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Mistral NeMo

mistral.ai

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Re: Mistral NeMo

#161

Earlier quoted context omitted.

You could consider the improvement in model performance a bit of a cheat - they beat other models "in the same size category" that have 30% fewer parameters. I still welcome this approach. 7B seems like a dead end in terms of reasoning and generalization. They are annoyingly close to statistical parrots, a world away from the moderate reasoning you get in 70B models. Any use case where that's useful can increasingly…

Except Llama 3 8b is a significant improvement over llama 2, which was basically so terrible that there was a whole community building fine tunes that are better than what the multi billion dollar company can do using a much smaller budget. With llama 3 8b things have shifted towards there being much less community fine-tunes that actually beat it. The fact that Mistral AI can still build models that beat it, means t…

Not that you aren't correct overall in terms of difficulty, but llama3 definitely still has a handful of fine tunes that I'd say outperform the base model by quite a bit, like the hermes model from Nous research, and we're only going to see more as time goes on.

Re: Mistral NeMo

#162
post #92

Earlier quoted context omitted.

Same could be said for onnx. Depends on which community you are in as to what you want.

Right - imagine how much of an impact a model release could have if it included GGUF and ONNX and MLX along with PyTorch.

I kinda wish Hugging Face just did it for people.

Re: Mistral NeMo

#163

Earlier quoted context omitted.

if you want to be lazy, 7b = 7gb of vRAM, 12b = 12gb of vRAM, but quantizing you might be able to do with with ~6-8. So any 16gb Macbook could run it (but not much else).

Welp, my data point of one shows you need more than 8 GB of vRam. When I run mistral-chat with Nemo-Instruct it crashes in 5 seconds with the error: "torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 40.00 MiB. GPU" This is on Ubuntu 22.04.4 with an NVIDIA GeForce RTX 3060 Ti with 8192MiB. I ran "nvidia-smi -lms 10" to see what it maxed out with, and it last recorded max usage of 7966MiB before the c…

When I run mistral-chat on Ubuntu 22.04 after cleaning up some smaller processes from the GPU (like gnome-remote-desktop-daemon) I am able to start Mistral-Nemo 2407 and get a Prompt on RTX 4090, but after entering the prompt it still fails with OOM, so, as someone noted, it narrowly fits 4090.

Re: Mistral NeMo

#164

Earlier quoted context omitted.

Welp, my data point of one shows you need more than 8 GB of vRam. When I run mistral-chat with Nemo-Instruct it crashes in 5 seconds with the error: "torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 40.00 MiB. GPU" This is on Ubuntu 22.04.4 with an NVIDIA GeForce RTX 3060 Ti with 8192MiB. I ran "nvidia-smi -lms 10" to see what it maxed out with, and it last recorded max usage of 7966MiB before the c…

When I run mistral-chat on Ubuntu 22.04 after cleaning up some smaller processes from the GPU (like gnome-remote-desktop-daemon) I am able to start Mistral-Nemo 2407 and get a Prompt on RTX 4090, but after entering the prompt it still fails with OOM, so, as someone noted, it narrowly fits 4090.

Agreed, it narrowly fits on RTX 4090. Yesterday I rented an RTX 4090 on vast.ai and setup Mistral-Nemo-2407. I got it to work, but just barely. I can run mistral-chat, get the prompt, and it will start generating a response to the prompt after 10 to 15 seconds. The second prompt always causes it to crash immediately from OOM error. At first I almost bought an RTX 4090 from Best Buy, but it was going to cost $2,000 after tax, so I'm glad that instead I only spent 40 cents.
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