Instructions to use ethicalabs/Echo-DSRN-486M-v0.7.6-SFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ethicalabs/Echo-DSRN-486M-v0.7.6-SFT with Transformers:

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

pipe = pipeline("text-generation", model="ethicalabs/Echo-DSRN-486M-v0.7.6-SFT", 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-486M-v0.7.6-SFT", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ethicalabs/Echo-DSRN-486M-v0.7.6-SFT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ethicalabs/Echo-DSRN-486M-v0.7.6-SFT"
# 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-486M-v0.7.6-SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ethicalabs/Echo-DSRN-486M-v0.7.6-SFT

SGLang

How to use ethicalabs/Echo-DSRN-486M-v0.7.6-SFT 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-486M-v0.7.6-SFT" \
    --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-486M-v0.7.6-SFT",
		"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-486M-v0.7.6-SFT" \
        --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-486M-v0.7.6-SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ethicalabs/Echo-DSRN-486M-v0.7.6-SFT with Docker Model Runner:
```
docker model run hf.co/ethicalabs/Echo-DSRN-486M-v0.7.6-SFT
```

You need to agree to share your contact information to access this model

This repository is publicly accessible, but you have to accept the conditions to access its files and content.

Model Card for Echo-DSRN-486M-v0.7.6-SFT

🏗️ Architecture Details

Property	Value
Model Type	echo_dsrn
Layers	19
Hidden Dim	768
Attention Heads	4
MLP Ratio	8.0
Vocab Size	32011
Hybrid Attention	True
RMSNorm	True

📊 Parameter Breakdown

Component	Parameters	% of Total
Total	486.65M (486,652,416)	100%
Embeddings	24.58M	5.05%
DSRN Blocks (Aggregate)	437.48M	89.90%
LM Head	24.58M	5.05%

🧩 Internal Block Structure (Per Layer)

Sub-Component	Parameters	Description
MLP (Feed-Forward)	9.44M	Upscaled hidden layers
DSRN Slow State	7.08M	Constant-time memory gates
GRU Fast State	3.54M	Recurrent fast path
Surprise Gating	592,896	Dynamic focus mechanism
Normalization	1,536	LayerNorm / RMSNorm

Downloads last month: 60

Safetensors

Model size

0.5B params

Tensor type

F32

Dataset used to train ethicalabs/Echo-DSRN-486M-v0.7.6-SFT

Collection including ethicalabs/Echo-DSRN-486M-v0.7.6-SFT

Kurtis-EON1

Collection

Transformer backbone + Echo-DSRN (RNN) • 15 items • Updated 3 days ago