BananaMind 2 Pico Test - 30%
This is the 30% checkpoint of a 900,002-parameter
base causal language model. It is not instruction tuned.
Architecture
| Field |
Value |
| Parameters |
900,002 |
| Layers / hidden size |
6 / 96 |
| SwiGLU intermediate size |
380 |
| Query / KV heads |
6 / 2 |
| Head dimension |
16 |
| Context |
4,096 |
| Vocabulary |
384, tied |
| Refresh layers |
4 and 6 |
| Refresh kernel |
Causal depthwise, width 9 |
The selective XSA refresh gate reads detached attention output as its signal,
reinjects the original input embedding as its value, and carries convolution
history alongside the K/V cache. Its learned residual scalar starts at zero.
Training
| Field |
Value |
| Progress |
30% |
| Tokens seen |
60,001,615,872 |
| Target tokens |
200,000,000,000 |
| Hardware |
4 x NVIDIA H200 |
| Matrix optimizer |
Stock torch.optim.Muon |
| Muon peak LR |
0.07 |
| Embedding/control optimizer |
AdamW, LR 0.004 |
| Precision |
bfloat16 autocast |
| Token range |
FineWeb-HQ |
Cosmopedia v2 |
| 0.00B-100.00B |
80% |
20% |
| 100.00B-200.00B |
60% |
40% |
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "Banaxi-Tech/pico-test"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True)
