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kaishuiji
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Model Details
Model Provider
kaishuiji
Model Tree
Qwen/Qwen3-4B
Input Modalities
Output Modalities
Supported Functionality
Same shape as v8 — reads a dialogue turn, emits an AffectDelta JSON.
What changed:
anticipation: {valence, arousal, weight} so the consumer can model
forward-looking mood, not just present.| use case | adapter |
|---|---|
| Tight inter-rater on 24 labels, consumer GPU, fast | v8 |
| Finer mixed-feeling resolution, anticipation modeling, 4B GPU available | v9 |
~800 SFT samples, hand-curated Chinese-first dialogue snippets. Data not released to protect contributor privacy. The schema and label vocabulary are documented; reproduction requires your own corpus.
24 v8 labels retained (backward-compatible) plus 26 new. Anchor table
follows the same (valence, arousal) format as v8 — see
label_anchors_v9.json in this repo for the full table.
python
from peft import PeftModelfrom transformers import AutoModelForCausalLM, AutoTokenizer base = "Qwen/Qwen3-4B"tok = AutoTokenizer.from_pretrained(base)mdl = AutoModelForCausalLM.from_pretrained( base, device_map="auto", torch_dtype="auto",)mdl = PeftModel.from_pretrained(mdl, "kaishuiji/vheart-affect-v9")mdl.eval()python
from feltstate import Enginefrom feltstate.sources import VheartSource eng = Engine(source=VheartSource("kaishuiji/vheart-affect-v9"))eng.observe("今晚跑通了三周的实验。")print(eng.state.mood.mixed_blend)bibtex
@software{feltstate_vheart_v9, author = {morephine}, title = {feltstate / vheart-affect-v9}, url = {https://huggingface.co/kaishuiji/vheart-affect-v9}, year = {2026}}Apache 2.0 (matches Qwen3 base).
from peft import PeftModelfrom transformers import AutoModelForCausalLM, AutoTokenizer base = "Qwen/Qwen3-4B"tok = AutoTokenizer.from_pretrained(base)mdl = AutoModelForCausalLM.from_pretrained( base, device_map="auto", torch_dtype="auto",)mdl = PeftModel.from_pretrained(mdl, "kaishuiji/vheart-affect-v9")mdl.eval()from feltstate import Enginefrom feltstate.sources import VheartSource eng = Engine(source=VheartSource("kaishuiji/vheart-affect-v9"))eng.observe("今晚跑通了三周的实验。")print(eng.state.mood.mixed_blend)@software{feltstate_vheart_v9, author = {morephine}, title = {feltstate / vheart-affect-v9}, url = {https://huggingface.co/kaishuiji/vheart-affect-v9}, year = {2026}}