* 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>
* Add Llama 3.1 rope
* Clippy
* Format
* Clippy
* Add support for multiple eos tokens:
* Untagged either
* Remove either dep and fix settings.json
* Make the max positional embeddings configurable
Codellama requires bf16 for now (error to convert from bf16 to f16).
Multiprocess demo not functional for it because flash-attn only supports
f16 for now.
* Add more stats to the ggml example.
* Build a quantized model from the file content.
* Move the tensor retrieval in the main crate.
* Start adding the forward pass.
* Add more to the forward pass of the quantized llama.
* Apply the attention layers.
* Add the sampling loop.
* Get the sampling loop to work.
* Minor tweak.
* Add a quantize/dequantize test.
* Bugfix.
* Add a comment + swap the order.
* Bugfixes.
* Again set a few extra params.
* Use the appropriate kernel sizes.
* Add all the kernel sizes.
* Parallel compiling.
* Reduce the amount of parallelism.
* Add the missing kernel.
* Fix a typo.
* Remove bf16 support for now.
* Proper flash-attn parameters.
* Set the flash attention parameters.
* Add more validations.
* Setup the o_ flash attn parameters.
* More flash-attn support.
* Set more flash attn parameters.
* Add some flash-attn kernel, import the code for flash-attn v2 from Dao-AILab.
* More flash attn.
* Set up the flash attn parameters.
* Get things to compile locally.
* Move the flash attention files in a different directory.
* Build the static C library with nvcc.
* Add more flash attention.
* Update the build part.
* Better caching.
* Exclude flash attention from the default workspace.
* Put flash-attn behind a feature gate.
* Get the flash attn kernel to run.
* Move the flags to a more appropriate place.
* Enable flash attention in llama.
* Use flash attention in llama.
