nausea, noun. a feeling of loathing or disgust.

This is interesting - compared to LoRa, it allows LLaMA to also accept images as inputs. And, I believe it is orthogonal to using LoRa. Meaning, they possibly can be used together. I'm unsure about the training stability though. I know that LoRa training allows ridiculously high learning rates (1e-5 for Text encoder), especially for dreambooth. Using LoRa for the frozen weights + LLaMA adapter is an interesting thing to explore.

Edit: spelling

Oh I've used it to <insert something I previously used to do to do with google>. It's great.

Pythonese is quite useful, from what I hear. Especially the Torchese dialect.

You know what? provide me with a 2-3 sample "good" responses to the above post, and explain why they make for a better response than what I wrote, and I'll actually use them from now on to respond to low-effort posts from this sub.

Sir, this is r/MachineLearning. May I take your quality contribution?

OP is peaking on Dunning-Kruger curve right now.

If you say so.

This is actually a very good PR material, as it will save engineers' time. Just opened it and referenced your comment. https://github.com/noahshinn024/reflexion-human-eval/pull/1

Nope. Ability to input something doesn't mean being able to use it reliably. For example, take this post - your eyes have an ability to input all the info on the screen, but as a contribution, this post is pretty worthless. And, you are a lot smarter than GPT-4, I think.

Edit: spelling

Patenting a dropout feels a lot like NFTs - it's useless. So why bother?

Edit:

What I don't understand is how can anyone prove that someone is multiplying together matrices in some way as long as they don't admit to that themselves.

That's like someone patenting a thought. If you think about a particular patented pair of pants™, can you be sued for propagating a patented neural activity through your bio network? It's absurd.

&gt;25% improvement over Whisper
&gt;Not open source
&gt;doubt.jpeg

No.

Isn't this kind of high-quantity-low-quality trend inevitable after some threshold popularity of the base topic? Is there any reason to try to fight the inevitable, instead of forming more niche, less popular communities?

The laziest option is fp16 quantization. As easy as model.half() on most torch-based models. Halves the physical size of the model. You could also try knowledge distillation (read up on how distilbert was made, for example). You can also do stuff that is more arch-specific, like if you have a transformer, you could use xformers efficient attention for example. The list goes on and on.

> reduce excess bulk in a NN without sacrificing performance

Simply put, that is not possible. There's literally always a trade-off. So, the question is, what are you willing to sacrifice? How much performance are you willing to forego?

Looking forward to updates. :)

Go for vast.ai if you don't have a huge budget. You could rent a 24GB vram instance for 0.4$/hr.

> replacement for Machine Learning Drama Twitter

FTFY

u/fchollet is probably frothing right now.

&#x200B;

Edit* spelling

Everything is slow and hard to implement on tensorflow, without much redeemable excuses either (compared to JAX e.g.).

Solid work. This reminds me of that internet explorer meme.

If you are looking to maximize the TFLOP per $, just use vast.ai. Unless you got enterprise-level VRAM needs, vast will likely be much cheaper than anything a cloud provider lists.

Correct me if I'm wrong, but this post feels like a thinly-veiled subreddit promotion, and nothing else. :)

Is this just rewording the TypeError's str description? What is the information context for the ChatGPT?