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Add the new models to the main readme. (#1797)

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Laurent Mazare
2024-03-03 16:25:14 +01:00
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@ -84,8 +84,6 @@ We also provide a some command line based examples using state of the art models
- [Replit-code-v1.5](./candle-examples/examples/replit-code/): a 3.3b LLM specialized for code completion.
- [Yi-6B / Yi-34B](./candle-examples/examples/yi/): two bilingual
(English/Chinese) general LLMs with 6b and 34b parameters.
- [EnCodec](./candle-examples/examples/encodec/): high-quality audio compression
model using residual vector quantization.
- [Quantized LLaMA](./candle-examples/examples/quantized/): quantized version of
the LLaMA model using the same quantization techniques as
[llama.cpp](https://github.com/ggerganov/llama.cpp).
@ -112,7 +110,12 @@ We also provide a some command line based examples using state of the art models
<img src="https://github.com/huggingface/candle/raw/main/candle-examples/examples/segment-anything/assets/sam_merged.jpg" width="200">
- [SegFormer](./candle-examples/examples/segformer/): transformer based semantic segmantation model.
- [Whisper](./candle-examples/examples/whisper/): speech recognition model.
- [EnCodec](./candle-examples/examples/encodec/): high-quality audio compression
model using residual vector quantization.
- [MetaVoice](./candle-examples/examples/metavoice/): foundational model for
text-to-speech.
- [T5](./candle-examples/examples/t5), [Bert](./candle-examples/examples/bert/),
[JinaBert](./candle-examples/examples/jina-bert/) : useful for sentence embeddings.
- [DINOv2](./candle-examples/examples/dinov2/): computer vision model trained
@ -220,13 +223,15 @@ If you have an addition to this list, please submit a pull request.
- BLIP.
- TrOCR.
- Audio.
- Whisper, multi-lingual text-to-speech.
- Whisper, multi-lingual speech-to-text.
- EnCodec, audio compression model.
- MetaVoice-1B, text-to-speech model.
- Computer Vision Models.
- DINOv2, ConvMixer, EfficientNet, ResNet, ViT, VGG, RepVGG, ConvNeXT,
ConvNeXTv2, MobileOne, EfficientVit (MSRA).
- yolo-v3, yolo-v8.
- Segment-Anything Model (SAM).
- SegFormer.
- File formats: load models from safetensors, npz, ggml, or PyTorch files.
- Serverless (on CPU), small and fast deployments.
- Quantization support using the llama.cpp quantized types.