Instructions to use DILAB-HYU/KoQuality-Polyglot-5.8b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DILAB-HYU/KoQuality-Polyglot-5.8b with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="DILAB-HYU/KoQuality-Polyglot-5.8b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("DILAB-HYU/KoQuality-Polyglot-5.8b")
model = AutoModelForCausalLM.from_pretrained("DILAB-HYU/KoQuality-Polyglot-5.8b", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use DILAB-HYU/KoQuality-Polyglot-5.8b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DILAB-HYU/KoQuality-Polyglot-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": "DILAB-HYU/KoQuality-Polyglot-5.8b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/DILAB-HYU/KoQuality-Polyglot-5.8b

SGLang

How to use DILAB-HYU/KoQuality-Polyglot-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 "DILAB-HYU/KoQuality-Polyglot-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": "DILAB-HYU/KoQuality-Polyglot-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 "DILAB-HYU/KoQuality-Polyglot-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": "DILAB-HYU/KoQuality-Polyglot-5.8b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use DILAB-HYU/KoQuality-Polyglot-5.8b with Docker Model Runner:
```
docker model run hf.co/DILAB-HYU/KoQuality-Polyglot-5.8b
```

nayohan commited on Nov 5, 2023

Commit

d82bc1b

1 Parent(s): 270b6dd

Update README.md

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@@ -59,7 +59,7 @@ We use [KoBEST benchmark](https://huggingface.co/datasets/skt/kobest_v1) dataset
 - learning_rate: 5e-5
 - train_batch_size: 4
 - seed: 42
-- distributed_type: multi-GPU (A100 80G)
 - num_devices: 4
 - gradient_accumulation_steps: 16
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08

 - learning_rate: 5e-5
 - train_batch_size: 4
 - seed: 42
+- distributed_type: multi-GPU (A100 80G) + No offloading
 - num_devices: 4
 - gradient_accumulation_steps: 16
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08