Text Generation
Transformers
PyTorch
Safetensors
Korean
gpt_neox
Generated from Trainer
polyglot-ko
gpt-neox
KoAlpaca
text-generation-inference
Instructions to use happyduck/koal_5.8b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use happyduck/koal_5.8b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="happyduck/koal_5.8b")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("happyduck/koal_5.8b") model = AutoModelForCausalLM.from_pretrained("happyduck/koal_5.8b") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use happyduck/koal_5.8b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "happyduck/koal_5.8b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "happyduck/koal_5.8b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/happyduck/koal_5.8b
- SGLang
How to use happyduck/koal_5.8b 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 "happyduck/koal_5.8b" \ --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": "happyduck/koal_5.8b", "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 "happyduck/koal_5.8b" \ --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": "happyduck/koal_5.8b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use happyduck/koal_5.8b with Docker Model Runner:
docker model run hf.co/happyduck/koal_5.8b
metadata
license: apache-2.0
tags:
- generated_from_trainer
- polyglot-ko
- gpt-neox
- KoAlpaca
model-index:
- name: KoAlpaca-Polyglot-5.8B
results: []
language:
- ko
datasets:
- KoAlpaca-v1.1b
pipeline_tag: text-generation
Update @ 2023.06.01
- Add Safetensor sharded model weight (max shard = 1GB)
KoAlpaca-Polyglot-5.8B (v1.1b)
This model is a fine-tuned version of EleutherAI/polyglot-ko-5.8b on a KoAlpaca Dataset v1.1b
Detail Codes are available at KoAlpaca Github Repository
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0
- mixed_precision_training: Native AMP
Framework versions
- Transformers 4.29.0.dev0
- Pytorch 2.0.0+cu117
- Datasets 2.10.1
- Tokenizers 0.13.2