AndrewThompson1233/maba-1.5-103m

Maba v1.5 (103.5M)

Research Proof-of-Concept - Not for General / Production Use This checkpoint is an empirical demonstration and verification artifact. It proves that the AndrewThompson1233/maba-v1.5-exp-architecture architecture is fully functional, trainable from scratch, and numerically stable on consumer/enterprise hardware (NVIDIA L4).

• Base Architecture: AndrewThompson1233/maba-v1.5-exp-architecture
• Parameters: 103,520,911 (103.5M)
• Core Computation Ratio: 95.21% (4.30% Vocab Tax)
• Macro-Stack: 3:1 (15 DGDA Recurrence : 5 MABA-SA Dynamic Sparse Attention)
• Positional Encoding: Strict NoPE (0 parameters)
• Training Corpus: 3,044 dialogue pairs on NVIDIA L4 (bfloat16)

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Empirical Benchmark vs Qwen3.8-Flash-Next (101.7M)

Evaluated under identical training budgets (3,044 dialogues, 15 epochs, bfloat16, NVIDIA L4):

Metric	Maba v1.5-exp	Qwen3.8-Flash-Next	Delta / Advantage
Parameters	103,520,911 (103.5M)	101,701,120 (101.7M)	0.2% parity
Architecture	75% DGDA + 25% MABA-SA	75% GDN + 25% QSA + MoE	Cyclic 3:1
Positional Encoding	Strict NoPE (0 params)	25% Partial RoPE	Zero positional overhead
Contrastive Retrieval (MCQ)	87.5% (7/8)	75.0% (6/8)	+12.5% accuracy
Validation Loss	0.0697	0.0778	-10.4% entropy
Validation Perplexity (PPL)	1.07	1.08	Maba wins
Decode Throughput (L4)	7.0 tok/s	5.5 tok/s	+27.3% faster generation

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Attention Ablation Proof

Empirical demonstration of the contribution of the 25% MABA-SA dynamic sparse attention layers against a pure linear recurrent baseline on the exact same checkpoint weights:

Model Variant	Attention Mechanism	Validation Loss	Perplexity (PPL)	Error Reduction
Pure DGDA (Ablation)	None (100% Linear Recurrence)	3.9360	51.21	Baseline
Qwen3.8-Flash-Next	QSA (GQA + Micro-block Indexer)	3.8772	48.29	-5.7% vs Recurrence
Maba v1.5 Full	MABA-SA (MLA + Top-32 + HCA)	3.5903	36.24	-29.2% error drop

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Needle-In-A-Haystack & Centroid Retrieval (512 to 4096 Tokens)

Context Length	Needle Position	Needle Block	DG-Indexer (Hybrid Mean+Max)	Standard Pure Mean Pooling
512 tokens	51 (10%)	Block #0	Retrieved (Top-32)	Retrieved
512 tokens	256 (50%)	Block #4	Retrieved (Top-32)	Retrieved
512 tokens	460 (90%)	Block #7	Retrieved (Top-32)	Retrieved
1024 tokens	102 (10%)	Block #1	Retrieved (Top-32)	Retrieved
1024 tokens	512 (50%)	Block #8	Retrieved (Top-32)	Retrieved
1024 tokens	921 (90%)	Block #14	Retrieved (Top-32)	Retrieved
2048 tokens	204 (10%)	Block #3	Retrieved (Top-32)	Retrieved
2048 tokens	1024 (50%)	Block #16	Retrieved (Top-32)	Retrieved
2048 tokens	1843 (90%)	Block #28	Retrieved (Top-32)	Retrieved
4096 tokens	2048 (50% Lost-in-Middle)	Block #32	Retrieved (Top-32)	Diluted to 0.0 (Failed)
4096 tokens	3686 (90%)	Block #57	Retrieved (Top-32)	Retrieved

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KV-Cache Footprint at 4k Context

Context Length	Dense Attention (Baseline)	Qwen3.8-Flash-Next	Maba v1.5 (MLA + Top-32)	Memory Reduction vs Dense
512 tokens	25.00 MB	1.00 MB	0.62 MB	-97.5%
1,024 tokens	50.00 MB	2.00 MB	1.25 MB	-97.5%
2,048 tokens	100.00 MB	4.00 MB	2.50 MB	-97.5%
4,096 tokens	200.00 MB	8.00 MB	2.50 MB	-98.8%

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How to Use

Load the base architecture from AndrewThompson1233/maba-v1.5-exp-architecture and load the weights:

import sys
import subprocess
import torch
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("gpt2")

if "maba-v1.5-exp-architecture" not in sys.path:
    subprocess.run(["git", "clone", "https://huggingface.co/AndrewThompson1233/maba-v1.5-exp-architecture"], check=False)
    sys.path.insert(0, "maba-v1.5-exp-architecture")

from maba_sparse.config import MabaSparseConfig
from maba_sparse.model import MabaSparseForCausalLM

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
cfg = MabaSparseConfig(vocab_size=len(tokenizer), dim=640, d_emb=128, intermediate_size=1248, n_layers=20)
model = MabaSparseForCausalLM(cfg).to(device)

weights_path = hf_hub_download(repo_id="AndrewThompson1233/maba-1.5-103m", filename="model.safetensors")
weights = load_file(weights_path)
model.load_state_dict(weights)
model.eval()

prompt = "User: tell me a joke\nAssistant: "
input_ids = tokenizer.encode(prompt, return_tensors="pt").to(device)

with torch.no_grad():
    with torch.amp.autocast(device_type="cuda", dtype=torch.bfloat16):
        output_ids = model.generate(input_ids, max_new_tokens=35, temperature=0.0)

print(tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True).strip())