Farook (EDev) Al-Sammarraie

I've looked into building each runtime for Android and here's what I found out: ## TFLite Google's warning about AI-Edge-Torch being experimental is quite the understatement. After much struggling I...

I tried getting AI-Edge-Torch to work again, this time using a docker container. The code seemed to run without issue until it killed itself, seemingly due to lack of RAM...

I managed to get input into and output out of the converted model. I used the python version of TFLite but will be moving the code to C++ since the...

I've attempted to generate an int8 quantized model, but no matter how much I tried, the output was garbled and nothing like the fp16 one. I should mention that I...

@freedomtan How did you manage to get the weight cache? I believe my device was running out of memory while attempting to generate it.

Thanks @freedomtan @anhappdev, I managed to get the example to compile for the x86-64 emulator, I needed to add the following config to bazelrc: ``` build:android_x86_64 --config=android build:android_x86_64 --cpu=x86_64 build:android_x86_64...

The next step I assume is to build a pipeline for LLM based on this example. Could you please confirm? @freedomtan, Alternatively, I could help in testing the different datasets...

> max input tokens: 1,024 max output tokens: 1,024/2,048 > > (input tokens, output tokens) = ((1024, 2048), (1024, 512)) > > query == sample == prompt, here > >...

@anhappdev I don't know about mlperf users, but as someone who personally doesn't use google play services, I generally appreciate github or [F-droid](https://f-droid.org/) builds when available. If we automate the...

The release process itself is through github workflow right? We are already generating both an APK along with the AAB we're using for play store, all we need to do...