Instructions to use AlumiK/LingLong-317M-Chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AlumiK/LingLong-317M-Chat with Transformers:

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

pipe = pipeline("text-generation", model="AlumiK/LingLong-317M-Chat", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("AlumiK/LingLong-317M-Chat", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AlumiK/LingLong-317M-Chat with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AlumiK/LingLong-317M-Chat"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AlumiK/LingLong-317M-Chat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AlumiK/LingLong-317M-Chat

SGLang

How to use AlumiK/LingLong-317M-Chat 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 "AlumiK/LingLong-317M-Chat" \
    --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": "AlumiK/LingLong-317M-Chat",
		"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 "AlumiK/LingLong-317M-Chat" \
        --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": "AlumiK/LingLong-317M-Chat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use AlumiK/LingLong-317M-Chat with Docker Model Runner:
```
docker model run hf.co/AlumiK/LingLong-317M-Chat
```

LingLong (玲珑): A Small-Scale Chinese PreTrained Language Model

This is LingLong (玲珑), a Chinese pretrained language model trained by the College of Software at Nankai University. Built on the foundation of the GPT-3 architecture, it has been meticulously trained on a vast collection of private datasets. With a modest parameter count of only 317 million, LingLong is significantly smaller than the original GPT-3 model, yet it delivers impressive results across various NLP tasks. In Chinese, "玲珑" translates to "exquisite" or "delicate," which perfectly embodies the small yet mighty nature of this model. Therefore, we chose to name it "LingLong" in honor of its exceptional precision and finesse.

Although it's true that this model's performance isn't on par with the large pretrained language models boasting hundreds of billions of parameters, its relatively low parameter count makes it accessible to researchers with limited computing resources. As a result, this model serves as an excellent foundation for conducting follow-up research, such as fine-tuning. By utilizing this model, researchers can begin to delve into the intricacies of pretrained language models and to unravel the mysteries of language processing without the need for excessive computational resources.

This is the chat version of LingLong-317M

Downloads last month: 9

Safetensors

Model size

0.3B params

Tensor type

F32