Model Card for ethicalabs/Echo-DSRN-114M

The Echo-DSRN(N) (Dual State Recurrent Neural Network, short name: Echo-DSRN, also know as echo) is a novel architecture specifically designed to be a viable alternative for low-resource tasks that are currently being inefficiently handled by the excessive scale of Large Language Models (LLMs) 🌱

⚠️ Important Notice

This is a research prototype and demo model.

• Not production-ready
• Will hallucinate and give incorrect answers
• Do not use for any real-world decisions
• Intended for architecture experimentation only

What Works

• Text generation is fluent
• Memory usage is constant O(1)
• Runs on CPUs, NPUs, GPUs (Tested on AMD's ROCm and Apple's MPS)

What Doesn't Work

• Factual accuracy
• Instruction following
• Common sense reasoning

🏗️ Architecture Details

Property	Value
Model Type	echo_dsrn
Layers	8
Hidden Dim	512
Attention Heads	4
MLP Ratio	8.0
Vocab Size	32011
Hybrid Attention	True
RMSNorm	True

📊 Parameter Breakdown

Component	Parameters	% of Total
Total	114.69M (114,687,488)	100%
Embeddings	16.39M	14.29%
DSRN Blocks (Aggregate)	81.91M	71.42%
LM Head	16.39M	14.29%

🧩 Internal Block Structure (Per Layer)

Sub-Component	Parameters	Description
MLP (Feed-Forward)	4.20M	Upscaled hidden layers
DSRN Slow State	3.15M	Constant-time memory gates
GRU Fast State	1.58M	Recurrent fast path
Surprise Gating	264,192	Dynamic focus mechanism
Normalization	1,024	LayerNorm / RMSNorm

Supervised Fine-Tuning (SFTTrainer)

5 epochs on a single AMD Ryzen AI Max+ 395 (128 GB RAM)

Evaluation

Work in progress.