Total Robots
—
Deployed
—
Provisioned
—
Online (1h)
—
Draft
—
Retired
—
Recent Robots
| Robot ID | Name | Soul | Brain | Status | Actions |
|---|
| Robot ID | Name | Hardware | Soul | Brain | Status | Provisioned | Actions |
|---|
🤖 Identity
✨ Soul
🧠 Brain
⚙️ Hardware
🌐 Network
🔒 Security
When triggered, permanently destroys on-board storage. Only for classified deployments.
Deployed Robots
| Robot ID | Name | Soul | Last Seen | Firmware | Status | Actions |
|---|
📋 Activity Log — All Robots
📡 Push to Single Robot
🌐 Fleet Broadcast
All deployed + provisioned robots
⚠️ This pushes to ALL active robots. Use carefully.
📋 OTA Update History
Loading…
🧬 ATAVUS Genesis-1 R8 — Live · Port 8008 · 4.7GB Q4_K_M
ATAVUS Neural Core A1 | Runtime: atavus.cpp v1.0.0 | Fine-tuned for ATAVUS humanoid robotics
Model
ATAVUS Genesis-1 (R8)
File
/data/models/genesis-1-gguf/genesis-1-q4_k_m.gguf
Size
4.7 GB (Q4_K_M quantized)
Serve Port
8008 (atavus.cpp, OpenAI-compat)
Training Rounds
4 rounds complete ✅
Final Loss / Acc
0.0397 / 98.5%
Training Samples
215 (115 language + 100 embodiment)
Context
8,192 tokens (deployed) / 40,960 (model max)
ATAVUS Genesis-1 R6: 6 rounds of QLoRA fine-tuning (rank 128, alpha 256) on 378 samples. Val loss 0.875, accuracy 79.5%. Embodiment-aware: manipulation, navigation, sensor fusion, safety, multilingual. Runs via atavus.cpp on port 8008. ● R8 deploying (val loss 0.659, acc 84.1%)
💬 Live Chat
🏋️ Fine-Tune
📦 Create Update
📊 Dataset
📋 Job Status
🧬 Genesis-1 Live Test
Talk to Genesis-1 — casual conversation, robot commands, sensor tests. It's a real companion, not just a task executor.
Ready — type a message to test Genesis-1...
Quick tests:
Latency:
● Port 8008
1 epoch ≈ 45min CPU. 3 epochs recommended for first run.
What happens when you train:
1. Loads ATAVUS Neural Core A1 base weights
2. Applies QLoRA (rank 64, alpha 128) — precision fine-tuning
3. Fine-tunes on your dataset (215+ samples: language + embodiment)
4. Saves LoRA adapter to
Training History:
R4 (CPU, 215 samples) — 98.5% train acc — overfit, no val split
R5 (GPU, 215 samples) — val loss 2.35, acc 41% — too few samples
R6 (GPU, 378 samples) — val loss 0.875, acc 79.5% ✅ LIVE
R7 (GPU, 487 samples) — val loss 0.926, acc 75.8% — regression
R8 (GPU, 959 samples) — val loss 0.659, acc 84.1% ✅ LIVE
R9 (GPU, 1500+ samples) — 🔄 dataset generating... 5. Push adapter via OTA to all robots
2. Applies QLoRA (rank 64, alpha 128) — precision fine-tuning
3. Fine-tunes on your dataset (215+ samples: language + embodiment)
4. Saves LoRA adapter to
/data/models/genesis-1/adapter/Training History:
R4 (CPU, 215 samples) — 98.5% train acc — overfit, no val split
R5 (GPU, 215 samples) — val loss 2.35, acc 41% — too few samples
R6 (GPU, 378 samples) — val loss 0.875, acc 79.5% ✅ LIVE
R7 (GPU, 487 samples) — val loss 0.926, acc 75.8% — regression
R8 (GPU, 959 samples) — val loss 0.659, acc 84.1% ✅ LIVE
R9 (GPU, 1500+ samples) — 🔄 dataset generating... 5. Push adapter via OTA to all robots
⏱️ Est. time per round: ~9 hrs on AMD EPYC (no GPU).
R4 complete. Next run = R5 incremental fine-tune.
R4 complete. Next run = R5 incremental fine-tune.
Build an OTA update package from your current Genesis-1 model config. Deploy to individual robots or broadcast to fleet.
R4 Dataset: 215 samples (115 language + 100 embodiment) · LoRA rank 64 · loss 0.0397 · acc 98.5%
Click Refresh to check