--- 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 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.