* Add a slice_set op.
* Add some testing.
* Add the dedicated kv-cache module.
* Derive debug and clone.
* Expose more kv-cache functions.
* Return the current data when appending.
* Use the new cache in the quantized phi3 model.
* Support embedding model gte-Qwen1.5-7B-instruct
This is a text embedding model based on Qwen2. They share same
model architecture except the last MLP module. This commit brings in
minimal modification of the old Qwen2 implementation to support both
models.
An example is provided, and had been verified according to the official
PyTorch implementation.
* Avoid doing the 'last-token filtering' based on the absence of attention mask.
---------
Co-authored-by: Laurent <laurent.mazare@gmail.com>
Threshold is 0.0 by default, negative values make more points included,
expanding the mask. Positive values make it more picky, making the mask
smaller.
Negative numbers start with a minus sign, which normally makes clap
consider it a flag.
* Separate quantized phi-3 implementation.
* Integrate the quantized phi3 model.=
* Small fixes, get the generation to work properly.
* Keep the old llama implementation around.
* Change the default.
* Quantized phi in a separate file.
* Add the quantized phi example + rework the model code.
* Improve the phi model.
* Get some generation out.
* Use the appropriate rope shape.
* Tweak the default prompt.
---------
Co-authored-by: Jane Doe <jane.doe@example.org>
* add support for l3b, new tokenizer
* add todo
* Add todo and use k_s model
* Use the official tokenizers.
---------
Co-authored-by: laurent <laurent.mazare@gmail.com>
* Utilize batches in Stable Diffusion that were already there, but unutilized.
Also refactor out the `save_image` function.
* Clippy + cosmetic fixes.
---------
Co-authored-by: laurent <laurent.mazare@gmail.com>
* Start adding the recurrent-gemma model.
* More griffin.
* Add the example + get the weights to load from the HF version.
* More inference code.
* Rope + kv-cache on the attention side.
* Add to the inference code.
* Add more to the recurrent gemma inference.
* Get some first inference to run.
* Add the softcap on logits.
* Fixes.
* Use partial rotary embeddings.
* Get inference to work.
* Add a comment.
* And add a readme.
* moondream implementation
* add moondream example
* change config default activation
* Add assets and integrate phi mixformer with example
* Make use of kv cache and fix seq_len bug; Clean up example code
* Add README link to example
* Remove pos_embed scaling; Remove assets; Add to README; Expand VisionConfig
* Delete image
* Use apply instead of forward
* Use latest release special token; Fix token/s accuracy; Use GeluPytorchTanh in VisionConfig v2
* Add flag to use f16
* Avoid breaking the quantized version on cuda.
---------
Co-authored-by: laurent <laurent.mazare@gmail.com>
* moondream implementation
* add moondream example
* change config default activation
* Add assets and integrate phi mixformer with example
* Make use of kv cache and fix seq_len bug; Clean up example code
* Add README link to example
* Remove pos_embed scaling; Remove assets; Add to README; Expand VisionConfig
* Delete image
* Use apply instead of forward
* Use latest release special token; Fix token/s accuracy; Use GeluPytorchTanh in VisionConfig v2
* moondream implementation
* add moondream example
* change config default activation
* Add assets and integrate phi mixformer with example
* Make use of kv cache and fix seq_len bug; Clean up example code
* Add README link to example
* Remove pos_embed scaling; Remove assets; Add to README; Expand VisionConfig
* Delete image
* Use apply instead of forward
* Pass bos token at the beginning of tensor.
* Quantize moondream.
* Forward with image bos token.
* Clippy.
* Use q4_0 quantization.
* Add pointers for sequence and tokens; Remove seq_len conditional
* Add more cuda kernels for quantized matmul.
* Add the vec-dot bits.
* Expose the quantized matmul-vec kernels.
* Also include the quantize-q8-1 kernel.
* Glue code for the q8-1 quantization.
* mm-vec product via q8-1 quantization.
* Add a test.
* Add a mm test.
* Get the test to return some sensible results.
* Also test dmmv.
* Fix the launch params.
* Allow for tweaking the force_dmmv parameter while it's experimental.
* moondream implementation
* add moondream example
* change config default activation
* Add assets and integrate phi mixformer with example
* Make use of kv cache and fix seq_len bug; Clean up example code
* Add README link to example
* Remove pos_embed scaling; Remove assets; Add to README; Expand VisionConfig
* Delete image
* Use apply instead of forward
