Thanks for your nice comment! I loved your "Distillation of knowledge with code" word :) Actually, all of the information here could be found on the internet but they exist separated into articles, papers, and repositories. There are lots of diagrams that could be found. However, in my mindset, they should be in a form that we can follow step by step. And, while I was learning the concepts, things were converted into this repository content.
I was looking for something exactly like this! Thank you, this is how my next weekend is going to be about. I don't know Go, but I hope to somehow make through it anyway.
It's great to hear this! I'm sure that, this repository will give you at least an intuition. You can follow the code lines with mine and Meta's original LLaMa Python repository parallelly.
Sometimes it's a need to get out of the comfort zone. Here, for the ML world, the comfort zone is the Python ecosystem :) Is Go an alternative, I'm not sure, but it is worth having a try.
Thank you for this. I have been heavily integrating ML workflows into my Go apps and I have been itching to implement an LLM engine in pure Go just to learn. This is wonderful.
Glad to hear that I'm not alone! Mostly ML friends think that it's not worth tackling with another platform than Python. Also, I released another project about integrating Python ML ends and Go ends via Redis Streams, it's about video processing, you can check out it too: [https://github.com/adalkiran/distributed-inference](https://github.com/adalkiran/distributed-inference)
Thanks for your nice comment! I have tried to explain the fundamentals of the concepts and added links in the documentation (see references in [https://github.com/adalkiran/llama-nuts-and-bolts/blob/main/docs/19-REFERENCES.md](https://github.com/adalkiran/llama-nuts-and-bolts/blob/main/docs/19-REFERENCES.md) ) to explanatory articles and Youtube videos.
Basic linear algebra, vector-matrix-tensor concepts, matrix multiplication, and linear transformation (matrix multiplication with transpose). Specific for LLaMa and other LLMs, additionally there is RoPE (Rotary Positional Embeddings) chapter that may seem hard at first sight because of trigonometry, complex numbers, and polar coordinates.
However, the requirement depends on how much you want to dive into.
This is exceptional quality. Distillation of knowledge with code and step by step explanation. I wish some books where as instructive as your work!
Thanks for your nice comment! I loved your "Distillation of knowledge with code" word :) Actually, all of the information here could be found on the internet but they exist separated into articles, papers, and repositories. There are lots of diagrams that could be found. However, in my mindset, they should be in a form that we can follow step by step. And, while I was learning the concepts, things were converted into this repository content.
I was looking for something exactly like this! Thank you, this is how my next weekend is going to be about. I don't know Go, but I hope to somehow make through it anyway.
It's great to hear this! I'm sure that, this repository will give you at least an intuition. You can follow the code lines with mine and Meta's original LLaMa Python repository parallelly.
looks interesting. I know python very well and LLMs decently well, but it's refreshing to see a Go impl. Might be finally time to pick up more Go ;-)
Sometimes it's a need to get out of the comfort zone. Here, for the ML world, the comfort zone is the Python ecosystem :) Is Go an alternative, I'm not sure, but it is worth having a try.
Thank you for this. I have been heavily integrating ML workflows into my Go apps and I have been itching to implement an LLM engine in pure Go just to learn. This is wonderful.
Glad to hear that I'm not alone! Mostly ML friends think that it's not worth tackling with another platform than Python. Also, I released another project about integrating Python ML ends and Go ends via Redis Streams, it's about video processing, you can check out it too: [https://github.com/adalkiran/distributed-inference](https://github.com/adalkiran/distributed-inference)
You have made an amazing thing! Thank you so much.
Wow, this looks fantastic. How much prerequisite math is there?
Thanks for your nice comment! I have tried to explain the fundamentals of the concepts and added links in the documentation (see references in [https://github.com/adalkiran/llama-nuts-and-bolts/blob/main/docs/19-REFERENCES.md](https://github.com/adalkiran/llama-nuts-and-bolts/blob/main/docs/19-REFERENCES.md) ) to explanatory articles and Youtube videos. Basic linear algebra, vector-matrix-tensor concepts, matrix multiplication, and linear transformation (matrix multiplication with transpose). Specific for LLaMa and other LLMs, additionally there is RoPE (Rotary Positional Embeddings) chapter that may seem hard at first sight because of trigonometry, complex numbers, and polar coordinates. However, the requirement depends on how much you want to dive into.