Downloads · 30 days
0
bananamind-2.0.1
bananamind-research-community/bananamind-2.0.1
bananamind-2.0.1 is a machine learning model from bananamind-research-community. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
All four models use the same BananaMind 2.0-derived decoder-only language-model architecture and exactly the same physical parameters. The experiment changes only which physical decoder layers are reused during a forw…
Access
Public
Updated Aug 30, 2026
Repo size
33.2 MB
Likes
2
Public
Repo makeup
Click a slice to open those files.
.safetensors33.2 MB · 98%
At a glance
- License
- apache-2.0
- Access
- Public
- Created
- Aug 30, 2026
- Updated
- Aug 30, 2026
- SHA
- f7613d2a
Model card
From the Hugging Face model README
Architecture
All four models use the same BananaMind 2.0-derived decoder-only language-model architecture and exactly the same physical parameters. The experiment changes only which physical decoder layers are reused during a forward pass and, for Partial HQ, the training dataset.
| Property | Value |
|---|---|
| Total trainable parameters | 1,480,516 |
| Transformer core parameters, including token embedding | 980,547 (66.23%) |
| Three physical decoder blocks alone | 718,275 (48.52%) |
| Parameters per physical decoder block | 239,425 |
| Causal trigram embedding module | 499,969 (33.77%) |
| Vocabulary | 2,048 tokens |
| Tokenizer | BananaMind 2 Micro |
| Hidden width | 128 |
| MLP intermediate width | 384 |
| Physical decoder layers | 3 |
| Query heads | 4 |
| Key/value heads | 1 |
| Attention head dimension | 32 |
| Context length | 3,072 tokens |
| RoPE theta | 100,000 |
| N-gram hash buckets | 3,906 |
| Refresh convolution | Kernel 9, causal depthwise |
Models
| Short name | Architecture execution | Training dataset |
|---|---|---|
| Partial HQ | L1 -> L2 -> L2 -> L3 | epfml/FineWeb-HQ |
| No-loop Edu | L1 -> L2 -> L3 | HuggingFaceFW/fineweb-edu |
| Partial Edu | L1 -> L2 -> L2 -> L3 | HuggingFaceFW/fineweb-edu |
| All-looped Edu | L1 -> L2 -> L3 -> L1 -> L2 -> L3 | HuggingFaceFW/fineweb-edu |
Primary comparison
The public multiple-choice values below are acc_norm. ARC mean is the mean of
ARC-Easy and ARC-Challenge before chance normalization.
| Metric | Partial HQ | No-loop Edu | Partial Edu | All-looped Edu |
|---|---|---|---|---|
| Base Bench 1.1 overall Elo | 889 | 884 | 885 | 875 |
| Base Bench raw accuracy | 35.43% | 33.71% | 34.57% | 33.14% |
| Base Bench weighted accuracy | 34.14% | 33.57% | 33.65% | 32.54% |
ARC-Easy acc_norm | 29.50% | 30.35% | 29.50% | 30.98% |
ARC-Challenge acc_norm | 22.18% | 22.10% | 22.53% | 21.33% |
ARC mean acc_norm | 25.84% | 26.22% | 26.01% | 26.15% |
PIQA acc_norm | 53.65% | 52.56% | 54.30% | 52.45% |
HellaSwag acc_norm | 27.04% | 26.93% | 27.04% | 27.28% |
ArithMark-3 acc_norm | 28.10% | 30.80% | 33.00% | 30.40% |
| Intelligence Index | 3.79 | 3.93 | 5.37 | 3.88 |
Raw public-benchmark accuracy
| Metric | Partial HQ | No-loop Edu | Partial Edu | All-looped Edu |
|---|---|---|---|---|
ARC-Easy acc | 28.62% | 30.01% | 29.00% | 29.92% |
ARC-Challenge acc | 18.86% | 17.75% | 17.49% | 16.89% |
PIQA acc | 53.75% | 54.08% | 55.50% | 54.46% |
HellaSwag acc | 26.89% | 26.53% | 26.80% | 26.94% |
ArithMark-3 acc | 27.90% | 30.30% | 33.00% | 29.80% |
Chance-normalized components
For a benchmark score s and random-chance score c:
N(s, c) = 100 * (s - c) / (100 - c)
The Intelligence Index is:
(HellaSwag + ARC + PIQA + 0.65 * ArithMark-3) / 3.65
| Normalized component | Partial HQ | No-loop Edu | Partial Edu | All-looped Edu |
|---|---|---|---|---|
| HellaSwag, chance 25% | 2.72 | 2.57 | 2.72 | 3.03 |
| ARC mean, chance 25% | 1.13 | 1.63 | 1.35 | 1.54 |
| PIQA, chance 50% | 7.29 | 5.11 | 8.60 | 4.90 |
| ArithMark-3, chance 25% | 4.13 | 7.73 | 10.67 | 7.20 |
| Intelligence Index | 3.79 | 3.93 | 5.37 | 3.88 |
Base Bench 1.1 category Elo
| Category | Partial HQ | No-loop Edu | Partial Edu | All-looped Edu |
|---|---|---|---|---|
| Language completion | 827 | 850 | 864 | 817 |
| Commonsense | 921 | 843 | 831 | 877 |
| World knowledge | 842 | 852 | 892 | 820 |
| Context tracking | 846 | 862 | 879 | 862 |
| Quantitative | 852 | 815 | 806 | 815 |
| Logical reasoning | 990 | 1010 | 965 | 975 |
| Code completion | 957 | 964 | 970 | 972 |
Base Bench 1.1 category raw accuracy
Each category contains 50 examples.
| Category | Partial HQ | No-loop Edu | Partial Edu | All-looped Edu |
|---|---|---|---|---|
| Language completion | 40% (20/50) | 44% (22/50) | 46% (23/50) | 38% (19/50) |
| Commonsense | 46% (23/50) | 34% (17/50) | 34% (17/50) | 40% (20/50) |
| World knowledge | 36% (18/50) | 34% (17/50) | 40% (20/50) | 32% (16/50) |
| Context tracking | 32% (16/50) | 34% (17/50) | 36% (18/50) | 34% (17/50) |
| Quantitative | 28% (14/50) | 22% (11/50) | 22% (11/50) | 22% (11/50) |
| Logical reasoning | 36% (18/50) | 38% (19/50) | 32% (16/50) | 34% (17/50) |
| Code completion | 30% (15/50) | 30% (15/50) | 32% (16/50) | 32% (16/50) |
Base Bench 1.1 category weighted accuracy
| Category | Partial HQ | No-loop Edu | Partial Edu | All-looped Edu |
|---|---|---|---|---|
| Language completion | 39.49% | 42.68% | 44.59% | 38.22% |
| Commonsense | 45.43% | 35.02% | 33.44% | 39.43% |
| World knowledge | 34.80% | 36.05% | 41.38% | 31.97% |
| Context tracking | 29.94% | 31.85% | 34.08% | 31.85% |
| Quantitative | 24.92% | 21.14% | 20.19% | 21.14% |
| Logical reasoning | 35.42% | 37.93% | 32.29% | 33.54% |
| Code completion | 31.85% | 32.80% | 33.44% | 33.76% |
- License
- apache-2.0
- Created
- Aug 30, 2026
- Updated
- Aug 30, 2026