Instructions to use inclusionAI/Ling-lite with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use inclusionAI/Ling-lite with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="inclusionAI/Ling-lite", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("inclusionAI/Ling-lite", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use inclusionAI/Ling-lite with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "inclusionAI/Ling-lite" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "inclusionAI/Ling-lite", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/inclusionAI/Ling-lite
- SGLang
How to use inclusionAI/Ling-lite 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 "inclusionAI/Ling-lite" \ --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": "inclusionAI/Ling-lite", "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 "inclusionAI/Ling-lite" \ --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": "inclusionAI/Ling-lite", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use inclusionAI/Ling-lite with Docker Model Runner:
docker model run hf.co/inclusionAI/Ling-lite
Please consider making a new repo for Ling-lite-0415
Hi inclusionAI,
I think it would be helpful to create a separate repository for Ling-lite-0415. Currently, it's hard to tell that there's a new model available because the old one is moved to a different branch. By creating a new repo specifically for Ling-lite-0415, it will be clearer what's new and what's the same, especially when it comes to quantized versions of the model.
ie:
Ling-lite-0220
Ling-lite-0415
When quantized:
Ling-lite-0220-GGUF
Ling-lite-0415-GGUF
p.s.: (friendly tag to @bartowski )
Oh weird, thanks for pointing it out! I'll take a look at it tomorrow
Why not add the year to model names (like Ling-lite-20250415) to avoid any confusion next years?
@supernovastar good point, yes that should definitely be added.
@J22 thank you, will convert it myself
@QuantPanda I uploaded this new version to here: https://modelscope.cn/models/judd2024/chatllm_quantized_bailing/files
Thank you @J22 !
