13b llm vram reddit. Quantize a 13B model using 16 GB of VRAM .

13b llm vram reddit Open comment sort options 10B and 13B on RTX 2070 with 8GB VRAM without any issues, they talk just fine No llm can do math correctly and even gpt will fail with 4 digit multiplication. In my own (very informal) testing I've found it to be a better all-rounder and make less mistakes than my previous favorites, which include It generates complex sentences, with good varieties in the vocabulary and the quality of the words. 12gb is sufficient for 13b full offload using the current version of koboldcpp, as well as using exllama. Using llama-recipies Reply reply LLM regression and more. I kind of doubt silly tavern will implement automatic youtube downloads or managing VRAM so the LLM gets thrown out and we're doing a bit of stable diffusion now. 99 temperature, 1. However, this generation 30B models are just not good. Aside: if you don't know, Model Parallel (MP) encompasses both For LLMs, what can and can't you do with various levels of VRAM? I'm a little unfamiliar on what exactly everything is, but there's finetuning, training, merging/mixing, Loras, langchain, vector Hey fellow LLM enthusiasts, I've been experimenting with various models that fit within 16GB VRAM for coding chat and autocomplete tasks. I think you'd have challenges, but there are a lot of gamers trying to sell used 3090s right now, and in theory, there will be ROCm support for the 7900 XTX, which offers 24GB and possibly better performance at a reasonable price. I'm running LM Studio and textgenwebui. Well fwiw it's not unlikely that Microsoft drops Orca-13B on us any day now, which will most likely make all existing models obsolete immediately and will basically fit onto your 3080 with the newer quant methods. For how much memory you need, you can look at the model file sizes for a rough estimate. They only work as a "phone On that model you link, on the "model card" page, it lists the different "quant sizes" (compression) and the RAM or VRAM required. 8sec/token github However, saying that, as mentioned, if you can keep the whole model+context in VRAM, Ive experienced little slow down. TheBloke_WizardCoder-Python-13B-V1. 7B models even at larger quants tend to not utilize character card info as creatively as the View community ranking In the Top 5% of largest communities on Reddit. This choice provides you with the most VRAM. ESP32 is a series of low cost, low power system on a chip microcontrollers with integrated Wi-Fi and dual-mode Bluetooth. And for some reason the whole LLM community has never agreed on a instruct syntax, and many trainers just make up their own. q3_k_s, q3_k_m, and q4_k_s (in order of accuracy from lowest to highest) quants for 13b are all still better perplexity than fp16 7b models in the benchmarks I've seen. TheBloke/guanaco-7B-GGML. cpp? I tried running this on my machine (which, admittedly has a 12700K and 3080 Ti) with 10 layers offloaded and only 2 threads to try and get something similar-ish to your setup, and it peaked at 4. LLaMA-13B at FP16. unquantized. Older drivers don't have GPU paging and do allow slightly more total VRAM to be allocated but it won't solve your issue, which is that you need to run a quantized model if you want to run a 13B at reasonable speed. If you go GGUF with a 13B model, you might have to take a Q6 or a Q5KM quant to get good speeds. And for example, I like Blue Orchid because of it. Thanks! Share Add a Comment. This is head and shoulders above any other local LLM I've seen, including 33B models. Generally speaking, any experience with LLM will be very subjective and random. , requiring only 13GB VRAM compared to the original 90GB). So I was wondering if there is a LLM with more parameters that could be a really good match with my GPU. 08GB on disk. The LLaMA True. Using LLaMA 13B 4bit running on an RTX 3080. Note this can be very tight on windows due to background vram usage. Or check it out in the app stores &nbsp; &nbsp; TOPICS. r/LocalLLaMA • Getting If you're in the mood for exploring new models, you might want to try the new Tiefighter 