Instructions to use liswei/Taiwan-ELM-1_1B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use liswei/Taiwan-ELM-1_1B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="liswei/Taiwan-ELM-1_1B-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-1_1B-Instruct", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use liswei/Taiwan-ELM-1_1B-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-1_1B-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-1_1B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/liswei/Taiwan-ELM-1_1B-Instruct

SGLang

How to use liswei/Taiwan-ELM-1_1B-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-1_1B-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-1_1B-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-1_1B-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-1_1B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use liswei/Taiwan-ELM-1_1B-Instruct with Docker Model Runner:
```
docker model run hf.co/liswei/Taiwan-ELM-1_1B-Instruct
```

liswei commited on Jun 2, 2024

Commit

077ad6a

verified ·

1 Parent(s): 5bae26d

Update README.md

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@@ -2,11 +2,12 @@
 library_name: transformers
 license: apache-2.0
 datasets:
-- liswei/zhtw-news-and-articles-2B
 - liswei/PromptPair-TW
 - yentinglin/TaiwanChat
 base_model:
 - liswei/Taiwan-ELM-1_1B
 language:
 - zh
 pipeline_tag: text-generation

 library_name: transformers
 license: apache-2.0
 datasets:
+- liswei/Taiwan-Text-Excellence-2B
 - liswei/PromptPair-TW
 - yentinglin/TaiwanChat
 base_model:
 - liswei/Taiwan-ELM-1_1B
+- apple/OpenELM-1_1B
 language:
 - zh
 pipeline_tag: text-generation