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
```

SyntheticLanguageAssociationArea_SLAA / README.md

EK-01

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	---
	base_model:
	- HuggingFaceTB/SmolLM2-360M-Instruct
	library_name: transformers
	tags:
	- mergekit
	- merge
	license: apache-2.0
	pipeline_tag: text-generation
	---

	# SyntheticLanguageAssociationArea_SLAA (Specialized Robot Brain)

	This is an experimental merge of pre-trained language models created using [mergekit](https://github.com).

	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](https://arxiv.org/abs/2311.03099) 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](https://huggingface.co/HuggingFaceTB/SmolLM2-360M-Instruct)
	* Custom experimental checkpoint slices (Internal/Local)

	### Configuration

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

	```yaml
	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.