Instructions to use minpeter/tiny-ko-187m-base-250718 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use minpeter/tiny-ko-187m-base-250718 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="minpeter/tiny-ko-187m-base-250718")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("minpeter/tiny-ko-187m-base-250718") model = AutoModelForCausalLM.from_pretrained("minpeter/tiny-ko-187m-base-250718", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use minpeter/tiny-ko-187m-base-250718 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "minpeter/tiny-ko-187m-base-250718" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "minpeter/tiny-ko-187m-base-250718", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/minpeter/tiny-ko-187m-base-250718
- SGLang
How to use minpeter/tiny-ko-187m-base-250718 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 "minpeter/tiny-ko-187m-base-250718" \ --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": "minpeter/tiny-ko-187m-base-250718", "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 "minpeter/tiny-ko-187m-base-250718" \ --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": "minpeter/tiny-ko-187m-base-250718", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use minpeter/tiny-ko-187m-base-250718 with Docker Model Runner:
docker model run hf.co/minpeter/tiny-ko-187m-base-250718
- Xet hash:
- 2744498e0f48cbcc7a98841e9a8216de68b826099a1576727014574b8d2759f0
- Size of remote file:
- 5.78 kB
- SHA256:
- f939a88a6228a7cbb9f9eda06d10196c6b5d24d573a6afa28910a0d0d8e57808
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