Model Card for ethicalabs/Echo-DSRN-114M-v0.1.2

[!WARNING] This repository contains experimental models designed strictly for academic evaluation and research purposes.

Critical Constraints:

• No Production Deployment: Experimental models must not be deployed in commercial, enterprise, or mission-critical environments under any circumstances.
• No Liability: Experimental models are provided "as-is" without warranties of any kind. The developers assume zero liability for downstream consequences, system integration failures, or regulatory non-compliance resulting from unauthorized deployment.

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

Intended Operations: Edge-Native "Smol" Tasks

Echo-DSRN is optimized for high-frequency, low-latency edge deployment.

• Intent Dispatch: routing of user prompts to APIs, scripts, or heavier cloud models. Gradio App
• Semantic Compression: long-context document digestion with flat O(1) memory.
• Schema Translation: Deterministic conversion of unstructured text into rigid JSON or function calls.
• NER & Classification: extraction of target variables from noisy text.
• PII Sanitization: On-device redaction of sensitive data before external network
• Log Parsing: log stream monitoring and anomaly detection without cache overflow.
• Local Autocomplete: next-word prediction for local scripting and queries.

🏗️ Architecture Details

📊 Parameter Breakdown

🧩 Internal Block Structure (Per Layer)

Supervised Fine-Tuning (SFTTrainer)

2 epochs on a single AMD Instinct MI300X (192 GB RAM)

Evaluation

Citation

If you use this model in your research, please cite it as follows:

@misc{Massimo Roberto Scamarcia, title={Echo-DSRN-114M: Surprise-Gated Dual-State Recurrent Architecture for Efficient Language Modeling and Classification}, DOI={10.5281/zenodo.19848279}, publisher={Zenodo}, author={Massimo Roberto Scamarcia} }