BananaMind-2-MoE

Name: BananaMind-2-MoE
Availability: InStock

BananaMind-2-MoE is a sparse decoder-only causal language model trained from scratch by BananaMind on a 30B-token curriculum.

The model has 25,086,592 total parameters, approximately 1,985,152 active parameters per token, a 4,096-token context window, and a custom 8k-token digit-aware byte-level BPE tokenizer.

Model Details

Field	Value
Total parameters	25,086,592
Active parameters per token	1,985,152
Architecture	BananaMind2MoE decoder-only Transformer
Routing	Sparse top-1, no capacity drops
Experts per layer	48
Active experts per token	1
Layers	8
Hidden size	128
Expert intermediate size	160
Attention heads	4
KV heads	2
Head dim	32
Attention style	Grouped-query attention with QK norm
Expert MLP	SwiGLU
Position embeddings	RoPE
RoPE theta	100,000
Normalization	RMSNorm
Vocabulary size	8,192
Context length	4,096
Embeddings	Tied input/output embeddings
Weight format	safetensors
HF architecture	`BananaMind2MoEForCausalLM`
HF model type	`bananamind2_moe`
Final checkpoint	`runs/bananamind2-moe/final.pt`
Final training step	55,485
Tokens seen	29,999,726,592

Active parameters count the tied embedding/output matrix, every attention and router parameter, normalization parameters, and one selected expert per layer.

Tokenizer

BananaMind-2-MoE uses the same custom 8k byte-level BPE tokenizer as BananaMind-2-Mini. Digits are kept as separate tokens so numbers do not collapse into large number tokens.

Special token	ID
`<	pad
`<	bos
`<	eos
`<	unk

Training Data

Dataset	Target Tokens	Share
FineWeb-Edu	16.5B	55%
DCLM	9.0B	30%
Cosmopedia-v2	3.0B	10%
FineMath-4+	1.5B	5%
Total	30.0B	100%

The run used a progressive curriculum, beginning web-heavy and gradually increasing synthetic textbook and mathematics data.

Training Setup

Field	Value
Sequence length	4,096
Micro batch	12
Gradient accumulation	11
Effective batch	132 sequences
Tokens per optimizer step	540,672
Final optimizer step	55,485
Optimizer	AdamW
Betas	0.9, 0.95
Peak learning rate	2.3e-3
Warmup steps	1,750
LR schedule	Warmup-stable-decay with cosine decay
Weight decay	0.1, then 0.01 after 12B tokens
Router balance loss	0.01
Router z-loss	1e-3
Gradient clipping	1.0
Compile	PyTorch compile enabled
Seed	1337

Evaluation

These are self-evaluated scores produced with lm_eval. Scores may vary slightly depending on the evaluation harness version, runtime settings, dtype, and environment.

An independent Open SLM Leaderboard evaluation is coming soon.

Benchmark	Score	Metric
Average	34.90	`mean`
ARC Easy	34.64	`acc_norm,none`
PIQA	56.37	`acc_norm,none`
ARC Challenge	21.16	`acc_norm,none`
HellaSwag	27.45	`acc_norm,none`

The unrounded average is 0.349047. Unrounded task results are ARC Easy 0.346380, PIQA 0.563656, ARC Challenge 0.211604, and HellaSwag 0.274547.

Usage

This model uses custom architecture code, so load it with trust_remote_code=True.

pip install -U transformers safetensors torch

python

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "BananaMind/BananaMind-2-MoE"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)

device = "cuda" if torch.cuda.is_available() else "cpu"
dtype = (
    torch.bfloat16
    if torch.cuda.is_available() and torch.cuda.is_bf16_supported()
    else torch.float32
)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    trust_remote_code=True,
    torch_dtype=dtype,
).to(device).eval()

prompt = "The color of the sky is"
inputs = tokenizer(prompt, return_tensors="pt").to(device)

with torch.no_grad():
    output = model.generate(
        **inputs,
        max_new_tokens=96,
        do_sample=True,
        temperature=0.7,
        top_p=0.9,
        repetition_penalty=1.1,
        pad_token_id=tokenizer.eos_token_id,
        eos_token_id=tokenizer.eos_token_id,
    )

print(tokenizer.decode(output[0], skip_special_tokens=True))

Notes

This is a base model, not a chat-tuned model.
Routing is top-1 and deterministic in evaluation mode.
Autoregressive generation supports the Transformers DynamicCache KV cache.
Current benchmark scores are self-evaluated; independent leaderboard evaluation is pending.

License

Apache 2.0