Instructions to use metterian/llama-pro-ko-8b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use metterian/llama-pro-ko-8b with Transformers:

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

pipe = pipeline("text-generation", model="metterian/llama-pro-ko-8b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("metterian/llama-pro-ko-8b")
model = AutoModelForCausalLM.from_pretrained("metterian/llama-pro-ko-8b")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use metterian/llama-pro-ko-8b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "metterian/llama-pro-ko-8b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "metterian/llama-pro-ko-8b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/metterian/llama-pro-ko-8b

SGLang

How to use metterian/llama-pro-ko-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 "metterian/llama-pro-ko-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": "metterian/llama-pro-ko-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 "metterian/llama-pro-ko-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": "metterian/llama-pro-ko-8b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use metterian/llama-pro-ko-8b with Docker Model Runner:
```
docker model run hf.co/metterian/llama-pro-ko-8b
```

LLaMA-Pro-Ko-8B Model Card

Model Description

LLaMA-Pro is an advanced iteration of the original LLaMA model, augmented with additional Transformer blocks. Unlike its predecessor, Llama-pro, which was specialized for programming and mathematics, Llama-Pro-Ko is tailored to the language domain, undergoing post-training for enhanced performance.

Development and Training

The NLP & AI Lab at Korea University developed LLaMA-Pro-Ko, a model boasting 8 billion parameters. This model extends LLaMA2-7B by incorporating Korean tokens via vocabulary extension and was further refined by training on a Korean corpus of 10 billion tokens, exclusively without the inclusion of English data.

Language Specialization and Transfer

While previous models like Llama-ko and Llama-2-ko experienced diminished English capabilities as they learned Korean, Llama-Pro's language transfer approach aims to bolster Korean language performance with minimal impact on its English proficiency.

Bilingual Performance Evaluation

LLaMA-Pro-Ko's performance is evaluated on two fronts: its proficiency in English and its mastery of Korean, showcasing its capabilities as a bilingual model.

Korean Evaluation

Open Ko LLM Benchmark

	Ko-ARC	Ko-HellaSwag	Ko-MMLU	Ko-TruthfulQA	Ko-CommonGen V2	AVG
Llama-2-7b	31.91	41.68	34.11	48.49	30.34	37.31
beomi/open-llama-2-ko-7b	40.02	50.27	27.60	38.67	42.15	39.74
llama-pro-ko-8b	40.19	51.26	36.80	40.24	43.8	42.46

English Evaluation

Open LLM Benchmark

	ARC	HellaSwag	MMLU	TruthfulQA	Winogrande	AVG	diff
meta-llama/Llama-2-7b	53.07	78.59	46.87	38.76	74.03	58.26	0
beomi/llama-2-ko-7b	48.46	75.28	39.56	34.49	72.14	53.99	-4.28
beomi/open-llama-2-ko-7b	46.84	69.48	29.86	35.35	66.30	49.57	-8.70
llama-pro-ko-8b	53.24	77.93	47.06	38.32	72.22	57.75	-0.51

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Safetensors

Model size

9B params

Tensor type

BF16

metterian
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llama-pro-ko-8b