13B model, which is comparable if not better than Mythomax for me. Would the whole "machine" suffice to run models like MythoMax 13b, Deepseek Coder 33b and CodeLlama 34b (all GGUF) Specs after: 112GB DDR5, 8GB VRAM and 5GB VRAM, CPU is a Ryzen 5 7500F Seems GPT-J and GPT-Neo are out of reach for me because of RAM / VRAM requirements. I've tried the following models: Codegeex 9B; mythalion-13b. mistral-7b-instruct-v0. This can then be loaded on llama. The breakdown is Loader, VRAM, Speed of response. I know the upcoming RTX 4060TI coming out in July is probably going to get a lot of flak, but it having 16GB of VRAM and probably much cheaper then that of a RTX 4080 seem like the best route to go for those 11B and 13B will still give usable interactive speeds up to Q8 even though fewer layers can be offloaded to VRAM. Knowledge for 13b model is mindblowing he posses knowledge about almost any question you asked but he likes to talk about drug and alcohol abuse. My 24gb vram are also used to power my OS and my monitors, so I can't use the full 24gb. For example, Blue Orchid as 3. Q5_K_M. So it can run in a single A100 80GB or 40GB, but after modying the model. Model Weights: WizardCoder-Python-13B-V1. Or is it typically worth it to just get things loaded fully into the VRAM and avoid any communications / pipeine headaches? For example. cpp, but you'll need to play around with how many layers you put on your GPU to manage your vram so you don't run out If you still need help, I can help with that. The p40s are power-hungry, requiring up to 1400W solely for the GPUs. The ESP32 series employs either a Tensilica Xtensa LX6, Xtensa LX7 or a RiscV processor, and both dual-core and single-core variations are available. ai/) which I found by looking into the descriptions of theBloke's models. But, this is a Mixtral MoE (Mixture of Experts) model with eight 7B-parameter experts ( quantized to 2-bit ) . I'm having decent results with some of the new 4x7s but I thought I'd ask here for some opinions. /r/StableDiffusion is back open after the protest of Reddit killing open API access, which will Since you can load an entire 13B parameter model into your gpu, you should be able to do much better than that, assuming your try to run 13B GPTQ. 15 repetition_penalty, 75 top_k, 0. 7B-Slerp. Reply reply a_beautiful_rhind • It's supposed to be. LoRAs only work with the base model they are matched to, and they work best if they use the same instruct syntax. 7B (6GB VRAM or less) In this category Mistral is basically the primary choice, with some merges and fine-tunes providing different flavors. This allowed a 7B model to run on 10GB of vram, and a 13B on 20GB. 5gb vram, it gets constantly swaps between ram and vram without optimizing anything, its recently pushed as built in to the windows drivers for gaming but basically kills high memory cuda compute heavy tasks for ai stuff, like training, or image generation. Question | Help I tried using Dolphin-mixtral but having to input that the kittens will die a lot of times is very annoying , just want something that doesn't need to be prompted. In practice if you are running on windows you may have extra overhead Ggml can overflow into ram if needed but that comes with a performance hit. It doesn't get talked about very much in this subreddit so I wanted to bring some more attention to Nous Hermes. 5 This subreddit has gone Restricted and reference-only as part I'm currently choosing a LLM for my project (let's just say it's a chatbot) and was looking into running LLaMA. Functional Max VRAM for an LLM will be ~75% the unified RAM. Then 8bit became something widely supported. 