Instructions to use nlpai-lab/kullm-polyglot-5.8b-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nlpai-lab/kullm-polyglot-5.8b-v2 with Transformers:

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

pipe = pipeline("text-generation", model="nlpai-lab/kullm-polyglot-5.8b-v2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("nlpai-lab/kullm-polyglot-5.8b-v2")
model = AutoModelForCausalLM.from_pretrained("nlpai-lab/kullm-polyglot-5.8b-v2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use nlpai-lab/kullm-polyglot-5.8b-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nlpai-lab/kullm-polyglot-5.8b-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nlpai-lab/kullm-polyglot-5.8b-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/nlpai-lab/kullm-polyglot-5.8b-v2

SGLang

How to use nlpai-lab/kullm-polyglot-5.8b-v2 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 "nlpai-lab/kullm-polyglot-5.8b-v2" \
    --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": "nlpai-lab/kullm-polyglot-5.8b-v2",
		"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 "nlpai-lab/kullm-polyglot-5.8b-v2" \
        --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": "nlpai-lab/kullm-polyglot-5.8b-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use nlpai-lab/kullm-polyglot-5.8b-v2 with Docker Model Runner:
```
docker model run hf.co/nlpai-lab/kullm-polyglot-5.8b-v2
```

kullm-polyglot-5.8b-v2 / README.md

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metadata

license: apache-2.0
datasets:
  - nlpai-lab/kullm-v2
language:
  - ko

KULLM-Polyglot-5.8B-v2

This model is a parameter-efficient fine-tuned version of EleutherAI/polyglot-ko-5.8b on a KULLM v2

Detail Codes are available at KULLM Github Repository

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 3e-4
train_batch_size: 128
seed: 42
distributed_type: multi-GPU (A100 80G)
num_devices: 4
gradient_accumulation_steps: 8
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 8.0

Framework versions

Transformers 4.28.1
Pytorch 2.0.0+cu117
Datasets 2.11.0
Tokenizers 0.13.3