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If you want to run Mixtral 8x7B locally you can use llama.cpp (including with any of the supporting libraries/interfaces such as text-generation-webui) with htt
by rhdunn 3y ago
If you want to run Mixtral 8x7B locally you can use llama.cpp (including with any of the supporting libraries/interfaces such as text-generation-webui) with https://huggingface.co/TheBloke/Nous-Hermes-2-Mixtral-8x7B-SFT-GGUF https://huggingface.co/TheBloke/Nous-Hermes-2-Mixtral-8x7B-S....
The smallest quantized version (2bit) needs 20GB of RAM (which can be offloaded onto the VRAM of a decent 4090 GPU). The 4bit quantized versions are the largest models that can just about fit onto a 32GB system (29GB-31B). The 6bit (41GB) and 8bit (52GB) models need a 64GB system. You would need multiple GPUs with shared memory if you wanted to offload the higher precision models to VRAM.
I've experimented with the 7B and 13B models, but haven't experimented with these models yet, nor other larger models.
- jodleif 3y agoI prefer koboldcpp over llama.cpp. It’s easy to spilt between gpu/cpu on models larger than VRAM
- DrSiemer 3y agoRuns in Oobabooga textUi as well, if you add the llama.cpp extension. Easier interface imo, plus fun stuff like coqui and whisper integration.
- rhdunn 3y agoThat's interesting. It also looks like koboldcpp works better with long interactions, as it only processes changed tokens. I'm using llama.cpp with text-generation-webui and its OpenAI compatible API. I'll have to look to see if I can use koboldcpp with it.
- sp332 3y agoLlama.cpp has an interactive mode, but I don't think text-generation-webui uses it. https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md#interaction https://github.com/ggerganov/llama.cpp/blob/master/examples/...
- jodleif 3y agoIndeed. Koboldcpp works fine with other UIs than the bundled one.
- magicalhippo 3y agoI've got an aging 2080Ti and Ryzen 3800X with 96GB RAM, any point in trying to mess with the GPU or? Haven't really been able to justify upgrading to a 4090 or similar given I play so few new games these days.
- htsh 3y agoYes, offloading some layers to the GPU and VRAM should still help. And 11gb isn't bad. If you're on linux or wsl2, I would run oobabooga with --verbose. Load a GGUF, start with a small number of GPU layers and creep up, keeping an eye on VRAM usage. If you're on windows, you can try out LM Studio and fiddle with layers while you monitor VRAM usage, though windows may be doing some weird stuff sharing ram. Would be curious to see the diffs. Specifically if there's a complexity tax in offloading that makes the CPU-alone faster but in my experience with a 3060 and a mobile 3080, offloading what I can makes a big diff.
- macNchz 3y ago> Specifically if there's a complexity tax in offloading that makes the CPU-alone faster Anecdotal, but I played with a bunch of models recently on a machine with a 16GB AMD GPU and 64GB of system memory/12 core CPU. I found offloading to significantly speed things up when dealing with large models, but there was seemingly an inflection point as I tested models that approached the limits of the system, where offloading did seem to significantly slow things down vs just running on the CPU.
- baq 3y agoI had only cuda installed and it took 2 ollama shell commands in WSL2 from quite literally 0 local LLM experience to running mixtral fast enough on a 1070 and 12700k. Go for it.
- sp332 3y agoLlama.cpp has --n-gpu-layers that lets you set how much of the model to put on the GPU.
- attentive 3y agokobold bundles and runs llama.cpp. So it should be fairly the same with convenient defaults.
- DreamGen 3y agoWhen talking about memory requirements one also needs to mention the sequence length. In case of Mixtral, which supports 32000 tokens, this can be a significant chunk of the memory used.
- viraptor 3y agoAnd if you want better performance when talking about code, you can try the dolphin-mixtral fine tuning https://huggingface.co/TheBloke/dolphin-2.7-mixtral-8x7b-GGUF https://huggingface.co/TheBloke/dolphin-2.7-mixtral-8x7b-GGU...
- thriw63748 3y agoWhy not normal RAM? Ryzen 5600 with 128GB DDR4 is perfectly fine to run mixtral 8bit, and costs less than $1000. GPUs are only needed if you can not wait 5 minutes for an answer, or for training.
- rhdunn 3y agoThat was what I was referring to with the 32/64 GB systems.
- juliangoldsmith 3y agoNot sure why you're getting downvoted. It performs decent enough on my Ryzen 3600X with 64GB of RAM. It definitely wouldn't be usable for production or fine-tuning, but it's fine for experimenting.
- snowfield 3y agoOr if you want multiple sessions at the same time. Or if you want to do anything else with your machine while it's running. But realistically, 5 minutes is too long. It should be conversational, and for that you need at least 5 tokens per second. Which your Ryzen just can't do.
- MPSimmons 3y ago>It should be conversational, and for that you need at least 5 tokens per second. To be fair, a lot of people are using this for non-interactive work, like batching document analysis or offline processing of user generated content.
- Diti 3y agoThis particular thread we are commenting on is about Dolphin Mixtral, which is mostly used for offline code completion (à là Microsoft GitHub Copilot). You don’t want to have to wait 5 minutes at every keystroke to get code suggestions.
- Gracana 3y agoIn my experience, it takes some experimentation to figure out a good prompt. I don’t think I would have gotten very far off I had to wait that long for each result.
- tarruda 3y ago> You would need multiple GPUs with shared memory if you wanted to offload the higher precision models to VRAM. Or just a powerful apple silicon machine? I've tried dolphin mixtral 4bit on a 36gb ram MacBook m3, and inference is super fast.
