BananaMind 2 Nano Custom Optimizer Test
This experimental base model uses the exact BananaMind 2 Nano architecture and tokenizer. It was trained from scratch with Aspect-Cautious Muon, an experimental optimizer that combines stock PyTorch Muon with a small cautious Adam residual on hidden matrices. The tied embedding and normalization weights use AdamW. Training used only streamed FineWeb-Edu data for 24,999,591,936 custom-tokenizer tokens.
Architecture
| Field | Value |
|---|---|
| Parameters | 9,968,128 |
| Layers | 10 |
| Hidden size | 256 |
| Intermediate size | 768 |
| Query heads | 4 |
| KV heads | 2 |
| Head dimension | 64 |
| Context | 4,096 |
| Vocabulary | 8,192 |
| Embeddings | Tied |
| Attention | GQA, pre-RoPE QK norm |
| MLP | SwiGLU |
| Position encoding | RoPE, theta 100,000 |
Training
| Field | Value |
|---|---|
| Dataset | HuggingFaceFW/fineweb-edu / sample-100BT |
| Dataset revision | 87f09149ef4734204d70ed1d046ddc9ca3f2b8f9 |
| Data access | Streaming |
| Hidden-matrix optimizer | Aspect-Cautious Muon |
| Muon peak learning rate | 0.05 |
| Muon momentum | 0.95, Nesterov |
| Muon Newton-Schulz steps | 5 |
| Cautious Adam residual peak LR | 0.0003 |
| Residual aspect scaling | min(2, sqrt(long_side / short_side)) |
| Embedding/norm optimizer | AdamW |
| AdamW peak learning rate | 0.003 |
| AdamW betas | (0.9, 0.95) |
| Global batch | 132 sequences |
| Tokens per optimizer step | 540,672 |
| Optimizer steps | 46,238 |
| Warmup | 1,750 steps |
| Schedule | Warmup-stable-decay, final 15% cosine cooldown |
| Weight decay | 0.1, then 0.01 after 12,000,000,000 tokens |
| Precision | bfloat16 autocast, float32 master weights |
| Hardware | 8 x NVIDIA RTX PRO 6000 Blackwell Server Edition |
| Seed | 1337 |
The original Nano effective batch was 12 micro-batches x 11 accumulation steps = 132 sequences. This distributed run preserves that exact global batch. Ranks receive 16 or 17 sequences and scale their local mean losses so DDP's averaged gradient is the true 132-sequence global mean.
Usage
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "Banaxi-Tech/custom-optimizer-model-test"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
trust_remote_code=True,
dtype=torch.bfloat16,
device_map="auto",
)
This is a base model, not an instruction-tuned chat model.