2023: The Year of AI
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2023: The Year of AI
1–10 of 84 posts
Re: 2023: The Year of AI
#2I think that it would also make sense to have a diagram that has open source achievements in Ai for 2023 (at least open weight & inference code). A lot of the announcements in the chart are behind an api so can’t be run locally.
Just thinking off the top of my head, Segment Anything, Llama 1 and 2, Mistral, Stable diffusion XL, ControlNet, Whisper are all open source AI releases this year.
Re: 2023: The Year of AI
#3This article seems very corporate centric. Like, I am able to run a ChatGPT3-ish code LLM locally on a 2015 midrange laptop. Just like this:
wget https://huggingface.co/TheBloke/deepseek-coder-6.7B-instruct-GGUF/resolve/main/deepseek-coder-6.7b-instruct.Q5_K_M.gguf
git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp
make
./main -ngl 32 -m ../deepseek-coder-6.7b-instruct.Q5_K_M.gguf --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -i -ins
Re: 2023: The Year of AI
#4Something I find interesting is that in the last year or so, the talk around AI shifted from the model architecture to the trained model. People talk about Mistral 7B e.g., not transformer with rotary position embedding and gelu feed forward network (I don't know Mistral's architecture).
Contrast this to a few years ago we'd talk about Resnet or Retina-net or whatever, not so much about the facebook pertain on Image-net when describing the model.
In 2018 I remember hearing "architecture is the new feature engineering" (mostly meaning over-fitting I undetstood). Now it's all (mostly) about the dataset and training, the architecture, in 2023, was a minor detail. I personally think architecture will make a comeback soon.
Re: 2023: The Year of AI
#5This article seems very corporate centric. Like, I am able to run a ChatGPT3-ish code LLM locally on a 2015 midrange laptop. Just like this: wget https://huggingface.co/TheBloke/deepseek-coder-6.7B-instruct-GGUF/resolve/main/deepseek-coder-6.7b-instruct.Q5_K_M.gguf git clone https://github.com/ggerganov/llama.cpp cd llama.cpp make ./main -ngl 32 -m ../deepseek-coder-6.7b-instruct.Q5_K_M.gguf --color -c 2048 --temp 0.…
I appreciate the sentiment. It definitely seems possible, but it doesn't ever seem as easy as copy + paste if you ever want performance or to go outside the same generic tutorial.
> Just like this:
Just is doing a lot of heavy lifting there.
Is this the first model you came across?
We're there any dependencies you had to install?
Did you have to check video card compatibility?
Where did you get the command arguments. It looks awfully complicated for a "just", as if there's a lot of options that aren't straightforward to use.
Etc
Re: 2023: The Year of AI
#6TLDR: openai still crushing the competition. Competing models beat benchmarks, but are borderline worthless on real tasks.
Re: 2023: The Year of AI
#7This article seems very corporate centric. Like, I am able to run a ChatGPT3-ish code LLM locally on a 2015 midrange laptop. Just like this: wget https://huggingface.co/TheBloke/deepseek-coder-6.7B-instruct-GGUF/resolve/main/deepseek-coder-6.7b-instruct.Q5_K_M.gguf git clone https://github.com/ggerganov/llama.cpp cd llama.cpp make ./main -ngl 32 -m ../deepseek-coder-6.7b-instruct.Q5_K_M.gguf --color -c 2048 --temp 0.…
No they haven't. It's actually quite weird how the whole hype-cycle is working this time; I think tech journalists are so caught up in the drameh of OAI and whatever they haven't a clue that most people can run most of these novelty UCs on their own. You don't even need a speedy rig!
Re: 2023: The Year of AI
#8TLDR: openai still crushing the competition. Competing models beat benchmarks, but are borderline worthless on real tasks.
Going to call BS on this. I've delivered multiple projects this year using open models.
Re: 2023: The Year of AI
#9This article seems very corporate centric. Like, I am able to run a ChatGPT3-ish code LLM locally on a 2015 midrange laptop. Just like this: wget https://huggingface.co/TheBloke/deepseek-coder-6.7B-instruct-GGUF/resolve/main/deepseek-coder-6.7b-instruct.Q5_K_M.gguf git clone https://github.com/ggerganov/llama.cpp cd llama.cpp make ./main -ngl 32 -m ../deepseek-coder-6.7b-instruct.Q5_K_M.gguf --color -c 2048 --temp 0.…
I appreciate the sentiment. It definitely seems possible, but it doesn't ever seem as easy as copy + paste if you ever want performance or to go outside the same generic tutorial. > Just like this: Just is doing a lot of heavy lifting there. Is this the first model you came across? We're there any dependencies you had to install? Did you have to check video card compatibility? Where did you get the command arguments.…
Ye ... with "just like this" I actually mean "proficient with compiling C projects on an Unixy system" which is like years of dev, "power user" or admin experience (not being sarcastic here). For the audience here I would say that "just" is about right though.
I have no clue about anything LLM related. I just made it run after reading some comment on HN pointing in its direction.
My point is that these locally run LLMs seems way "underreported". I even tried to make a "Show HN" post about it but it got zero interest.
But maybe I am missing something?
Re: 2023: The Year of AI
#10This article seems very corporate centric. Like, I am able to run a ChatGPT3-ish code LLM locally on a 2015 midrange laptop. Just like this: wget https://huggingface.co/TheBloke/deepseek-coder-6.7B-instruct-GGUF/resolve/main/deepseek-coder-6.7b-instruct.Q5_K_M.gguf git clone https://github.com/ggerganov/llama.cpp cd llama.cpp make ./main -ngl 32 -m ../deepseek-coder-6.7b-instruct.Q5_K_M.gguf --color -c 2048 --temp 0.…
I appreciate the sentiment. It definitely seems possible, but it doesn't ever seem as easy as copy + paste if you ever want performance or to go outside the same generic tutorial. > Just like this: Just is doing a lot of heavy lifting there. Is this the first model you came across? We're there any dependencies you had to install? Did you have to check video card compatibility? Where did you get the command arguments.…
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