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Echo-DSRN-114M: A Constant-Memory O(1) Semantic Compressor
While traditional models target general conversational reasoning, Echo-DSRN(N) is a specialized structural prototype.
It is a dual-state recurrent neural network engineered strictly for low-latency semantic compression and continuous text streaming with a permanent O(1) memory footprint.
⚙️ Echo-DSRN (114M Parameters) manages context via continuous structural compression:
- 8 Layers | 512 Hidden Dim
- Transformer Fast State + DSRN/GRU Recurrent Slow State + Surprise Gating
Initial pre-training on a single AMD Instinct MI300X, followed by localized refinement across AMD Radeon PRO GPUs and an AMD Ryzen AI Max+ 395 (Strix Halo).
🖥️ A Hugging Face Space showcasing the architecture is currently running on the free shared CPU tier.
- The Compressor: Ingest a long document and crush it into a fixed 2048-dimensional .npy state vector.
- Vector Similarity: Upload two compressed .npy states to instantly calculate cosine similarity for ultra-lightweight RAG pre-filtering.
- The CPU Streamer: Continuous, fluent text generation running on raw CPU compute.
⚠️ Disclaimer: This is a structural prototype. It has internalized formatting and conversational syntax, but it possesses zero world knowledge. It will confidently hallucinate. Use it for streaming transcription, style mimicry, and local semantic hashing, not for factual reasoning.
Try the CPU Demo: ethicalabs/Echo-DSRN-114M
Try the Model: ethicalabs/Echo-DSRN-114M
While traditional models target general conversational reasoning, Echo-DSRN(N) is a specialized structural prototype.
It is a dual-state recurrent neural network engineered strictly for low-latency semantic compression and continuous text streaming with a permanent O(1) memory footprint.
⚙️ Echo-DSRN (114M Parameters) manages context via continuous structural compression:
- 8 Layers | 512 Hidden Dim
- Transformer Fast State + DSRN/GRU Recurrent Slow State + Surprise Gating
Initial pre-training on a single AMD Instinct MI300X, followed by localized refinement across AMD Radeon PRO GPUs and an AMD Ryzen AI Max+ 395 (Strix Halo).
🖥️ A Hugging Face Space showcasing the architecture is currently running on the free shared CPU tier.
- The Compressor: Ingest a long document and crush it into a fixed 2048-dimensional .npy state vector.
- Vector Similarity: Upload two compressed .npy states to instantly calculate cosine similarity for ultra-lightweight RAG pre-filtering.
- The CPU Streamer: Continuous, fluent text generation running on raw CPU compute.
⚠️ Disclaimer: This is a structural prototype. It has internalized formatting and conversational syntax, but it possesses zero world knowledge. It will confidently hallucinate. Use it for streaming transcription, style mimicry, and local semantic hashing, not for factual reasoning.
Try the CPU Demo: ethicalabs/Echo-DSRN-114M
Try the Model: ethicalabs/Echo-DSRN-114M
