Text Generation
Transformers
Safetensors
Turkish
qwen3
turkish
türkçe
continued-pretraining
turkish-llm
türkçe-dil-modeli
conversational
Eval Results (legacy)
text-generation-inference
Instructions to use ecloudtech/Erk-32B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ecloudtech/Erk-32B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ecloudtech/Erk-32B") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("ecloudtech/Erk-32B") model = AutoModelForCausalLM.from_pretrained("ecloudtech/Erk-32B", device_map="auto") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=256) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ecloudtech/Erk-32B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ecloudtech/Erk-32B" # 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-32B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/ecloudtech/Erk-32B
- SGLang
How to use ecloudtech/Erk-32B 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-32B" \ --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-32B", "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-32B" \ --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-32B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use ecloudtech/Erk-32B with Docker Model Runner:
docker model run hf.co/ecloudtech/Erk-32B
Kart: yalniz gomulu video
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<a href="https://huggingface.co/ecloudtech/Erk-32B/resolve/main/assets/erk-32b-terminal.mp4"><img src="https://huggingface.co/ecloudtech/Erk-32B/resolve/main/assets/erk-32b-terminal-kapak.png" alt="Terminal videosu — izlemek için tıklayın (67 sn, mp4)" width="100%"></a>
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<td align="center"><a href="https://huggingface.co/ecloudtech/Erk-32B/resolve/main/assets/01-sonuc.png"><img src="https://huggingface.co/ecloudtech/Erk-32B/resolve/main/assets/01-sonuc.png" alt="Sonuç: %71,01, tabana göre +3,37" width="260"></a><br><sub>Sonuç</sub></td>
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<td align="center"><a href="https://huggingface.co/ecloudtech/Erk-32B/resolve/main/assets/01-sonuc.png"><img src="https://huggingface.co/ecloudtech/Erk-32B/resolve/main/assets/01-sonuc.png" alt="Sonuç: %71,01, tabana göre +3,37" width="260"></a><br><sub>Sonuç</sub></td>
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<td align="center"><a href="https://huggingface.co/ecloudtech/Erk-32B/resolve/main/assets/02-egitim.png"><img src="https://huggingface.co/ecloudtech/Erk-32B/resolve/main/assets/02-egitim.png" alt="Eğitim künyesi" width="260"></a><br><sub>Eğitim künyesi</sub></td>
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