- pennaMan 3y agoI can run 4bit on a beat up 1070 ti. GP talks about higher precision models
- mkesper 3y agoOr a Linux machine with a Ryzen using the internal GPU and the unified RAM (scroll down at llama.cpp and look for ROCm).
- chpatrick 3y ago`ollama run mixtral:8x7b-instruct-v0.1-q3_K_L` works fast on my 3090 locally
- beck5 3y agoDumb question, but how can a 32 bit number be converted to 2 bits and still be useful? It seems like magic.
- ttoinou 3y agoAll the 32 bits weren't necessarily used, and it's the whole network itself that has to be useful. It's a tradeoff. We started with very good precision to test the new method, now we can optimize some parts of it
- sp332 3y agoThe extra precision is more useful for training. Once the network is optimized, it's a statistical model and only needs enough precision to make good guesses. In fact, one of the big papers on this also pointed out that you can drop about 40% of the weights completely. I think people generally skip that part because sparse matrix operations are slower, so it doesn’t help here.
- viraptor 3y agoFor models with dropped weights, the keyword is "distilled". For example ssd-1b is a 50% size version of Stable Diffusion XL (https://huggingface.co/segmind/SSD-1B https://huggingface.co/segmind/SSD-1B)
- sp332 3y agoThat’s crazy, I’ve never seen one that dropped whole layers from a pre-trained model. I guess that avoids the sparse matrix math.
- beefield 3y agoI think of it with this kind of analogy: the original image is stored with 32 bit color scheme. You can reduce the color scheme to 16 bit accuracy and still figure out pretty well what the image is about. 2 bit is stretching this to a bit far, basically either pixel is white or it is black, but even if you lose lots of nuances in the image, in many images even that gives you some idea whats going on in the image.
- zaat 3y agoUsing TheBloke nous-hermes-2-mixtral-8x7b-dpo.Q5_K_M.gguf, llam.cpp with oobabooga, on Ryzen 9 7950x and RTX 2070 super, offloading 4 layers to the GPU. 27,631MiB system memory used. 7358/8192MiB usage on the GPU. I'm getting 6.32 tokens/second. You: You are Hunter S. Thompson, write a 450 words essay on the LHC project. The writing must be in Thompson's style AI: Well, buckle up, kiddos, because we're about to dive into the twisted world of particle physics and the Large Hadron Collider, or as I like to call it, the Doomsday Machine. This beast of a thing is buried deep beneath the Swiss countryside, a 17-mile-long ring of death and destruction that makes the atom bomb look like a goddamn firecracker. Now, let me tell you, when the Swiss government first proposed this monstrosity, I thought they'd gone completely batshit crazy. But then again, this is the land of Toblerone and Heidi, so maybe I shouldn't be too surprised. The LHC is essentially a giant, underground racetrack for subatomic particles, where they're accelerated to nearly the speed of light before being smashed into each other like a couple of drunken frat boys at a kegger. The goal of this madness? To recreate the conditions that existed just after the Big Bang, when the universe was nothing more than a seething cauldron of energy and chaos. And if that doesn't sound like a recipe for disaster, I don't know what does. But let's not forget the real reason we're all here: the Higgs boson, or as it's more commonly known, the God Particle. This little bugger is what gives other particles their mass, and without it, we'd all be floating around like a bunch of cosmic tumbleweeds. The LHC was designed to find this elusive particle, and in 2012, they finally did it. Sort of. You see, the Higgs boson they found was a bit of a disappointment. It was too light, too wimpy, and didn't behave quite the way they expected it to. So now, the physicists are back at the drawing board, trying to figure out what the hell went wrong. And in the meantime, the LHC continues to churn out more and more data, like some kind of unholy particle-smashing machine. But let's not forget the dark side of the LHC. There are those who believe that this thing could very well be the end of the world as we know it. They fear that the LHC could create a black hole, or even a miniature Big Bang, right here on Earth. And while the scientists assure us that these fears are unfounded, I can't help but wonder if they're just whistling past the graveyard. So, what have we learned from all this? Well, for one thing, we've discovered that the universe is a much stranger place than we ever imagined. We've also learned that even the most brilliant minds can be blinded by their own ambition, and that sometimes, the pursuit of knowledge can lead us down some very dark paths. In the end, the LHC is a testament to the insatiable curiosity of the human race, and our unquenchable thirst for knowledge. It's a monument to the power of science, and the potential consequences of playing God. And as we continue to push the boundaries of what's possible, we must always remember that with great power comes great responsibility. Or, as I like to say, "When the going gets weird, the weird turn pro."
- bearjaws 3y ago2bit is pretty damn terrible, I don't recommend it for anything serious.
- ignoramous 3y agoAt that level of quantization / distillation, smaller models like phi-2 (q&a) and wavecoder-6.7b (code-gen) might be preferable over QLoRAd ones: https://huggingface.co/microsoft/phi-2 https://huggingface.co/microsoft/phi-2 > 2bit is pretty damn terrible Wait till you go hybrid [0] or even 1bit [1] [0] https://github.com/efeslab/Atom https://github.com/efeslab/Atom [1] https://github.com/IST-DASLab/qmoe https://github.com/IST-DASLab/qmoe
- EVa5I7bHFq9mnYK 3y agoFaraday.dev has it in its selection of models now. Good for us clueless Windows folks. Runs decently fast with 16gb mobile 3080 gpu. Results seem better than any other free option.
- MuffinFlavored 3y agoWhat differences would I measurably notice running the 2-bit version vs the 4-bit version vs the 6-bit vs the 8-bit?