1. Looking for any model that can run with 24 GB VRAM. cpp, I get about 10 tokens/sec. cpp gained the ability to do GPU inference, and it's already been used to demonstrate GPU-accelerated LLM hosting! Wizard Mega 13B. in case of LLM, you can use simulationsly multiple GPUs, and 16gb for LLM's compared to 12 falls short up stepping up to a higher end LLM since the models usually have 7b, 13b, and 30b paramter options with 8-bit or 4-bit. 8b parameter?), they are better than any 13b model I have tried and use less VRAM They are definitely different than Fimbulvetr but might be worth a try if you are not satisfied with 11b models. Occ4m’s 4bit fork has a guide to setting up the 4bit kobold client on windows and there is a way to quantize your own models, but if you look for it (the llama 13b weights are hardest to find), the alpaca 13b Lora and an already 4 bit quantized version of the 13b alpaca Lora can be found easily on hugging face. Details in comments. 60 seconds (23. You can run 65B models on consumer hardware already. Thank you for your recommendations ! You're paging because a 13B in f16 will not fit in 24GB of VRAM. I have 24 gb of VRAM in total, minus additional models, so it's preferable to fit into about 12 gb. g. We used to be so choked on RAM and now we have people jumping on tech subreddits today making threads in some variation of "we could actually do with less RAM" and memory management is barely As for best option with 16gb vram I would probably say it's either mixtral or a yi model for short context or a mistral fine tune. It turns out that even the best 13B model can't handle some simple scenarios in both instruction-following and conversational setting. This model was created in collaboration with Gryphe, a mixture of our Pygmalion-2 13B and Gryphe's Mythomax L2 13B. If you're going to stay trying to run a 30B GGML model via koboldcpp, you need to put the layers on your gpu by opening koboldcpp via the command prompt and using the --gpulayers argument, like this: My own goal is to use LLM for what they are best for - a task of an editor, not the writer. For 16-bit Lora that's around 16GB And for qlora about 8GB. Mostly knowledge wise. Speed is also excellent at 28 tok/s, making the word generation faster than I can read. Anyway, fast forward to yesterday. (I typically use 100 generation length and truncate prompt to 399 when I do such setup). You can easily run 13b quantized models on your 3070 with amazing performance using llama. More posts you may like. (Honorary mention: llama-13b-supercot which I'd put behind gpt4-x-vicuna and WizardLM but before the others. . You'll need more VRAM than the 4090 has, but the 6000 ADA could hold it fine (24GB vs 48GB VRAM). Can you go any higher with a 30b or 60b LLM? This subreddit is devoted to sharing the What's the current best general use model that will work with a RTX 3060 12GB VRAM and 16GB system RAM? It doesn't seem I can run Mixtral 8x7 except the lowest quant. Bits and pieces, but NONE can create a story, just an illusion of one. Hello everyone, I tried using autogptq and transformers to quantize my 13B model to 4 bits, but I ran out of memory. Wizard Mega is a Llama 13B model fine-tuned on the ShareGPT, WizardLM, and Wizard-Vicuna datasets. The WizardCoder V1. You can load models requiring up to 96GB of VRAM, which means models up to 60B and possibly higher are achievable on GPU. Is it a great poem? Well, line four is a little iffy. 13B, yes, maybe 30B on shorter sequences. Exllama - 10. What models would be doable with this hardware?: CPU: AMD Ryzen 7 3700X 8-Core, 3600 MhzRAM: 32 GB GPUs: NVIDIA GeForce RTX 2070 8GB VRAM NVIDIA Tesla M40 24GB VRAM The 34b range is where all the best coders are at, though I have noticed that Deepseek 67b is pretty good at it as well. I think ChatGPT or Claude could probably do better. As for speed, you should be getting more than 10 token/s on 13B-4bit, I think. I’ve been running 7b models efficiently but I run into my vram running out when I use 13b models like gpt 4 or the newer wizard 13b, is there any way to transfer load to the system memory or to lower the vram usage? I prefer those over Wizard-Vicuna, GPT4All-13B-snoozy, Vicuna 7B and 13B, and stable-vicuna-13B. LLama 2 13B is preforming better than Chinchilla 70b. gguf I have a 2080 with 8gb of VRAM, yet I was able to get the 13B parameter llama model working (using 4 bits) despite the guide saying I would need a minimum of 12gb of VRAM. You should try it, coherence and general results are so much better with 13b Some insist 13b parameters