Instructions to use EK-01/SyntheticLanguageAssociationArea_SLAA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use EK-01/SyntheticLanguageAssociationArea_SLAA with Transformers:

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

pipe = pipeline("text-generation", model="EK-01/SyntheticLanguageAssociationArea_SLAA")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("EK-01/SyntheticLanguageAssociationArea_SLAA")
model = AutoModelForCausalLM.from_pretrained("EK-01/SyntheticLanguageAssociationArea_SLAA")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use EK-01/SyntheticLanguageAssociationArea_SLAA with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "EK-01/SyntheticLanguageAssociationArea_SLAA"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "EK-01/SyntheticLanguageAssociationArea_SLAA",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/EK-01/SyntheticLanguageAssociationArea_SLAA

SGLang

How to use EK-01/SyntheticLanguageAssociationArea_SLAA 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 "EK-01/SyntheticLanguageAssociationArea_SLAA" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "EK-01/SyntheticLanguageAssociationArea_SLAA",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "EK-01/SyntheticLanguageAssociationArea_SLAA" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "EK-01/SyntheticLanguageAssociationArea_SLAA",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use EK-01/SyntheticLanguageAssociationArea_SLAA with Docker Model Runner:
```
docker model run hf.co/EK-01/SyntheticLanguageAssociationArea_SLAA
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

SyntheticLanguageAssociationArea_SLAA (Specialized Robot Brain)

This is an experimental merge of pre-trained language models created using mergekit.

By leveraging aggressive DARE/TIES parameter reduction, this project explores highly efficient, eco-friendly "Green AI" optimization—maximizing model performance while completely bypassing the environmental degradation and carbon footprint of traditional training.

Merge Details

Merge Method

This model was merged using the DARE TIES merge method using SmolLM2-360M-Instruct structural layers as the foundational base.

Models Merged

The following models were included in the merge:

HuggingFaceTB/SmolLM2-360M-Instruct
Custom experimental checkpoint slices (Internal/Local)

Configuration

The following YAML configuration layout was used to produce this model:

merge_method: dare_ties
base_model: HuggingFaceTB/SmolLM2-360M-Base
models:
  - model: HuggingFaceTB/SmolLM2-360M-Instruct
    parameters:
      weight: 0.65
      density: 1.0   # Keep 100% of the core grammar paths
  - model: HuggingFaceTB/SmolLM2-360M-Instruct
    parameters:
      weight: 0.35
      density: 0.15  # Drops 85% of Instruct's facts, code, and safety bloat to isolate specific structural layers

Licensing & Attribution

This project is officially distributed under the Apache 2.0 License to ensure absolute compliance with upstream requirements. Huge credit to the Hugging Face Team for the exceptional SmolLM2 architecture.