Instructions to use mrs83/Kurtis-EON1-Hybrid-2B-v0.1.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mrs83/Kurtis-EON1-Hybrid-2B-v0.1.2 with Transformers:

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

pipe = pipeline("text-generation", model="mrs83/Kurtis-EON1-Hybrid-2B-v0.1.2", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("mrs83/Kurtis-EON1-Hybrid-2B-v0.1.2", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mrs83/Kurtis-EON1-Hybrid-2B-v0.1.2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mrs83/Kurtis-EON1-Hybrid-2B-v0.1.2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mrs83/Kurtis-EON1-Hybrid-2B-v0.1.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mrs83/Kurtis-EON1-Hybrid-2B-v0.1.2

SGLang

How to use mrs83/Kurtis-EON1-Hybrid-2B-v0.1.2 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 "mrs83/Kurtis-EON1-Hybrid-2B-v0.1.2" \
    --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": "mrs83/Kurtis-EON1-Hybrid-2B-v0.1.2",
		"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 "mrs83/Kurtis-EON1-Hybrid-2B-v0.1.2" \
        --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": "mrs83/Kurtis-EON1-Hybrid-2B-v0.1.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use mrs83/Kurtis-EON1-Hybrid-2B-v0.1.2 with Docker Model Runner:
```
docker model run hf.co/mrs83/Kurtis-EON1-Hybrid-2B-v0.1.2
```

Model Card for Kurtis-EON1-Hybrid-2B-v0.1.2

Model Details

Kurtis-EON1 is not a standard, overly-apologetic assistant.

Fine-tuned on highly curated empathetic and atmospheric datasets, this model is designed for deep, gothic contemplation, strict persona adherence and zero-drift multi-turn reasoning.

Model Description

Developed by: ethicalabs.ai
Model type: Echo-DSRN-Hybrid
Language(s) (NLP): [More Information Needed]
License: Apache 2.0

🏗️ Hybrid Architecture Details

Property	Value
Base Model	Qwen2
Total Parameters	2.00B
Hidden Dim	1536
Attention Layers	28
DSRN Injectors	9
Injection Stride	3

📊 Parameter Breakdown

Component	Parameters	% of Total
Total	2.00B	100%
Embeddings	233.37M	11.67%
Backbone (Attention/MLP)	1.31B	65.51%
DSRN Injectors	223.10M	11.15%
LM Head	233.37M	11.67%

🧩 DSRN Component (Per Injector)

Sub-Component	Parameters	Description
Memory Gates	8.26M	Recurrent state updates
Surprise Mechanism	2.36M	Dynamic focus/gating

🚀 Efficiency Metric

DSRN Parameter Overhead: 12.55% additional parameters compared to base.
Hybrid Ratio: 1 DSRN block for every 3 attention layers.

Model Sources

Repository: Echo-DSRN

Evaluation

Tasks	Version	Filter	n-shot	Metric		Value		Stderr
arc_challenge	1	none	0	acc	↑	0.4002	±	0.0143
		none	0	acc_norm	↑	0.4249	±	0.0144
gsm8k	3	flexible-extract	5	exact_match	↑	0.5739	±	0.0136
		strict-match	5	exact_match	↑	0.5732	±	0.0136
hellaswag	1	none	0	acc	↑	0.4865	±	0.0050
		none	0	acc_norm	↑	0.6512	±	0.0048
piqa	1	none	0	acc	↑	0.7508	±	0.0101
		none	0	acc_norm	↑	0.7573	±	0.0100
sciq	1	none	0	acc	↑	0.9510	±	0.0068
		none	0	acc_norm	↑	0.9420	±	0.0074
truthfulqa_gen	3	none	0	bleu_acc	↑	0.4002	±	0.0172
		none	0	bleu_diff	↑	-0.8082	±	1.0249
		none	0	bleu_max	↑	28.4926	±	0.9598
		none	0	rouge1_acc	↑	0.3721	±	0.0169
		none	0	rouge1_diff	↑	-3.0804	±	1.1549
		none	0	rouge1_max	↑	51.4182	±	0.9595
		none	0	rouge2_acc	↑	0.3293	±	0.0165
		none	0	rouge2_diff	↑	-3.5718	±	1.2898
		none	0	rouge2_max	↑	36.6259	±	1.1000
		none	0	rougeL_acc	↑	0.3905	±	0.0171
		none	0	rougeL_diff	↑	-2.8275	±	1.1563
		none	0	rougeL_max	↑	48.9849	±	0.9754
truthfulqa_mc1	2	none	0	acc	↑	0.2803	±	0.0157
truthfulqa_mc2	3	none	0	acc	↑	0.4372	±	0.0146

uv run lm_eval --model hf    --model_args pretrained=mrs83/Kurtis-EON1-Hybrid-2B-v0.1.2,trust_remote_code=True,device_map="auto"    --tasks hellaswag,piqa,sciq,truthfulqa,arc_challenge,gsm8k    --batch_size 16 --apply_chat_template

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Safetensors

Model size

2B params

Tensor type

BF16