BQN-autograd
tinygrad
Our great sponsors
| BQN-autograd | tinygrad | |
|---|---|---|
| 1 | 58 | |
| 4 | 17,800 | |
| - | - | |
| 0.0 | 9.7 | |
| about 2 years ago | 10 months ago | |
| Python | ||
| - | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
BQN-autograd
-
Jd
Oh, thanks for clarifying, since it occurred to me that you might mean just the appeal to you, but not that you meant the field of programming! I'm no NN expert, but tinygrad looks very approachable in BQN. You might be interested in some other initial work along those lines: https://github.com/loovjo/BQN-autograd with automatic differentiation, and the smaller https://github.com/bddean/BQNprop using backprop.
tinygrad
- tinygrad: extreme simplicity, easiest framework to add new accelerators to
-
GGML – AI at the Edge
Might be a silly question but is GGML a similar/competing library to George Hotz's tinygrad [0]?
[0] https://github.com/geohot/tinygrad
-
Render neural network into CUDA/HIP code
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...
-
llama.cpp now officially supports GPU acceleration.
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)
- George Hotz building an AMD competitor to Nvidia.
-
George Hotz ROCm adventures
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/
-
The Coming of Local LLMs
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.
- Everything we know about Apple's Neural Engine
- Everything we know about the Apple Neural Engine (ANE)
- How 'Open' Is OpenAI, Really?
What are some alternatives?
BQNprop - Toy backpropagation implementation written in BQN.
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
Singeli - High-level interface for low-level programming
llama.cpp - LLM inference in C/C++
data_jd - Jd
openpilot - openpilot is an open source driver assistance system. openpilot performs the functions of Automated Lane Centering and Adaptive Cruise Control for 250+ supported car makes and models.
jsource - J engine source mirror
llama - Inference code for Llama models
tinygrad - You like pytorch? You like micrograd? You love tinygrad! ❤️
tensorflow_macos - TensorFlow for macOS 11.0+ accelerated using Apple's ML Compute framework.
GPTQ-for-LLaMa - 4 bits quantization of LLaMA using GPTQ
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.