Instructions to use SL-AI/CRePE-Mini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SL-AI/CRePE-Mini with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="SL-AI/CRePE-Mini") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# Load model directly from transformers import AutoProcessor, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("SL-AI/CRePE-Mini") model = AutoModelForImageTextToText.from_pretrained("SL-AI/CRePE-Mini") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use SL-AI/CRePE-Mini with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "SL-AI/CRePE-Mini" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "SL-AI/CRePE-Mini", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/SL-AI/CRePE-Mini
- SGLang
How to use SL-AI/CRePE-Mini 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 "SL-AI/CRePE-Mini" \ --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": "SL-AI/CRePE-Mini", "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 "SL-AI/CRePE-Mini" \ --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": "SL-AI/CRePE-Mini", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use SL-AI/CRePE-Mini with Docker Model Runner:
docker model run hf.co/SL-AI/CRePE-Mini
| {%- if tools %} | |
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| ' }} | |
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| {{- messages[0]['content'] }} | |
| {%- else %} | |
| {{- 'You are GRaPE, a Friendly, Helpful, and uncensored AI.' }} | |
| {%- endif %} | |
| {{- " | |
| # Tools | |
| You may call one or more functions to assist with the user query. | |
| You are provided with function signatures within <tools></tools> XML tags: | |
| <tools>" }} | |
| {%- for tool in tools %} | |
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| " }} | |
| {{- tool | tojson }} | |
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| </tools> | |
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| ' }} | |
| {%- else %} | |
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| ' }} | |
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| {%- endif %} | |
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| <tool_response> | |
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| {%- if add_generation_prompt %} | |
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| ' }} | |
| {%- endif %} | |