Instructions to use vikhyatk/moondream2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vikhyatk/moondream2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="vikhyatk/moondream2", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("vikhyatk/moondream2", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use vikhyatk/moondream2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "vikhyatk/moondream2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "vikhyatk/moondream2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/vikhyatk/moondream2
- SGLang
How to use vikhyatk/moondream2 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 "vikhyatk/moondream2" \ --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": "vikhyatk/moondream2", "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 "vikhyatk/moondream2" \ --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": "vikhyatk/moondream2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use vikhyatk/moondream2 with Docker Model Runner:
docker model run hf.co/vikhyatk/moondream2
update benchmarks for 2024-05-20
Browse files- README.md +4 -3
- versions.txt +2 -1
README.md
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@@ -13,7 +13,8 @@ moondream2 is a small vision language model designed to run efficiently on edge
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| 2024-03-06 | 75.4 | 59.8 | 43.1 | 79.5 | 73.2 |
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| 2024-03-13 | 76.8 | 60.6 | 46.4 | 79.6 | 73.3 |
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| 2024-04-02 | 77.7 | 61.7 | 49.7 | 80.1 | 74.2 |
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**Usage**
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from PIL import Image
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model_id = "vikhyatk/moondream2"
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revision = "2024-05-
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model = AutoModelForCausalLM.from_pretrained(
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model_id, trust_remote_code=True, revision=revision
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```
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The model is updated regularly, so we recommend pinning the model version to a
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specific release as shown above.
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| 2024-03-06 | 75.4 | 59.8 | 43.1 | 79.5 | 73.2 |
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| 2024-03-13 | 76.8 | 60.6 | 46.4 | 79.6 | 73.3 |
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| 2024-04-02 | 77.7 | 61.7 | 49.7 | 80.1 | 74.2 |
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| 2024-05-08 | 79.0 | 62.7 | 53.1 | 81.6 | 76.1 |
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| **2024-05-20** (latest) | 79.4 | 63.1 | 57.2 | 82.1 | 76.6 |
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**Usage**
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from PIL import Image
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model_id = "vikhyatk/moondream2"
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revision = "2024-05-20"
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model = AutoModelForCausalLM.from_pretrained(
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model_id, trust_remote_code=True, revision=revision
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)
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```
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The model is updated regularly, so we recommend pinning the model version to a
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specific release as shown above.
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versions.txt
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