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648596c073
* added chatGLM readme * changed wording in readme * added readme for chinese-clip * added readme for convmixer * added readme for custom ops * added readme for efficientnet * added readme for llama * added readme to mnist-training * added readme to musicgen * added readme to quantized-phi * added readme to starcoder2 * added readme to whisper-microphone * added readme to yi * added readme to yolo-v3 * added readme to whisper-microphone * added space to example in glm4 readme * fixed mamba example readme to run mamba instead of mamba-minimal * removed slash escape character * changed moondream image to yolo-v8 example image * added procedure for making the reinforcement-learning example work with a virtual environment on my machine * added simple one line summaries to the example readmes without * changed non-existant image to yolo example's bike.jpg * added backslash to sam command * removed trailing - from siglip * added SoX to silero-vad example readme * replaced procedure for uv on mac with warning that uv isn't currently compatible with pyo3 * added example to falcon readme * added --which arg to stella-en-v5 readme * fixed image path in vgg readme * fixed the image path in the vit readme * Update README.md * Update README.md * Update README.md --------- Co-authored-by: Laurent Mazare <laurent.mazare@gmail.com>
26 lines
1.2 KiB
Markdown
26 lines
1.2 KiB
Markdown
# candle-moondream
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[Moondream](https://github.com/vikhyat/moondream) is a computer-vision model can answer real-world questions about images. It's tiny by today's models, with only 1.6B parameters. That enables it to run on a variety of devices, including mobile phones and edge devices.
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## Running some examples
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First download an example image
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```bash
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$ wget https://raw.githubusercontent.com/vikhyat/moondream/main/assets/demo-1.jpg
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```
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<img src="https://raw.githubusercontent.com/vikhyat/moondream/main/assets/demo-1.jpg" width="200">
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Now you can run Moondream from the `candle-examples` crate:
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```bash
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$ cargo run --example moondream --release -- --prompt "Describe the people behind the bikers?" --image "candle-examples/examples/yolo-v8/assets/bike.jpg"
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avavx: false, neon: true, simd128: false, f16c: false
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temp: 0.00 repeat-penalty: 1.00 repeat-last-n: 64
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retrieved the files in 3.395583ms
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Running on CPU, to run on GPU(metal), build this example with `--features metal`
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loaded the model in 5.485493792s
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loaded and encoded the image Tensor[dims 3, 378, 378; f32] in 4.801396417s
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starting the inference loop
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The girl is eating a hamburger.<
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9 tokens generated (0.68 token/s)
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``` |