Comments (1)
I would think that compared to mlc-llm it's more of a general framework, e.g. we have a bunch of vision models or audio models. Compared to tvm, candle tries to be much closer to the pytorch experience so that it's easy to port models, also it's in rust rather than c++ and my guess is that it's also simpler/with a smaller scope when it comes to platform support.
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Related Issues (20)
- unsupported op_type STFT for op
- ONNX: MaxPool with pads != 0
- Qwen2: can not run with the latest Qwen2 models HOT 3
- How to Implement New Operators Using CUDA Host Functions Along with Thrust and CUB Libraries HOT 1
- Implement unfold function
- Implement torch.scatter HOT 1
- Provide a simple Stable Diffusion 3 (SD3) inference example
- Status of Apple silicon M3 GPU support?
- Quantized-t5 models on Cuda HOT 2
- WASM library examples require HTTPS
- How to select which GPU to use HOT 3
- Metal memory leak multiplying matrices HOT 3
- Is it advisable to avoid variable shadowing when using Candle?
- candle-flash-attn infinite compile time HOT 1
- Model Context Not Resetting Between Requests Leading to Invalid Responses
- Tensor not dropped after another tensor was assigned to variable in cuda HOT 3
- Mamba model is broken with `f16` precision HOT 1
- Slow YOLOv8 using MKL
- candle examples fail on cuda with CUDA_DEVICE_NO_ERROR. Device caps of 7.5 HOT 2
- Parler TTS support?
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