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)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("inclusionAI/Ling-lite", trust_remote_code=True, device_map="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
Download model-00023-of-00066.safetensors from inclusionAI/Ling-lite: direct link, hf CLI and curl.
- Browser
- Download file 738 MB
-
https://huggingface.co/inclusionAI/Ling-lite/resolve/main/model-00023-of-00066.safetensors
- Command line
-
hf download hf://inclusionAI/Ling-lite/model-00023-of-00066.safetensors
-
curl -L -o model-00023-of-00066.safetensors https://huggingface.co/inclusionAI/Ling-lite/resolve/main/model-00023-of-00066.safetensors
738 MB
- Xet hash:
- 31717317f7d75cf138e846f574f498323d584a49937692010e98d1f659812043
- Size of remote file:
- 738 MB
- SHA256:
- 7ddbf8ab7c1ea10a8fae4fc28e03a43081c827ac56955edf3bda59f7d8502ce6
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