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| author | Laurent Mazare <laurent.mazare@gmail.com> | 2023-10-20 09:08:39 +0100 |
|---|---|---|
| committer | GitHub <noreply@github.com> | 2023-10-20 09:08:39 +0100 |
| commit | 31ca4897bbff517156f7730b9562ac30061b39d5 (patch) | |
| tree | deeff4b9211896a00aef8b03864b94945dc7c26f /README.md | |
| parent | 55351ef57d53caea8ef6de9509f6d29c523bacbb (diff) | |
| download | candle-31ca4897bbff517156f7730b9562ac30061b39d5.tar.gz candle-31ca4897bbff517156f7730b9562ac30061b39d5.tar.bz2 candle-31ca4897bbff517156f7730b9562ac30061b39d5.zip | |
Readme updates. (#1134)
Diffstat (limited to 'README.md')
| -rw-r--r-- | README.md | 15 |
1 files changed, 7 insertions, 8 deletions
@@ -130,8 +130,11 @@ And then head over to <!--- ANCHOR: useful_libraries ---> -## Useful Libraries -- [`candle-lora`](https://github.com/EricLBuehler/candle-lora) provides a LoRA implementation that conforms to the official `peft` implementation. +## Useful External Resources +- [`candle-tutorial`](https://github.com/ToluClassics/candle-tutorial): a + very detailed tutorial showing how to convert a PyTorch model to Candle. +- [`candle-lora`](https://github.com/EricLBuehler/candle-lora): a LoRA implementation + that conforms to the official `peft` implementation. If you have an addition to this list, please submit a pull request. @@ -163,12 +166,8 @@ If you have an addition to this list, please submit a pull request. - Stable Diffusion v1.5, v2.1, XL v1.0. - Wurstchen v2. - Computer Vision Models. - - DINOv2. - - ConvMixer. - - EfficientNet. - - ResNet-18/34/50/101/152. - - yolo-v3. - - yolo-v8. + - DINOv2, ConvMixer, EfficientNet, ResNet, ViT. + - yolo-v3, yolo-v8. - Segment-Anything Model (SAM). - File formats: load models from safetensors, npz, ggml, or PyTorch files. - Serverless (on CPU), small and fast deployments. |