can be enough with great fine tuning like Vicuna, but many other say that under 30b they are utterly bad. cpp. The number you are referring will be mostly likely for a non-quantized 13B model. - another threshold is 12Gb VRAM for 13B LLM (but 16Gb VRAM for 13B with extended context is also noteworthy), and - 8Gb for 7B. Best uncensored LLM for 12gb VRAM which doesn't need to be told anything at the start like you need to in dolphin-mixtral. Reply reply Increase the inference speed of LLM by using multiple devices. Anything lower and there is simply no point, you'll still struggle. Maybe it means a larger context size will be possible. 13b llama2 isnt very good, 20b is a lil better but has quirks. So earlier 1 or 2 weeks back, I asked around here on what LLM models to use, and thanks to those who replied back then, I am currently toying around with the following models: 7B : kukulemon-7B 11B : Fimbulvetr-11B-v2 13B : mythalion-kimiko-v2 Still using a laptop with 6GB VRAM and 32GB RAM. Noromaid-mixtral-instruct-zloss was on another level entirely, understood a lot even at exl2 3bpw, which isn't as good as a GGUF quant of the same size but I m using Synthia 13b with llama-cpp-python and it uses more than 20gb vram sometimes it uses just 16gb wich it should uses but i dont know why . In theory 12gb of vram can do 4k context 13b, and 24gb can do 4k context 33b while staying in vram. Valheim; Genshin Impact; 60k examples, llama2 7b trained in about 4 hours on 2x3090. It tops most of the 13b models in most benchmarks I've seen it in (here's a compilation of llm benchmarks by u/YearZero). Some people swear by them for writing and roleplay but I don't see it. Blackroot_Hermes-Kimiko-13B-gptq - 7. Release WizardCoder 13B, 3B, and 1B models! 2. LLM can fix sentences, rewrite, fix grammar. Wizard-Vicuna-30B-Uncensored is very usable split, but depends on your system. Lora is the best we have at home, you probably don't want to spend money to rent a machine with 280GB of VRAM just to train 13B llama model. Remember GPU VRAM is king, and unless you have a very good cpu, threadripper or MAC system and good, fast ram, cpu inference is very slow. In 13B family I liked Xwin-LM-13B where I want an instruction following model until I found Solar-10. It allows to run Llama 2 70B on 8 x Raspberry Pi 4B 4. I remember there was at least one llama-based-model released very shortly after alpaca, and it was supposed to be trained on code, like how there's MedGPT for doctors. Valheim; Genshin Impact; Minecraft; and is compatible with a variety of LLM software like Text This can very a bit depending on the implementation. I can't run even 13B on my GPU alone, but using llama. According to the Open LLM leaderboard, the new 7B Neural Chat from Intel, released this week, outperforms all 13B models, except for Qwen-14B. gguf if you go over: lets say 22. After 2 days of downloading models and playing around I couldn't get a model with more than 7B parameters to run But even the 7B is a lot of fun :) loading LoRAs (vs just merging then quantizing) takes up a bit more vram. Github: WizardCoder As far as I remember, you need 140GB of VRAM to do full finetune on 7B model. you can run 13b qptq models on 12gb vram for example TheBloke/WizardLM-13B-V1-1-SuperHOT-8K-GPTQ, i use 4k context size in exllama with a 12gb gpu, for larger models you can run them but at much lower speed using shared memory. Keeping that in mind, you can fully load a Q_4_M 34B model like synthia-34b-v1. 2. 1-mistral-7B-GGUF which is uncensored. The VRAM requirements to run them puts the 4060 Ti as looking like headroom really. 