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 Jul 30

Commit

214a908

verified ·

1 Parent(s): 77d5ecc

Update README.md

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README.md +16 -6

README.md CHANGED Viewed

@@ -5,6 +5,8 @@ base_model:
   - Qwen/Qwen3-14B
 language:
   - tr
 tags:
   - linear-attention
   - gated-deltanet
@@ -13,6 +15,7 @@ tags:
   - turkish
   - erk
   - research
 inference: false
 model-index:
   - name: Erk-Linear
@@ -32,7 +35,7 @@ model-index:
 **%20-lineer hibrit:** Erk-14B'nin 40 dikkat katmanından 8'i, subquadratic bir lineer-dikkat mekanizmasına ([Gated DeltaNet](https://github.com/fla-org/flash-linear-attention)) damıtılmıştır. Kalan 32 katman softmax "çıpa" olarak korunur.
-> ⚠️ **Standart yükleme çalışmaz.** Bu özel bir hibrit mimaridir (`model_type: erk-linear-hybrid`); `AutoModelForCausalLM`, `pipeline`, vLLM ve SGLang **doğrudan çalışmaz**. Yüklemek için aşağıdaki özel loader (`modeling_erk_linear.py`) ve `flash-linear-attention` gerekir.
 > **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)
@@ -62,13 +65,20 @@ pip install torch transformers flash-linear-attention safetensors huggingface_hu
 ```
 ```python
-from modeling_erk_linear import load_erk_linear   # depodaki dosya
-model, tokenizer = load_erk_linear()
-ids = tokenizer("Türkiye'nin başkenti", return_tensors="pt").to(model.device)
-print(tokenizer.decode(model.generate(**ids, max_new_tokens=20)[0], skip_special_tokens=True))
 ```
-Loader Erk-14B tabanını ve 8 katmanın Gated DeltaNet ağırlıklarını indirip hibridi kurar. GDN sarmalayıcısı cache'li üretimde (`use_cache=True`) recurrent + convolution durumunu adımlar arası devreder; `model.generate()` çıktısı tam-yeniden-hesaplamayla sayısal gürültüye kadar aynıdır. Adımlar: [GitHub deposu](https://github.com/ecloudtechnology/erk-linear).
 ## Sınırlamalar

   - Qwen/Qwen3-14B
 language:
   - tr
+library_name: transformers
+pipeline_tag: text-generation
 tags:
   - linear-attention
   - gated-deltanet
   - turkish
   - erk
   - research
+  - custom_code
 inference: false
 model-index:
   - name: Erk-Linear
 **%20-lineer hibrit:** Erk-14B'nin 40 dikkat katmanından 8'i, subquadratic bir lineer-dikkat mekanizmasına ([Gated DeltaNet](https://github.com/fla-org/flash-linear-attention)) damıtılmıştır. Kalan 32 katman softmax "çıpa" olarak korunur.
+> **Gereksinim:** `trust_remote_code=True`, `flash-linear-attention` ve bir CUDA GPU'su. Mimari özeldir (`model_type: erk_linear`) ve Gated DeltaNet çekirdekleri Triton kullanır.
 > **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)
 ```
 ```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+model = AutoModelForCausalLM.from_pretrained("ecloudtech/Erk-Linear", trust_remote_code=True)
+tok   = AutoTokenizer.from_pretrained("ecloudtech/Erk-Linear")
+msg = [{"role": "user", "content": "Türkiye'nin başkenti neresidir?"}]
+ids = tok.apply_chat_template(msg, add_generation_prompt=True, return_tensors="pt").to(model.device)
+print(tok.decode(model.generate(ids, max_new_tokens=48)[0][ids.shape[1]:], skip_special_tokens=True))
 ```
+Hibrit iki kaynaktan kurulur: **gövde** (32 softmax katmanı + gömme/LM başı) [`ecloudtech/Erk-14B`](https://huggingface.co/ecloudtech/Erk-14B) deposundan, **8 GDN katmanı** bu depodan (`gdn_weights.safetensors`). `from_pretrained` ikisini birleştirip çalışır bir nedensel dil modeli döndürür — `.generate()`, sohbet şablonu ve `use_cache` standart biçimde çalışır.
+GDN sarmalayıcısı cache'li üretimde recurrent + convolution durumunu adımlar arası **devreder**; çıktı tam-yeniden-hesaplamayla (`use_cache=False`) sayısal gürültüye kadar aynıdır. Sarmalayıcı tek-dizi referans kullanım içindir; eş zamanlı/batch-paylaşımlı serviste her dizi için ayrı durum yönetimi gerekir.
 ## Sınırlamalar