| # 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. |
