Horniness aside, is Goliath really the best model right now for roleplaying? I’m getting a bit of fomo from not being able to run this model locally, so I would like to know if there are 70B or 34B models that hold their own against Goliath in terms of RP. I have 24GB vram so a 2.6bpw 70B (a little unstable) or a 5bpw 34B is the best I can run.
Hi! What are your settings for Ooba to get this to work? On Windows 11 on a single 3090, I keep getting CUDA out of memory error trying to load a 2.4bpw 70B model with just 4k context. It’s annoying because this used to work but after a recent update it just won’t load anymore.
The options you are seeing are different quants of the same model. For 7Bs, you generally want to stick to Q4_K_M and up. Generally, the bigger the file size, the closer its quality is to the original unquantized model.
For 7B models, your 16GB unified memory should be able to run the Q6_K variant with 8192 context size no problem. The model you’re looking at is good but it’s slightly dated at this point. Hard to recommend models without knowing your specific use case for it, but here goes nothing:
I recommend trying out some 13Bs as well. In my experience, a good 13B is still better than a good 7B (for roleplaying purposes at least). With 13Bs, I recommend using Q5_K_M variants with 6144 context size. KoboldCpp sets the role scaling automatically, but I’m not sure how LMStudio handles it. Here are some models you can try out:
The latest version of KoboldCpp v1.50.1 now loads this model properly.
Glad to see dolphin 2.1 still getting love! I too prefer it to even its newer versions when it comes to creative storytelling.
Have you tried any 34Bs? I found even just the Q3_K_M quant of Nous-Cabypara 34B to be more creative and coherent than any smaller model I’ve tried. Try using a low-temp preset like Kobold’s Liminal Drift. If on SillyTavern, I found the Roleplay context template and instruct mode to work well with this.
I haven’t tried to run a model that big on CPU RAM only, but running a Q4_0 gguf of Causal 14B was already mind numbingly slow on my rig.
General rule of thumb, always utilize as much of your VRAM (GPU RAM) as possible since CPU RAM is exponentially slower. I’m guessing your connection timed out because it just took to long to load/run.
With a 4090, you can actually run lzlv 70B fully on your 24GB VRAM. Let’s not let your amazing GPU go to waste! Try these steps and let me know if it works out for you:
Thanks for the detailed answer! Ubuntu does seem to be much more memory-efficient compared to Windows. However, the problem just fixed itself seemingly overnight. Now I’m not running into out of memory errors. 8-bit cache is a godsend for vram efficiency.