Instructions to use yentinglin/Llama-3.1-Taiwan-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use yentinglin/Llama-3.1-Taiwan-8B with Transformers:

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

pipe = pipeline("text-generation", model="yentinglin/Llama-3.1-Taiwan-8B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("yentinglin/Llama-3.1-Taiwan-8B")
model = AutoModelForCausalLM.from_pretrained("yentinglin/Llama-3.1-Taiwan-8B")

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use yentinglin/Llama-3.1-Taiwan-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "yentinglin/Llama-3.1-Taiwan-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "yentinglin/Llama-3.1-Taiwan-8B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/yentinglin/Llama-3.1-Taiwan-8B

SGLang

How to use yentinglin/Llama-3.1-Taiwan-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 "yentinglin/Llama-3.1-Taiwan-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": "yentinglin/Llama-3.1-Taiwan-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 "yentinglin/Llama-3.1-Taiwan-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": "yentinglin/Llama-3.1-Taiwan-8B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use yentinglin/Llama-3.1-Taiwan-8B with Docker Model Runner:
```
docker model run hf.co/yentinglin/Llama-3.1-Taiwan-8B
```

Disclaimer

This model is provided “as‑is” and without warranties of any kind. Users are solely responsible for evaluating the accuracy and suitability of the outputs. The developers assume no liability for any direct or indirect damages arising from its use.
The model is strictly not intended for high‑risk applications such as medical diagnosis, legal advice, or financial investment. For such use cases, please consult qualified professionals.

本模型「如是」（as‑is）提供，使用者須自行評估結果之正確性與適用性。開發者對於使用本模型所引發之任何直接或間接損失，不承擔任何法律責任。
嚴禁用於醫療診斷、法律諮詢、金融投資等高風險場景；若有相關需求，請尋求專業人員協助。

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Safetensors

Model size

8B params

Tensor type

BF16

Collection including yentinglin/Llama-3.1-Taiwan-8B

Taiwan LLM

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