Text Generation
Transformers
Turkish
erk_linear
linear-attention
gated-deltanet
hybrid-attention
efficient-attention
turkish
erk
research
custom_code
conversational
Eval Results (legacy)
Instructions to use ecloudtech/Erk-Linear with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
Standart yukleme: auto_map + ErkLinearForCausalLM
Browse files- config.json +15 -7
config.json
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"model_type": "
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"base_model": "ecloudtech/Erk-14B",
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"
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"hidden_size": 5120,
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"num_hidden_layers": 40,
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"
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"gdn_config": {"head_dim": 128, "num_heads": 40, "use_gate": true, "use_short_conv": true, "mode": "chunk"},
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"load_fn": "modeling_erk_linear.load_erk_linear",
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"requires": ["transformers", "flash-linear-attention", "safetensors", "huggingface_hub"]
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}
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{
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"model_type": "erk_linear",
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"architectures": ["ErkLinearForCausalLM"],
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"auto_map": {
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"AutoConfig": "configuration_erk_linear.ErkLinearConfig",
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"AutoModelForCausalLM": "modeling_erk_linear.ErkLinearForCausalLM"
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},
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"base_model": "ecloudtech/Erk-14B",
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"gdn_layers": [1, 3, 5, 7, 10, 36, 38, 39],
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"gdn_head_dim": 128,
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"gdn_num_heads": 40,
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"gdn_use_gate": true,
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"gdn_use_short_conv": true,
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"gdn_mode": "chunk",
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"gdn_weights_file": "gdn_weights.safetensors",
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"hidden_size": 5120,
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"num_hidden_layers": 40,
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"dtype": "bfloat16",
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"transformers_version": "5.13.0"
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}
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