tinygrad
minikeyvalue
| tinygrad | minikeyvalue | |
|---|---|---|
| 58 | 18 | |
| 17,800 | 2,878 | |
| - | - | |
| 9.7 | 0.0 | |
| 10 months ago | 3 months ago | |
| Python | Go | |
| MIT License | MIT License |
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tinygrad
- tinygrad: extreme simplicity, easiest framework to add new accelerators to
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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
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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...
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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.
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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/
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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?
minikeyvalue
- A 4+1 node storage cluster intended for AI ingest datasets. What platform should we use? (ceph, btrfs, OpenZFS, TruNas Scale?
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File Systems implemented in Go
minikeyvalue - A ~1000 line distributed key value store.
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Could people put where they are from approximately on their posts because its pointless for some of us to answer questions from people in India.
Sure, you can do something like this in 1000 LOC, but it seems unlikely that a uni student would be writing that type of program.
- minikeyvalue
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A distributed key value store in under 1000 lines open-sourced by comma.ai
Hrm. How does the distributed part work? I'm somewhat confused there. I've opened an issue to ask about this too.
Handling errors with "ugh"
- A distributed key value store in under 1000 lines
What are some alternatives?
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
LevelDB - LevelDB is a fast key-value storage library written at Google that provides an ordered mapping from string keys to string values.
llama.cpp - LLM inference in C/C++
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.
mynewt-nimble - Apache mynewt
llama - Inference code for Llama models
Apache Tomcat - Apache Tomcat
tensorflow_macos - TensorFlow for macOS 11.0+ accelerated using Apple's ML Compute framework.
distribkv - Distributed key-value database in Go
GPTQ-for-LLaMa - 4 bits quantization of LLaMA using GPTQ
gcsfuse - A user-space file system for interacting with Google Cloud Storage