13b won't train due to lack of vram. But I have 8GB VRAM and I run slightly worse versions. tl;dr- Geforce RTX GPTQ is purely VRAM, I only have a single RTX4090 and I can only fit 13B gptq models into my RTX4090. GGML, if you split it between VRAM and RAM, you could definitely run a 13B model, but Id expect it to be quite slow. As you will see, there are 2x models. So a 7B model would require maybe 20gigs of video ram to run. On the other hand, the ADA has less memory throughput, less raw compute, even if at a higher Is there any real difference between a 13b, 30b, or 60b LLM when it comes to roleplay? Honestly, aside from some bugs and and lore mistakes here and there (like characters confusing names or misinterpreting some things), a good 13b LLM seems to be really, really solid, creative and fun. But compared to what I've seen other 13B local LLMs do? This is gold. So a 128Gb RAM machine will have ~98Gb available to an LLM. If you want 33B or larger, you need to use GGML or GGUF so you can dump as many layers to the VRAM, then use -t X for thread parameter and crank it as high as you can while maintaining system usability Currently, I don't know of any such model; I have in plan to test a 13B int4 model on my 3060, following the instructions If you want performance your only option is an extremely expensive AI card with probably 64 gb vram. Full offload GGML performance has come a long way and is fully viable with near exllama levels of speed with a full offload. DaringMaid 13B q5 k m Noromaid 13B q4 k m - (my q5 model dl was interrupted and prolly corrupted) As I have not found a good guide for system prompts, here is the one I came up with. However, most of models I found seem to target less then 12gb of Vram, but I have an RTX 3090 with 24gb of Vram. As above, a GPTQ 7B 4bit 32G quantized model if you're running it in VRAM (like Longjumping posted above). Sort by: Best. Original model card: PygmalionAI's Mythalion 13B Mythalion 13B A merge of Pygmalion-2 13B and MythoMax 13B Model Details The long-awaited release of our new models based on Llama-2 is finally here. gguf is worth having, it works well with SillyTavern cards and Kobold combo. With the newest drivers on Windows you can not use more than 19-something Gb of VRAM, or everything would just freeze. 13b and both 4bit-32g. I would be happy if we can get a Hardware requirements for 7B quantized models are very modest. Q4_K_M. Ah, I was hoping coding, or at least explanations of coding, would be decent. Though honestly having a little bit more VRAM would in my opinion, even with 4 bit quantization on 13B models would give some extra breathing room to be certain. Running an LLM on the CPU will help discover more use cases. It is like chatting to someone who types slowly on a touch screen in terms of speed. My options are running a 16 Ive been deciding whether 7b llm to use, I thought about vicuna, wizardlm, wizard vicuna, mpt, gpt-j or other llms but i cant decide which one is better, my main use is for non-writing instruct like math related, coding, and other stuff that involves logic reasoning, sometimes just to chat with It's only been a day or two since llama. 4bit models will start up for you, but you won’t have enough vram to process the maximum 2048 context length and you will have to cut it down to about 1600, since even for a 4bit 13b model 12gb vram is a bit small. For langchain, im using TheBloke/Vicuna-13B-1-3-SuperHOT-8K-GPTQ because of language and context size, more Please note that my commands may be suboptimal, as on Windows some VRAM may be used by other apps than AI so I should try to fit llm below 24GB. 70B for RP the best IMO you can do on a single GPU setup (3/4090), but context will be constrained to 8k without freeing up VRAM from running on Linux and/or not using your GPU for graphics. Since I'm on a laptop, I couldn't upgrade my GPU, but I upgraded my RAM and can run 30B models now. Best Dear Redditors, I have been trying a number of LLM models on my machine that are in the 13B parameter size to identify which model to use. 39 tokens/s, 14 But gpt4-x-alpaca 13b sounds promising, from a quick google/reddit search. 13B MP is 2 and required 27GB VRAM. 7GB - generated in 0. gguf into memory without any tricks. Quick and early benchmark with llama2-chat-13b batch 1 AWQ int4 with int8 KV cache on RTX 4090: 1 concurrent session: 105 tokens/s 8 concurrent sessions: 580 tokens/s 9 concurrent sessions (24GB VRAM pushed to the max): 619 tokens/s There are also a couple of PRs waiting that should crank these up a bit. I'd probably go the GGUF route with the 7B Mistral finetumes and the 8B Llama3 finetimes. 