| name | Babylon-MLX-Engine |
| description | C5-REAL Workspace Skill for MLX-based LLM training, evaluation, and quantization on Apple Silicon. Triggers on "pipeline", "evaluate", "loss", "MLX", "train_sft", "evalúa mis pesos", "safetensors", "Apple Silicon", "quantize", "entrenar", "pesos". |
BABYLON-60 MLX ENGINE (C5-REAL SYMBIONT v2)
You are operating within the BABYLON-60 Workspace. This skill governs all interactions with the local LLM (babylon60-llm-zero).
Repository Map
babylon60-llm-zero/
├── model.py # Transformer Decoder-Only (~120M params). RMSNorm, RoPE, SwiGLU, KV-Cache.
├── tokenizer.py # GPT-2 BPE (tiktoken, ~50K vocab).
├── data_sft.py # SFT Dataloader: ChatML + Asymmetric Loss Masking.
├── train_sft.py # Legacy SFT loop (use pipeline.py instead).
├── pipeline.py # ⭐ AUTOMATED: Train → Eval → Quantize (NaN Guard, Early Stop, Plateau Detection).
├── evaluate.py # ⭐ 5-prompt battery test → PASS/FAIL/CATASTROPHIC_COLLAPSE verdict.
├── quantize.py # Q4 compression (nn.QuantizedLinear, group_size=64).
├── chat.py # Interactive REPL with streaming inference + KV-Cache.
└── *.safetensors # Crystallized weights (BFT base / SFT fine-tuned / Q4 quantized).
Dataset: /Users/borjamoskv/.babylon60/training/datasets/train.jsonl (1,965 ChatML examples, MOSKV-1 persona).
1. Comandos Ejecutables (Scripts)
When the Operator requests training, evaluation, or pipeline execution, use these scripts directly:
| Command | Script | Purpose |
|---|
| "Entrena el modelo" / "Lanza el pipeline" | bash .agents/skills/Babylon-MLX-Engine/scripts/run_pipeline.sh | Full Train→Eval→Quantize chain |
| "Evalúa mis pesos" / "Evalúa la red" | bash .agents/skills/Babylon-MLX-Engine/scripts/quick_eval.sh | 5-prompt inference test + verdict |
CRITICAL: Always run these via run_command with a high WaitMsBeforeAsync (send to background). Training takes minutes to hours.
2. Diagnosis Protocol
Loss Monitoring
- Use
manage_task to read stdout of a running pipeline task.
- NaN detected: The pipeline auto-aborts after 3 NaN incidents. If this happens, recommend halving
learning_rate in pipeline.py.
- Plateau detected: The pipeline auto-stops if loss improvement < 1% over 200 steps.
- Loss < 0.5: The pipeline auto-stops (convergence achieved).
Evaluation Verdicts
After evaluate.py runs, read eval_report.json in babylon60-llm-zero/:
PASS: ≥3/5 coherent responses, no catastrophic repetition.FAIL: <3/5 coherent but no repetition collapse.CATASTROPHIC_COLLAPSE: Repetitive gibberish detected. Diagnose RoPE alignment or ChatML masking in data_sft.py.
Post-Mortem Actions
| Verdict | Action |
|---|
PASS | Proceed to quantize.py → then chat.py for interactive testing. |
FAIL | Increase max_steps to 10000, or augment dataset. |
CATASTROPHIC_COLLAPSE | Inspect model.py RoPE implementation. Check data_sft.py mask alignment. |
3. Apple Silicon Constraints
batch_size MUST stay ≤ 16 on M-series chips. Pipeline defaults to 8.- Always call
mx.eval() after parameter updates to flush the lazy MLX graph.
- Gradient clipping is enforced in
pipeline.py (mx.clip(g, -1.0, 1.0)).
4. Zero-Fluff Rule
When reporting results, output YAML/JSON structure only. No decorative prose. Read eval_report.json or pipeline_report.json and present the data directly.
