metadata
tags:
- gymnastics
- coaching
- motion-analysis
- gemma3
- lora
- kim
license: apache-2.0
base_model: google/gemma-3-4b-it
KIM-Coach v3 — Gymnastics Coaching LLM
Fine-tuned Gemma 3 4B for generating motor-instruction coaching cues from motion analysis data.
What Changed in v3
| Version | Training Pairs | Key Improvement | Val Loss |
|---|---|---|---|
| v1 | 1,538 | First fine-tune, assessment-style templates | 0.140 → 0.070 |
| v2 | 1,538 | Motor instruction templates (action verbs, feel cues) | 0.110 → 0.070 |
| v3 | 3,798 | Directional error taxonomy + output diversity | 0.110 → 0.067 |
v3 Improvements
- Directional error taxonomy: 10 categories (insufficient_extension, over_flexion, timing_early/late, balance_loss, etc.) grounded in LucidAction penalties, USAG deductions, and real Habitude app data
- 2-3 output variations per input: same divergence pattern gets different coaching language, breaking template memorization
- 50 gold-standard cues as style anchors (hand-written by coaching framework)
- Novel cue generation: model composes cues it was never explicitly trained on
Evidence of Generalization (v3)
v1/v2 produced verbatim copies of training data. v3 generates novel coaching cues:
- Input: torso divergence during takeoff
- Expected: "hips level, midline braced"
- Predicted: "hips over hands, arched bridge during the takeoff — you should feel hips pushing forward"
- Both are valid motor instructions — the model learned the pattern, not the template
Pipeline
Input Format
Output Format
Training Details
- Base model: google/gemma-3-4b-it (4-bit quantized via Unsloth)
- Method: LoRA (r=16, alpha=16, dropout=0)
- Data: 3,427 train / 371 val pairs from KIM VQ-VAE codec + directional error taxonomy
- Training: 3 epochs, batch size 8, lr 2e-4, A100 GPU, ~77 minutes
- Best val loss: 0.067 at step 1200
Part of KIM (Kinematic Instruction Model)
- Codec: antking1/KIM
- Coach: antking1/KIM-coach (this model)