But I think it was a lot of gdb backtrace that leads me to the file.

. It seems that it is getting confused with the double quotes .

we are converting them to reshapes so that we can use standard reshape optimization transforms"

Ignoring version 0.3.1.dev202105110329 of tflite-model-maker-nightly since it has invalid metadata:

We have not released a version supporting Python 3.9 yet in PyPi.

So the issue is with the custom terminator which isn't strict about the possible parent operations? Then the minimal example is something like:

AttributeError: module 'numpy' has no attribute '_get_promotion_state'

Both libraries use signals as a identifier which leads to a namespace collision.

So they may change between versions. My guess is that for me it was an installation issue

It is triggered when bucket.exists() is called,

OOM when allocating tensor with shape[50982027264] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc [[{{node gradient_tape/UnsortedSegmentSum/pfor/UnsortedSegmentSum}}]]

we require input indices to be unique. Otherwise, not only is the output non-deterministic on GPU, but gradients are broken on any device.

so, some weights in new model have different shape compared to the old model.

The area which corresponds to the difference in the lite versions are actually different in the h5 files. Furthermore, the dimensionality of some elements have changed from the start to the red arrow and downwards. Can either of you try again when the model architecture is identical prior to conversion?

tf.app.run is deprecated in TF 2.x, please use TF tf.compat.v1.app.run for TF 2.12.

s a result, when users build ChatModel / ChatAgent with those providers, they cannot log the model with a confusing error: with mlflow.start_run():

Update DetermineArgumentLayoutsFromCompileOptions to not overwrite parameter & result memory spaces.

Mark all user-facing tracing APIs as experimental.

The main client currently connects to the rabbitmq database but occasionally connection is being dropped. I'm having trouble to get the AI to use the commands in order for me to debug them. The qa client has been rewritten using Rich to provide a very nice UI for question answering, but I haven't tested it yet. I'm a bit bogged down at work. Still hope to work on it and be done this week, at least this weekend

Hi @Gumichocopengin8 Did you brought any credits for your OPEN AI API? The above error happens when you dont have any credits in your api.

Relevant log output

No problems using TensorFlow Lite 2.18. This commit 977257e caused issue.