README.md

---

license: apache-2.0
tags:
  - sentinelbrain
  - moe
  - rocm
  - mi300x
  - pytorch
  - checkpoint
  - realignment
library_name: pytorch
pipeline_tag: text-generation
---


# SentinelBrain 14B MoE v0.1 - Frankenstein Realignment v2

This repository now includes SentinelBrain Frankenstein realignment v2 artifacts from the AMD MI300X run completed on 2026-05-03.

## v2 Training Update

- Architecture: custom SentinelBrain sparse MoE decoder, approximately 14.4B stored parameters, 4 experts, top-2 routing, 24 layers, d_model 4096, seq_len 4096.
- Hardware: AMD Instinct MI300X via ROCm/HIP.
- Run: Frankenstein realignment v2 from raw merged checkpoint.
- Completed steps: 5,000.
- Total training tokens during realignment: approximately 0.98B.
- Best validation loss observed: 5.1359.
- Final checkpoint: `checkpoints/frankenstein_v2_final.pt`.
- Best checkpoint: `checkpoints/frankenstein_v2_best.pt`.
- EMA best checkpoint: `checkpoints/frankenstein_v2_ema_best.pt`.
- Previous Hugging Face version preserved on branch: `previous-before-v2-realign-5000-20260503-103121`.

## Included Files

- `checkpoints/frankenstein_v2_final.pt`: full final checkpoint at step 5000, including optimizer/progress state.
- `checkpoints/frankenstein_v2_best.pt`: best model-only checkpoint by validation loss.
- `checkpoints/frankenstein_v2_ema_best.pt`: EMA best checkpoint from the v2 run.
- `checkpoints/sentinelbrain_pretrain_step2471_hf.pt`: pretrain anchor used for comparison.
- `logs/realign_v2.log`: full realignment console log.
- `logs/realign_v2_metrics.jsonl`: step metrics emitted during training.
- `reports/train_metrics_final.json`: final dashboard training metrics snapshot.
- `reports/conductor_state_final.json`: final dashboard/conductor state.
- `reports/sft_combined_ready_report.*`: cleaned SFT dataset preflight report.
- `reports/sentinelbrain_quality_stub_full_fixed.json`: MI300X executable-code benchmark report.

A full progress archive containing all v2 milestones and optimizer-bearing checkpoints is backed up off-Hub on the Azure VM at `/home/msrusu/sentinelbrain_backups/v2_realign_5000/sentinelbrain_v2_realign_full_20260503.tar.zst`. A SHA256 sidecar is generated at archive completion.

## Current Evaluation Notes

MI300X executable-code tests show that v2 realignment is not yet ready as a coding assistant checkpoint:

| Checkpoint | Pass@1 | Syntax Rate | Notes |
| --- | ---: | ---: | --- |
| `frankenstein_v2_best.pt` | 0.0% | 62.5% | Failed all 8 HumanEval-style stub tasks. |
| `frankenstein_v2_final.pt` | 0.0% | 75.0% | Failed all 8 HumanEval-style stub tasks. |
| `sentinelbrain_pretrain_step2471_hf.pt` | 0.0% | 87.5% | Failed all 8 tasks but produced the most syntactically valid Python. |

Interpretation: v2 successfully completed corpus realignment and preserved all progress artifacts, but it needs a focused next phase of executable code SFT, function-call/chat formatting, and auto-critic rejection sampling before quality claims should be made.

## Dataset Preparation Status

The next SFT combined dataset was cleaned non-destructively on the MI300X server:

- Input rows: 42,138.
- Kept rows: 32,996 (78.3%).
- Removed rows: 9,142.
- Max estimated tokens: 3,072.
- Main removals: short assistant/user outputs, garbage responses, repetitive responses, and over-length samples.

## Loading

These are custom SentinelBrain PyTorch checkpoints, not standard Hugging Face `AutoModelForCausalLM` weights. Load with the SentinelBrain code from `/workspace/sentinelprime` or the matching source package.

```python

import torch

from config import ModelConfig

from model.sentinel import SentinelBrain



ckpt = torch.load("checkpoints/frankenstein_v2_best.pt", map_location="cpu", weights_only=False)

model = SentinelBrain(ModelConfig())

state = ckpt.get("model_state_dict") or ckpt.get("model") or ckpt

model.load_state_dict(state, strict=False)

model.eval()

```

## Next Phase Direction

The recommended next phase is a controlled SFT/auto-critic cycle: train from the pretrain anchor plus selected v2 weights only after passing format probes, prioritize executable Python/TypeScript/code-repair datasets, reject non-compiling generations, and benchmark every 250-500 steps before continuing.

<!-- sentinel-vision-navigator-progress-2026-05-07 -->

## Sentinel Vision Navigator Progress Log

**Date:** 2026-05-07  
**Hackathon:** AMD Developer Hackathon  
**Tracks:** Vision & Multimodal AI, AI Agents & Agentic Workflows, Fine-Tuning on AMD GPUs, Hugging Face Space

Sentinel Vision Navigator is now connected to this model page as the applied assistive-AI product layer for the SentinelBrain roadmap. The public prototype focuses on blind and partially sighted users who need immediate, camera-first understanding of their surroundings.

### Current build

- Public web demo: https://amdvision.qubitpage.com/
- Hugging Face Space: https://huggingface.co/spaces/lablab-ai-amd-developer-hackathon/sentinel-vision-navigator
- Whitepaper PDF: https://amdvision.qubitpage.com/downloads/sentinel-vision-whitepaper.pdf
- Pitch deck PDF: https://amdvision.qubitpage.com/downloads/sentinel-vision-pitch-deck.pdf
- Android APK distribution path: https://amdvision.qubitpage.com/downloads/sentinel-vision.apk

### Implemented application features

- Live camera scene analysis for blind mobility prompts.
- Camera-first local navigation behavior for commands such as `guide me`, `get out`, `avoid obstacles`, and `which way`.
- Embedded vision-direction RAG knowledge for indoor paths, outdoor sidewalks, stairs, curbs, text reading, and object finding.
- Fluent English speech output with calmer, shorter instructions.
- Android first-run setup for camera, mic/speech, contacts, location, email handoff, WhatsApp handoff, SMS, and calls.
- Multitasking agent routing for navigation, calls, SMS, WhatsApp, email, maps, app opening, battery, time, text reading, object finding, and conversation.
- 3D Terminator-style camera HUD with reticle, scan line, perspective grid, connector status, and detected-object chips.

### Model status note

The production demo currently uses Akash-hosted Qwen multimodal inference for visual reasoning while SentinelBrain-14B-MoE-v0.1 remains the project model artifact and training/progress page for the specialized assistant roadmap. The next step is to collect interaction traces and evaluate a fine-tuned SentinelBrain navigation/router layer on AMD GPU infrastructure.