41–50 of 337 posts
I think it's poor form that they are taking the GPT-4 name for an unrelated project. After all, the underlying Vicuna is merely a fine-tuned LLaMA. Plus they use the smaller 13B version. The results look interesting, however. Here's hoping that they'll add GTPQ 4bit quantizing so the 65B version of the model can be run on 2x 3090.
I think it's poor form that they are taking the GPT-4 name for an unrelated project. After all, the underlying Vicuna is merely a fine-tuned LLaMA. Plus they use the smaller 13B version. The results look interesting, however. Here's hoping that they'll add GTPQ 4bit quantizing so the 65B version of the model can be run on 2x 3090.
I think it's poor form that they are taking the GPT-4 name for an unrelated project. After all, the underlying Vicuna is merely a fine-tuned LLaMA. Plus they use the smaller 13B version. The results look interesting, however. Here's hoping that they'll add GTPQ 4bit quantizing so the 65B version of the model can be run on 2x 3090.
Someone needs to write a buyer's guide for GPUs and LLMs. For example, what's the best course of action if don't need to train anything but do want to eventually run whatever model becomes the first local-capable equivalent to ChatGPT? Do you go with Nvidia for the CUDA cores or with AMD for more VRAM? Do you do neither and wait another generation?
[1] https://developers.google.com/fonts/faq#how_can_i_get_a_lice...
The ramen example is kind of hilarious. Wonder if it would make more sense with a bigger model.
I think it's poor form that they are taking the GPT-4 name for an unrelated project. After all, the underlying Vicuna is merely a fine-tuned LLaMA. Plus they use the smaller 13B version. The results look interesting, however. Here's hoping that they'll add GTPQ 4bit quantizing so the 65B version of the model can be run on 2x 3090.
Someone needs to write a buyer's guide for GPUs and LLMs. For example, what's the best course of action if don't need to train anything but do want to eventually run whatever model becomes the first local-capable equivalent to ChatGPT? Do you go with Nvidia for the CUDA cores or with AMD for more VRAM? Do you do neither and wait another generation?
Get a 3090 or 4090. Forget about AMD.
Earlier quoted context omitted.
What did you expect?
A 14-line poem with a consistent rhyme scheme and meter. Perhaps my request should have been more specific.
This is definitely something they could be trained to be much better at, but I guess it's hasn't been a priority.
Earlier quoted context omitted.
> they're doing something really simple -- take BLIP2's ViT-L+Q-former, connect it to Vicuna-13B with a linear layer, and train just the tiny layer on some datasets of image-text pairs Oh yes. Simple! Jesus, this ML stuff makes a humble web dev like myself feel like a dog trying to read Tolstoy.
In practice, it's a lot more like web dev than you might imagine. The above means that the approach is web-dev like gluing, almost literally just, from existingliba import someop from existinglibb import anotherop from someaifw import glue a = someop(X) b = glue(a) Y = anotherop(b)
I think it's poor form that they are taking the GPT-4 name for an unrelated project. After all, the underlying Vicuna is merely a fine-tuned LLaMA. Plus they use the smaller 13B version. The results look interesting, however. Here's hoping that they'll add GTPQ 4bit quantizing so the 65B version of the model can be run on 2x 3090.
Someone needs to write a buyer's guide for GPUs and LLMs. For example, what's the best course of action if don't need to train anything but do want to eventually run whatever model becomes the first local-capable equivalent to ChatGPT? Do you go with Nvidia for the CUDA cores or with AMD for more VRAM? Do you do neither and wait another generation?
On a technical level, they're doing something really simple -- take BLIP2's ViT-L+Q-former, connect it to Vicuna-13B with a linear layer, and train just the tiny layer on some datasets of image-text pairs. But the results are pretty amazing. It completely knocks Openflamingo && even the original blip2 models out of the park. And best of all, it arrived before OpenAI's GPT-4 Image Modality did. Real win for Open Sourc…
> they're doing something really simple -- take BLIP2's ViT-L+Q-former, connect it to Vicuna-13B with a linear layer, and train just the tiny layer on some datasets of image-text pairs Oh yes. Simple! Jesus, this ML stuff makes a humble web dev like myself feel like a dog trying to read Tolstoy.
this got a chuckle out loud from me. great visual.