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…
Mistral NeMo
161–165 of 165 posts
Re: Mistral NeMo
#162Earlier 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.
Re: Mistral NeMo
#163Earlier 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…
Re: Mistral NeMo
#164Earlier 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.