Instructions to use ecloudtech/Erk-Linear with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ecloudtech/Erk-Linear with Transformers:

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

pipe = pipeline("text-generation", model="ecloudtech/Erk-Linear", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("ecloudtech/Erk-Linear", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ecloudtech/Erk-Linear with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/ecloudtech/Erk-Linear

SGLang

How to use ecloudtech/Erk-Linear 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 "ecloudtech/Erk-Linear" \
    --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": "ecloudtech/Erk-Linear",
		"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 "ecloudtech/Erk-Linear" \
        --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": "ecloudtech/Erk-Linear",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ecloudtech/Erk-Linear with Docker Model Runner:
```
docker model run hf.co/ecloudtech/Erk-Linear
```

aigencydev commited on 9 days ago

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@@ -20,7 +20,7 @@ pipeline_tag: text-generation
 > **Kod, protokol, teknik rapor:** [github.com/ecloudtechnology/erk-linear](https://github.com/ecloudtechnology/erk-linear) · **Temel model:** [Qwen3-14B](https://huggingface.co/Qwen/Qwen3-14B)
-## Ne olduğunu dürüstçe
 Bu, sıfırdan bir mimari değil, güçlü bir açık modelin üzerinde bir **mimari araştırmasıdır**: Türkçe bir dil modelinin dikkatinin ne kadarının, kaliteyi ölçülebilir biçimde bozmadan lineerleştirilebileceğini haritalayan bir çalışma. Sonuç, dört modern hibritin (Qwen3-Next, Kimi Linear, Jamba, Zamba) izlediği yolun muhafazakâr, kalite-öncelikli bir örneğidir.

 > **Kod, protokol, teknik rapor:** [github.com/ecloudtechnology/erk-linear](https://github.com/ecloudtechnology/erk-linear) · **Temel model:** [Qwen3-14B](https://huggingface.co/Qwen/Qwen3-14B)
+## Konumlandırma
 Bu, sıfırdan bir mimari değil, güçlü bir açık modelin üzerinde bir **mimari araştırmasıdır**: Türkçe bir dil modelinin dikkatinin ne kadarının, kaliteyi ölçülebilir biçimde bozmadan lineerleştirilebileceğini haritalayan bir çalışma. Sonuç, dört modern hibritin (Qwen3-Next, Kimi Linear, Jamba, Zamba) izlediği yolun muhafazakâr, kalite-öncelikli bir örneğidir.