Instructions to use inclusionAI/Ring-lite with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use inclusionAI/Ring-lite with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="inclusionAI/Ring-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/Ring-lite", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use inclusionAI/Ring-lite with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "inclusionAI/Ring-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/Ring-lite", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/inclusionAI/Ring-lite
- SGLang
How to use inclusionAI/Ring-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/Ring-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/Ring-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/Ring-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/Ring-lite", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use inclusionAI/Ring-lite with Docker Model Runner:
docker model run hf.co/inclusionAI/Ring-lite
Update tokenizer_config.json
Browse files- tokenizer_config.json +1 -1
tokenizer_config.json
CHANGED
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@@ -2140,7 +2140,7 @@
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"<|number_end|>"
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],
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"bos_token": "<|startoftext|>",
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"chat_template": "{% set ns = namespace() %}{% set ns.system_present = false %}{% set thinking_option = 'on' %}{% if enable_thinking is defined and not enable_thinking %}{% set thinking_option = 'off' %}{% endif %}{% for message in messages %}{% set role = message['role'] | lower %}{% if role == 'system' %}{% set ns.system_present = true %}{
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"clean_up_tokenization_spaces": false,
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"cls_token": "[CLS]",
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"eos_token": "<|endoftext|>",
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"<|number_end|>"
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],
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"bos_token": "<|startoftext|>",
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+
"chat_template": "{% set ns = namespace() %}{% set ns.system_present = false %}{% set thinking_option = 'on' %}{% if enable_thinking is defined and not enable_thinking %}{% set thinking_option = 'off' %}{% endif %}{% for message in messages %}{% set role = message['role'] | lower %}{% if role == 'system' %}{% set ns.system_present = true %}{% endif %}{% endfor %}{% if not ns.system_present %}{{ '<role>SYSTEM</role>detailed thinking ' + thinking_option }}{% endif %}{% for message in messages %}{% set role = message['role'] | lower %}{% if role == 'user' %}{% set role = 'HUMAN' %}{% endif %}{% set role = role | upper %}{{ '<role>' + role + '</role>' + message['content'] + ('\n' if role == 'SYSTEM' and message['content'] != '' else '') + ('detailed thinking ' + thinking_option if role == 'SYSTEM' else '') }}{% endfor %}{% if add_generation_prompt %}{{ '<role>ASSISTANT</role>' }}{% if thinking_option == 'on' %}{{ '<think>\n' }}{% endif %}{% endif %}",
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"clean_up_tokenization_spaces": false,
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"cls_token": "[CLS]",
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"eos_token": "<|endoftext|>",
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