Instructions to use ethicalabs/Echo-DSRN-Qwen2.5-0.5B-Hybrid with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ethicalabs/Echo-DSRN-Qwen2.5-0.5B-Hybrid with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="ethicalabs/Echo-DSRN-Qwen2.5-0.5B-Hybrid", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("ethicalabs/Echo-DSRN-Qwen2.5-0.5B-Hybrid", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ethicalabs/Echo-DSRN-Qwen2.5-0.5B-Hybrid with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ethicalabs/Echo-DSRN-Qwen2.5-0.5B-Hybrid"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ethicalabs/Echo-DSRN-Qwen2.5-0.5B-Hybrid",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ethicalabs/Echo-DSRN-Qwen2.5-0.5B-Hybrid

SGLang

How to use ethicalabs/Echo-DSRN-Qwen2.5-0.5B-Hybrid with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "ethicalabs/Echo-DSRN-Qwen2.5-0.5B-Hybrid" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ethicalabs/Echo-DSRN-Qwen2.5-0.5B-Hybrid",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "ethicalabs/Echo-DSRN-Qwen2.5-0.5B-Hybrid" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ethicalabs/Echo-DSRN-Qwen2.5-0.5B-Hybrid",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ethicalabs/Echo-DSRN-Qwen2.5-0.5B-Hybrid with Docker Model Runner:
```
docker model run hf.co/ethicalabs/Echo-DSRN-Qwen2.5-0.5B-Hybrid
```

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Model Card for Echo-DSRN-Qwen2.5-0.5B-Hybrid

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.

Echo-DSRN-Hybrid: Transformers Backbone + Surprise-Gated Dual-State Recurrent Architecture

Echo-DSRN-Hybrid is a hybrid recurrent architecture designed for resource-constrained deployment on narrow, well-defined tasks (e.g., intent routing, NER, semantic classification).

The Echo-DSRN memory-injectors combine three parallel computational paths within each block:

Fast GRU state: Tracks short-range token dynamics, updated every token.
Surprise-gated slow state: Selectively accumulates long-range information, write-protected by default and triggered by prediction error.
Sliding window attention: Handles fine-grained local dependencies within a bounded context window (128 tokens).

Echo-DSRN offers constant memory overhead (O(1) recurrent core + bounded O(window_size) attention) during generation.

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Safetensors

Model size

0.7B params

Tensor type

F32

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Finetunes

2 models

Datasets used to train ethicalabs/Echo-DSRN-Qwen2.5-0.5B-Hybrid

Collection including ethicalabs/Echo-DSRN-Qwen2.5-0.5B-Hybrid

Echo-DSRN - Transformers Hybrid

Collection

Qwen2.5-0.5B-Instruct (backbone) + Echo-DSRN (memory) • 2 items • Updated 5 days ago