Instructions to use echo840/Monkey with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use echo840/Monkey with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="echo840/Monkey", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("echo840/Monkey", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use echo840/Monkey with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "echo840/Monkey" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "echo840/Monkey", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/echo840/Monkey
- SGLang
How to use echo840/Monkey 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 "echo840/Monkey" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "echo840/Monkey", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "echo840/Monkey" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "echo840/Monkey", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use echo840/Monkey with Docker Model Runner:
docker model run hf.co/echo840/Monkey
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README.md
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In order to generate more detailed captions, we provide some prompt examples so that you can conduct more interesting explorations. You can modify these two variables in the `caption` function to implement different prompt inputs for the caption task, as shown below:
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query = "Generate the detailed caption in English
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chat_query = "Generate the detailed caption in English
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- Generate the detailed caption in English.
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- Explain the visual content of the image in great detail.
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In order to generate more detailed captions, we provide some prompt examples so that you can conduct more interesting explorations. You can modify these two variables in the `caption` function to implement different prompt inputs for the caption task, as shown below:
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```
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query = "Generate the detailed caption in English: "
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chat_query = "Generate the detailed caption in English: "
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```
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- Generate the detailed caption in English.
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- Explain the visual content of the image in great detail.
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