tinygrad VS Django

Might be a silly question but is GGML a similar/competing library to George Hotz's tinygrad [0]?

[0] https://github.com/geohot/tinygrad

at first glance i thought may its like tinygrad. but looks has many ops than that tiny grad but most maps to underlying hardware provided ops?

i wonder how well tinygrad's apporach will work out, ops fusion sounds easy, just a walk a graph, pattern match it and lower to hardware provided ops?

Anyway if anyone wants to understand the philosophy behind tinygrad, this file is great start https://github.com/geohot/tinygrad/blob/master/docs/abstract...

There are currently at least 3 ways to run llama on m1 with GPU acceleration. - mlc-llm (pre-built, only 1 model has been ported) - tinygrad (very memory efficient, not that easy to integrate into other projects) - llama-mps (original llama codebase + llama adapter support)

Hopefully we will see now full support with AMD hardware on https://github.com/geohot/tinygrad. You can read more about it on https://tinygrad.org/

tinygrad

https://github.com/geohot/tinygrad/tree/master/accel/ane

But I have not tested it on Linux since Asahi has not yet added support.

llama.cpp runs at 18ms per token (7B) and 200ms per token (65B) without quantization.

>As an example, AutoCodeRover successfully fixed issue #32347 of Django.

This bug was fixed three years ago in a one-line change.[0] Presumably the fix was already in the training data.

[0] https://github.com/django/django/pull/13933

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If found this:

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