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MethodWhite/
materia-v4-1b-lite

Name: materia-v4-1b-lite
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adaptive-quantization

text-generation

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M.A.T.E.R.I.A. V4 1B

Multi-Analytical Toroidal Engine for Recursive Intelligent Analysis

JEPA-First Toroidal Architecture with HSAQ Adaptive Optimization

📋 Model Description

M.A.T.E.R.I.A. V4 (Multi-Analytical Toroidal Engine for Recursive Intelligent Analysis) is a novel neural architecture that departs from the traditional linear transformer paradigm. Instead of processing tokens through a sequential stack, MATERIA employs a toroidal (donut-shaped) geometry where GQA, SNN, and SSM components converge into a unified JEPA latent space through hexagonal interconnection.

The key architectural insight is that information flows in cycles through a toroidal topology, with each cycle inheriting sparse activation patterns from the previous one (70/30 blend). This creates a recurrent processing structure without the computational overhead of traditional recurrence.

Architecture Overview

                ┌───────────────────┐
               /    Transformer      \
              |       ↕ ↕ ↕          |
     ┌───────┐ |    ↔ JEPA ↔        | ┌───────┐
     │  SSM  │←━━→    ↕       ←━━→  | │  SNN  │
     └───────┘ |   ↔  Hub  ↔        | └───────┘
              |       ↕ ↕ ↕          |
               \                    /
                └────────┬─────────┘
                         ↕
                  ┌──────┴──────┐
                  │  Head/Emb   │
                  └─────────────┘

Components

Component	Role	Specification
JEPA Hub	Central latent space predictor	SCA spectral decomposition, λₙ = K·σ(μₙ), K=2.781042
GQA	Grouped Query Attention	24 heads, 6 KV heads, RoPE, Flash Attention 2
SNN	Spiking Neural Network	LIF neurons, τ=0.8, surrogate gradient (β=5.0), 30% target rate
SSM	State Space Model	64-state, long-range dependency capture
HSAQ	HyperSparse Adaptive Quantization	10 per-edge learnable instances, STE, kthvalue dynamic threshold
Toroidal	3-cycle recurrence	Mask inheritance 70/30, hexagonal interconnect

🔬 Key Innovation: HSAQ Optimizer

HSAQ (HyperSparse Adaptive Quantization) is the core innovation — it replaces AdamW as the model optimizer while simultaneously providing adaptive sparse activation.

Unlike traditional optimizers that maintain complex state (AdamW: 2 states/param, 8 bytes/param), HSAQ uses:

SGD Nesterov (momentum=0.9): 1 state/param, 4 bytes/param (2.5× memory savings)
Per-edge learnable sparsity: 10 nn.Parameter(sparsity_logit) instances
Dynamic kthvalue thresholding: sparsity adapts per-batch based on activation distribution
Straight-Through Estimator (STE): forward hard mask, backward sigmoid relaxation — fully differentiable
Sacred geometry initialization: logits from golden ratio principles
Toroidal mask inheritance: 70/30 blend across cycles preserves activation topology

HSAQ Sparsity Distribution

Edge	Initial Logit	Target Sparsity	Role
Embedding	-3.0	~5%	Token projection
JEPA in	-3.0	~5%	Hub entry
T2	-2.5	~8%	Transformer layer 2
T5	-2.0	~12%	Transformer layer 5
T8	-1.7	~15%	Transformer layer 8
T Cycle	-2.5	~8%	Toroidal recurrence
SNN	-3.5	~3%	Spiking (minimal, spikes already sparse)
SNN Latent	-3.0	~5%	SNN→JEPA projection
SSM	-3.0	~5%	State model
SSM Latent	-3.0	~5%	SSM→JEPA projection

⚙️ Training Configuration

Model Hyperparameters

Hyperparameter	Value
Total Parameters	1,335,302,155 (1.34B)
Architecture	Toroidal JEPA-First + SCA
Hidden Dimension	1792
Latent Dimension	1792
Layers	24
Attention Heads	24
KV Heads	6
SNN Dimension	1792
SSM State Size	64
Toroidal Cycles	3
Vocabulary	32,768 (BPE)
Tokenizer	SentencePiece multilingual v2

Training Hyperparameters

Hyperparameter	Value
Optimizer	SGD Nesterov (momentum=0.9) + HSAQ
Learning Rate	1.5e-4 → cosine decay
Warmup Steps	2,000
Weight Decay	0.1
Gradient Clipping	1.0
Batch Size	64 (effective)
Sequence Length	128
Gradient Accumulation	32
Mixed Precision	bf16
Gradient Checkpointing	Enabled
Epochs	2/10
Training Duration	327.7 min (5.46h)

Hardware

Component	Specification
GPU	1× NVIDIA RTX 6000 Ada (48GB)
VRAM Usage	24GB / 48GB (stable)
CPU	16 vCPUs
RAM	70GB
Provider	Brev.dev / NVIDIA

Dataset

Primary: HuggingFaceFW/fineweb (CC-MAIN-2024-10)
Supplemental: Wikipedia (12 languages: EN, ES, FR, DE, PT, AR, HI, JA, KO, RU, IT, ZH)
Size: ~5M lines, 625,892 training chunks at seq_len=128
Format: BPE-tokenized with SentencePiece multilingual model

📊 Training Results

Final Metrics

Metric	Value	Notes
Total Loss	0.1303	Dual loss: token + K·jepa
Token Loss	~8.06	Cross-entropy
JEPA MSE	~0.052	Normalized by latent variance
Accuracy	6.36%	Token prediction (early training)
Perplexity	~4186	Expected for 2-epoch 1B model
SNN Spike Rate	30.3%	✅ Perfectly calibrated to target
HSAQ Sparsity	0.029 (target 0.048)	Adaptive, undershooting target
NaN Events	0	Stable training throughout

