hikewa/eisv-lumen-teacher

EISV-Lumen Teacher — LoRA on Qwen3-4B

A LoRA fine-tune of Qwen/Qwen3-4B that maps EISV trajectory shapes to primitive expression tokens. Given a sequence of EISV dimension values and a classified trajectory shape, the model generates a structured expression — a short sequence of emotion-like tokens that a downstream embodied system (Lumen, running on a Raspberry Pi 4) uses to drive LED color, screen drawing, and inner-voice narration.

This is the teacher model in a teacher-student pipeline. Its outputs are distilled into a lightweight RandomForest student that runs on-device.

Model Details

What is EISV?

EISV is a four-dimensional state space computed from real sensor readings on a Raspberry Pi 4 (BrainCraft HAT with BME280, VEML7700, CPU telemetry):

These are not simulated or synthetic dimensions — they are derived from physical sensors and computational telemetry on a real device, sampled every 30 seconds.

Trajectory Shapes

A trajectory is a time-window of EISV values. The shape classifier identifies 9 distinct patterns:

The distribution is heavily skewed toward settled_presence and convergence because a well-functioning Pi is usually stable. The training set uses 50/50 real+synthetic blending to ensure the model sees enough rare shapes.

Expression Vocabulary

The model generates structured expressions using 15 primitive tokens and 5 patterns.

Tokens (15)

~warmth~ ~curiosity~ ~resonance~ ~stillness~ ~boundary~ ~reaching~ ~reflection~ ~ripple~ ~deep_listening~ ~emergence~ ~questioning~ ~holding~ ~releasing~ ~threshold~ ~return~

Patterns (5)

Evaluation Results

Evaluated on 500 real trajectories (no synthetic data in the test set):

Coherence measures how well the generated expression fits the trajectory shape (semantic alignment between tokens and shape meaning). Valid rate measures whether outputs parse as legal expressions. Pattern accuracy measures exact match of the structural pattern — the relatively low score is expected since multiple patterns can be coherent for a given shape.

Per-Shape Coherence

Usage

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load base model
base_model = AutoModelForCausalLM.from_pretrained(
    "Qwen/Qwen3-4B",
    torch_dtype="auto",
    device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-4B")

# Apply LoRA adapter
model = PeftModel.from_pretrained(base_model, "hikewa/eisv-lumen-teacher")

# Format input prompt
prompt = """You are an EISV expression generator. Given trajectory dynamics, produce a primitive expression.

Trajectory shape: settled_presence
EISV values: E=0.72, I=0.85, S=0.15, V=0.08
Dominant dimension: I (Information Integrity)
Trend: stable

Expression:"""

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(
    **inputs,
    max_new_tokens=32,
    temperature=0.7,
    do_sample=True,
)
result = tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)
print(result)
# Example output: ~stillness~ ~resonance~

Related

• —Dataset: hikewa/unitares-eisv-trajectories — 2,880 trajectory-expression pairs used for training
• —Student model: hikewa/eisv-lumen-student — distilled RandomForest that runs on Raspberry Pi 4
• —Demo: hikewa/eisv-lumen-explorer — interactive Space to explore trajectories and expressions

Citation

@misc{eisv-lumen-teacher-2026,
  title={EISV-Lumen Teacher: LoRA Fine-Tune for Trajectory-to-Expression Generation},
  author={hikewa},
  year={2026},
  url={https://huggingface.co/hikewa/eisv-lumen-teacher},
  note={LoRA adapter on Qwen/Qwen3-4B mapping EISV trajectory shapes to primitive expression tokens}
}