Text Generation
Transformers
PyTorch
Safetensors
Korean
gpt_neox
Generated from Trainer
polyglot-ko
gpt-neox
KoAlpaca
text-generation-inference
Instructions to use beomi/KoAlpaca-Polyglot-12.8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use beomi/KoAlpaca-Polyglot-12.8B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="beomi/KoAlpaca-Polyglot-12.8B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("beomi/KoAlpaca-Polyglot-12.8B") model = AutoModelForCausalLM.from_pretrained("beomi/KoAlpaca-Polyglot-12.8B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use beomi/KoAlpaca-Polyglot-12.8B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "beomi/KoAlpaca-Polyglot-12.8B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "beomi/KoAlpaca-Polyglot-12.8B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/beomi/KoAlpaca-Polyglot-12.8B
- SGLang
How to use beomi/KoAlpaca-Polyglot-12.8B 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 "beomi/KoAlpaca-Polyglot-12.8B" \ --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": "beomi/KoAlpaca-Polyglot-12.8B", "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 "beomi/KoAlpaca-Polyglot-12.8B" \ --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": "beomi/KoAlpaca-Polyglot-12.8B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use beomi/KoAlpaca-Polyglot-12.8B with Docker Model Runner:
docker model run hf.co/beomi/KoAlpaca-Polyglot-12.8B
- Xet hash:
- d8505e9b7965809db0b73f27b0e722564f8165b86bbb1cd33c56e6c7b0ce4509
- Size of remote file:
- 26 GB
- SHA256:
- 4b06c217dc641008ad56030c95d59940d9fd021d87a9c1321e6a5bc721bcc6ed
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