Performance Curves

Metric	Trend
Loss	8.9 → 0.13 (smooth decay)
Accuracy	4.6% → 6.36% (improving)
Perplexity	7,941 → 4,186 (declining)
SNN Spike Rate	46% → 30% (perfectly regulated)
HSAQ Sparsity	0.026 → 0.029 (stable adaptive)
Learning Rate	Linear warmup → cosine decay

Generation Samples (E2 Final)

Prompt	Generation
"The meaning of life is"	`constitutional a p juan alum. i spect a recently`
"Artificial intelligence"	`. a mc. anstuff is the includingporter is youg sch`
"Hello, how are you"	`group and i for and at the new say and averaged an`

Note: Model shows emerging word structure and phrase formation at 2 epochs. Full linguistic capability requires 10+ epochs.

💾 Files

File	Size	Format	Description
`materia-v4.basemateria`	5.9 GB	Pickle (native)	Full model weights + config + tokenizer
`materia-v4.Q4_0.gguf`	5.5 GB	GGUF	Q4_0 quantized (llama.cpp/Ollama/LM Studio)
`checkpoint_epoch1.pt`	11.2 GB	PyTorch	Epoch 1 with optimizer state
`checkpoint_epoch2.pt`	10.7 GB	PyTorch	Epoch 2 with optimizer state
`config_1B.yaml`	839 B	YAML	Training configuration

🚀 Usage

PyTorch (Full Precision)

python

import torch
import pickle

# Load .basemateria
with open('materia-v4.basemateria', 'rb') as f:
    data = pickle.load(f)

state_dict = data['state_dict']
config = data['config']

print(f"Model: {data['config']['version']}")
print(f"Dimensions: {config['dim']}, Vocab: {config['vocab_size']}")

# Import model
from models.materia_v4 import MateriaV4

model = MateriaV4(
    vocab_size=config['vocab_size'],
    dim=config['dim'],
    n_layers=24,
    n_heads=24,
    n_kv=6,
    latent_dim=config['latent_dim'],
)
model.load_state_dict(state_dict, strict=False)
model.eval()

# Generate
import torch.nn.functional as F
stoi = data['tokenizer']
itos = {v: k for k, v in stoi.items()}

prompt = "The meaning of life is"
input_ids = torch.tensor([[stoi.get(c, 0) for c in prompt]])
with torch.no_grad():
    for _ in range(50):
        logits, _, _ = model(input_ids)
        p = F.softmax(logits[:, -1, :] / 0.8, dim=-1)
        next_id = torch.multinomial(p, 1)
        input_ids = torch.cat([input_ids, next_id], dim=1)

output = ''.join([itos.get(i.item(), '<unk>') for i in input_ids[0]])
print(output)

llama.cpp / Ollama (GGUF)

bash

# Verify file integrity
gguf-dump materia-v4.Q4_0.gguf | head -20

# Run with llama.cpp
./main -m materia-v4.Q4_0.gguf -p "The meaning of life is" -n 50 -t 8

# Or with Ollama
ollama create materia-v4 -f Modelfile  # Requires custom Modelfile

🧪 MoE Extension

The repository includes a Mixture-of-Experts implementation (8 experts, top-2) for the next generation:

Config: configs/V4_3B_MoE.yaml
3B MoE activates only 25% of parameters per token (effective ~7B behavior)
Export: materia-v4.Q4_0.gguf supports llama.cpp/Ollama/LM Studio
Compatible with HSAQ, Flash Attention 2, and gradient checkpointing

Scaling Roadmap

Model	Params	GPUs	Budget	Next Steps
V4 1B Dense (this)	1.34B	1× RTX 6000 Ada	$5.46	✅ Complete
V4 3B MoE	~3B (8 experts)	1× RTX 6000 Ada	~$23	🔧 Ready to train
V4 7B Dense	~7B	2× RTX 6000 Ada	~$30	🔜 Training (FSDP)

📈 Benchmarks (Planned)

Comparison targets for next evaluation phase:

Model	Size	Architecture	Notes
GPT-2 1.5B	1.5B	Transformer	Classic baseline
Mamba 1.4B	1.4B	SSM	Direct SSM competitor
S4	~1B	SSM	SSM pioneer
SpikeGPT	~1B	SNN	Direct SNN comparator
Llama 3 8B	8B	Transformer	Top-tier hybrid reference

📚 Citation

bibtex

@software{materia_v4_2026,
  author = {MethodWhite},
  title = {{MATERIA V4}: Multi-Analytical Toroidal Engine for Recursive Intelligent Analysis},
  year = {2026},
  month = {July},
  url = {https://huggingface.co/MethodWhite/materia-v4-1b-bpe},
  publisher = {HuggingFace},
  version = {1.0.0},
  note = {Toroidal JEPA-First architecture with HSAQ adaptive optimizer}
}

@misc{hsaq_optimizer_2026,
  author = {MethodWhite},
  title = {{HSAQ}: HyperSparse Adaptive Quantization — Replacing AdamW with Learnable Sparse Optimization},
  year = {2026},
  url = {https://huggingface.co/MethodWhite/materia-v4-1b-bpe},
  note = {Per-edge learnable sparsity with Straight-Through Estimator}
}

📄 License

This model is released under the MIT License.

🙏 Acknowledgments

Built with PyTorch and HuggingFace ecosystem
Trained on Brev.dev NVIDIA GPU infrastructure
Dataset: HuggingFaceFW/fineweb
GGUF conversion via llama.cpp

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"HSAQ comprime de manera inteligente los tokens, es adaptativo, no es un valor fijo, sino lo que necesita la IA como tal."

MethodWhite/materia-v4-1b-lite