-Decision-v3@656ca59be52fd6100eb5dba7bc9e79cddcd09251/NOTICE
-
curl -L -o NOTICE https://huggingface.co/chaoliangUNSW/Jev-Style-0.8B-Decision-v3/resolve/656ca59be52fd6100eb5dba7bc9e79cddcd09251/NOTICE
1.97 kB
| chaoliangUNSW/Jev-Style-0.8B-Decision-v3 | |
| Copyright 2026 chaoliangUNSW. Licensed under the Apache License, Version 2.0 (see LICENSE). | |
| This model is a fine-tuned derivative of Qwen3.5-0.8B (https://huggingface.co/Qwen/Qwen3.5-0.8B, | |
| revision 2fc06364715b967f1860aea9cf38778875588b17), Copyright 2026 Alibaba Cloud, licensed under the Apache | |
| License, Version 2.0. The LICENSE file in this repository is the license file distributed with Qwen3.5-0.8B. | |
| Modifications relative to Qwen3.5-0.8B: | |
| - all text-model weights were fine-tuned (full fine-tuning, bf16 training) to score typed decision questions | |
| (choice / score / true-false) with a verdict readout: logit(" yes") - logit(" no") at one " ->" slot per | |
| option; calibration temperatures were fitted afterwards (readout_config.json); | |
| - the vision tower (model.visual.*) and the multi-token-prediction head (mtp.*) were removed; the checkpoint | |
| is a text-only Qwen3_5ForCausalLM with tied input/output embeddings; | |
| - added the runtime script, readout/release configuration files, the integrity manifest and this NOTICE. | |
| The question types (choice / score / noul) follow the typed-decision convention of Laya | |
| (https://github.com/NandhaKishorM/laya, Apache-2.0) so both models can be evaluated on the same | |
| inputs. No Laya code or weights are included. | |
| Third generation (v3) of the Jev-Style decision series. Earlier generations: v1 = | |
| chaoliangUNSW/Jev-Style-Qwen3.5-2B-Decision (public GGUF release: chaoliangUNSW/Jev-Style-Qwen3.5-2B-Decision-GGUF) and | |
| v2 = chaoliangUNSW/Jev-Style-Qwen3.5-2B-Decision-v2, both 2B models built on Qwen3.5-2B-Base. v3 is fine-tuned from | |
| Qwen3.5-0.8B; no weights of v1 or v2 were reused. | |
| Not affiliated with, endorsed by or connected to TypeSafe or Jev. "Jev-Style" only describes the kind | |
| of model (a small typed-decision model in a similar style); no Jev weights, code or outputs are included. | |
| Not affiliated with or endorsed by Alibaba Cloud / the Qwen team or the Laya authors. | |