Instructions to use inclusionAI/Ring-flash-2.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use inclusionAI/Ring-flash-2.0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="inclusionAI/Ring-flash-2.0", 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-flash-2.0", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use inclusionAI/Ring-flash-2.0 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "inclusionAI/Ring-flash-2.0" # 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-flash-2.0", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/inclusionAI/Ring-flash-2.0
- SGLang
How to use inclusionAI/Ring-flash-2.0 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-flash-2.0" \ --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-flash-2.0", "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-flash-2.0" \ --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-flash-2.0", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use inclusionAI/Ring-flash-2.0 with Docker Model Runner:
docker model run hf.co/inclusionAI/Ring-flash-2.0
Update tokenizer_config.json
Browse files- tokenizer_config.json +15 -15
tokenizer_config.json
CHANGED
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{
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"add_bos_token": false,
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"add_eos_token": false,
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"bos_token": "<|startoftext|>",
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"chat_template": "{%
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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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"fast_tokenizer": true,
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"gmask_token": "[gMASK]",
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"merges_file": null,
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"model_max_length": 1000000000000000019884624838656,
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"pad_token": "<|endoftext|>",
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"tokenizer_class": "PreTrainedTokenizerFast",
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"trust_remote_code": true,
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"vocab_file": null
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}
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{
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"add_bos_token": false,
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"add_eos_token": false,
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"bos_token": "<|startoftext|>",
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"chat_template": "{% 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'] }}{% endfor %}{% if add_generation_prompt %}{{ '<role>ASSISTANT</role>' }}{% 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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"fast_tokenizer": true,
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"gmask_token": "[gMASK]",
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"merges_file": null,
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"model_max_length": 1000000000000000019884624838656,
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"pad_token": "<|endoftext|>",
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"tokenizer_class": "PreTrainedTokenizerFast",
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"trust_remote_code": true,
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"vocab_file": null
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}
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