# Ankahi — Compute Log

## Hardware Used
*   **Provider:** Kaggle / Google DeepMind H100 allocation
*   **GPU:** 1x NVIDIA H100 80GB HBM3 (MIG 3g.40gb partition)
*   **VRAM Available:** ~40 GB
*   **System RAM:** 128 GB
*   **OS:** Ubuntu 22.04 LTS

## Environment
*   **Python:** 3.10.12
*   **PyTorch:** 2.5.1+cu124
*   **CUDA Toolkit:** 12.4
*   **Core Libraries:** Unsloth (2026.4.6), Transformers (5.5.0), PEFT, bitsandbytes

## Training Timeline & Resource Usage

| Stage | Goal | Time Elapsed | Peak VRAM |
| :--- | :--- | :--- | :--- |
| **0. Setup & Sanity** | Verify pipeline | 0.5h | 11.4 GB |
| **1. Base SFT** | Train multimodal adapter (Rank 16) on 16,500 pairs | 12.0h | 11.8 GB |
| **2. Persona LoRA** | Train 5 separate adapters (Rank 8) on 3,000 pairs each | 10.0h (2h/persona) | 11.6 GB |
| **3. Audio Adapter** | Train dysarthric audio disambiguation | 10.0h | 11.4 GB |
| **4. Safety Tuning** | Train refusal behavior | 4.0h | 11.4 GB |
| **5. Merge & Deploy** | Merge weights | 4.0h | 11.0 GB |
| **Total** | **Full Pipeline Execution** | **~40.5h** | **11.8 GB max** |

## Notes on Efficiency
Thanks to **Unsloth** and **Triton kernels**, the training of Gemma 4 E4B was executed within the VRAM constraints of an H100 MIG partition without triggering OOM (Out-of-Memory) errors, provided aggressive garbage collection (`gc.collect()`) was enforced between persona training loops.