Instructions to use liswei/Taiwan-ELM-270M-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use liswei/Taiwan-ELM-270M-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="liswei/Taiwan-ELM-270M-Instruct", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("liswei/Taiwan-ELM-270M-Instruct", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use liswei/Taiwan-ELM-270M-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "liswei/Taiwan-ELM-270M-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "liswei/Taiwan-ELM-270M-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/liswei/Taiwan-ELM-270M-Instruct

SGLang

How to use liswei/Taiwan-ELM-270M-Instruct 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 "liswei/Taiwan-ELM-270M-Instruct" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "liswei/Taiwan-ELM-270M-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "liswei/Taiwan-ELM-270M-Instruct" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "liswei/Taiwan-ELM-270M-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use liswei/Taiwan-ELM-270M-Instruct with Docker Model Runner:
```
docker model run hf.co/liswei/Taiwan-ELM-270M-Instruct
```

Efficient LLM for Taiwan

Taiwan ELM

Taiwan ELM is a family of Efficient LLMs for Taiwan base on apple/OpenELM. The project aims to provide an efficient model for researchers without access to large-scale computing resources.

The model is trained using a custom fork of LLaMA-Factory on 2B Traditional Chinese tokens and 500K instruction samples. We will extend the model to train on larger data sets and different base models if there is sufficient demand.

What is being released?

We release both pre-trained base models and instruction tuned variants with 270M and 1.1B parameters. Along with the model, datasets used to train the base and instruction-tuned models are also released.

List of released models:

List of released datasets:

Usage Examples

We adapt the LLaMA2 template:

<s>[INST] <<SYS>>
{{ system_prompt }}
<</SYS>>

{{ user_message }} [/INST]

The model could be load via AutoModelForCausalLM with trust_remote_code=True:

taiwanelm_270m = AutoModelForCausalLM.from_pretrained("liswei/Taiwan-ELM-270M", trust_remote_code=True)

We also support additional generation methods and speculative generation, please find reference at OpenELM#usage.

Downloads last month: 9

Safetensors

Model size

0.3B params

Tensor type

F32

Model tree for liswei/Taiwan-ELM-270M-Instruct

Base model

apple/OpenELM-270M

Finetuned

(5)

this model

Datasets used to train liswei/Taiwan-ELM-270M-Instruct

Spaces using liswei/Taiwan-ELM-270M-Instruct 6

Collection including liswei/Taiwan-ELM-270M-Instruct

Taiwan ELM

Collection

Taiwan ELM is a family of Efficient LLMs for Taiwan with open weights/datasets/checkpoints and affordable sizes (270M/1.1B) • 5 items • Updated Jun 2, 2024