Instructions to use hyunseoki/ko-en-llama2-13b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hyunseoki/ko-en-llama2-13b with Transformers:

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

pipe = pipeline("text-generation", model="hyunseoki/ko-en-llama2-13b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("hyunseoki/ko-en-llama2-13b")
model = AutoModelForMultimodalLM.from_pretrained("hyunseoki/ko-en-llama2-13b")

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use hyunseoki/ko-en-llama2-13b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hyunseoki/ko-en-llama2-13b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hyunseoki/ko-en-llama2-13b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/hyunseoki/ko-en-llama2-13b

SGLang

How to use hyunseoki/ko-en-llama2-13b 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 "hyunseoki/ko-en-llama2-13b" \
    --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": "hyunseoki/ko-en-llama2-13b",
		"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 "hyunseoki/ko-en-llama2-13b" \
        --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": "hyunseoki/ko-en-llama2-13b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use hyunseoki/ko-en-llama2-13b with Docker Model Runner:
```
docker model run hf.co/hyunseoki/ko-en-llama2-13b
```

Occured problem at long context

by Se-Hun - opened Oct 12, 2023

Discussion

Se-Hun

Oct 12, 2023

I found empty output string when long context is passed to this model.
As my inference testing, i suggest that this problem is occurred in case of text longer than 2000 tokens (or about 2040 tokens) is passed.

Why this problem is occured ? Is it caused by your dataset configurations ?

Se-Hun changed discussion title from List of datasets to Occured problem at long context Oct 12, 2023

andreaKIM

Oct 21, 2023

Did you check max_position_embedding in config.json? I guess this problem occurd by token length. Also, check the tokenizer with your data language. Becuase llama's vocab does not contain much tokens except english subwords.

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