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Model Card for ethicalabs/Echo-DSRN-114M-v0.1.2-Base
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
Pre-Training
Truncated Backpropagation Through Time (TBPTT) on Fineweb-EDU + Smoltalk2 (no think) (6BT)
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ethicalabs Echo-DSRN-114M-v0.1.2-Base online free url in huggingface.co:
Echo-DSRN-114M-v0.1.2-Base is an open source model from GitHub that offers a free installation service, and any user can find Echo-DSRN-114M-v0.1.2-Base on GitHub to install. At the same time, huggingface.co provides the effect of Echo-DSRN-114M-v0.1.2-Base install, users can directly use Echo-DSRN-114M-v0.1.2-Base installed effect in huggingface.co for debugging and trial. It also supports api for free installation.
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