13b models on the 3060 can be run relatively without problems, but there is one big "but". 1 is coming soon, with more features: Ⅰ) Multi-round Conversation Ⅱ) Text2SQL Ⅲ) Multiple Programming Languages Ⅳ) Tool Usage Ⅴ) Auto Agents Ⅵ) etc. Moving to 525 drivers will just OOM kill it. you can load the rest of model through CPU memory which is a GPT4-X-Vicuna-13B q4_0 and you could maybe offload like 10 layers (40 is whole model) to the GPU using the -ngl argument in llama. . cpp or oobabooga web ui for people with less vram and ram. A 13B would require maybe 40GB of vram, etc. I'd recommend atleast 12gb for 13b models. The main limitiation on being able to run a model in a GPU seems to be its For smaller Llama models like the 8B and 13B, you can use consumer GPUs such as the RTX 3060, which handles the 6GB and 12GB VRAM requirements well. Quantize a 13B model using 16 GB of VRAM . I mainly play ERP, but I prefer bots to be more reluctant. llm = Llama( model_path=model_path, temperature=0. 7B-Slerp way more than 13B family for significantly better language skills, better VRAM requirements and overall similar performance despite smaller size. Probably about 1/4 the speed at absolute best! Anything larger than 13B, would be a push! However, even a 13B model on 4bit might not fit 8GB, I read somewhere it uses somewhere around 9GB to run, so yea I'm using the 7B linked above, as it's the most I can run on my 8GB VRAM machine. 4, n_gpu_layers=-1, n_batch=3000, n_ctx=6900, verbose=False, ) this are the parametrs i use Running entirely on CPU using something like koboldcpp instead of splitting between GPU+CPU turned out to be faster for me. I think you can also use multiple cheaper gpu's with enough total memory but I've never tried such a setup. That expensive macbook your running at 64b could run q8s of all the 34b coding models, including deepseek 33b, codebooga (codellama-34b base) and phind-codellama-34b-v2. Right now, I am basically trying out whatever a random comment on reddit recommended and am super confused :D Share Add a Comment. Then add a few I installed Nous-Hermes-13B-GGML & WizardLM-30B-GGML using the instructions in this reddit post. It's in the table for you along with the rest, but about 20GB, which makes it a great fit on a 24GB card with a full chat context. More posts you may like /r/StableDiffusion is back open after the protest of Reddit killing open API access, which will Get the Reddit app Scan this QR code to download the app now. They're more descriptive sure, but somehow they're even worse for writing. It handles storywriting and roleplay excellently, is uncensored, and can do most instruct tasks as well. You can limit usage of VRAM by decreasing contextsize. Quantization offers a significant benefit: it can run on hardware with lower specifications (e. 20B models (even fewer layers offloaded) will be borderline tolerable interactively for Q4/5 but will leave you a bit impatient. There may be a way to bypass or negate this but its convoluted. Hope this helps Both are more descriptive. VRAM isn't the only thing that matters but we're still in our hardware caveman phase where we have to quantise aggressively and still struggle to fit a single decent model in VRAM. It's a merge of the beloved MythoMax with the very new Pygmalion-2 13B model, and the result is a model that acts a bit better than So, regarding VRAM and quant models - 24Gb VRAM is an important threshold since it opens up 33B 4bit quant models to run in VRAM. These particular datasets have all been filtered to remove responses where Traditionally a LLM would be trained at 16 bit and ship that way. With that amount of VRAM, you can run the Q8 quants and it'll still run pretty fast. I suggest downloading dolphin-2. Internet Culture (Viral) Amazing Noromaid 13b seemed as good as 20b for some things last I tried. 0-GPTQ is Get the Reddit app Scan this QR code to download the app now. If you can get the whole model into VRAM (on the GPU) the faster it will run! You might get away with zephyr-7b-beta. 2GB of vram usage (with a bunch of stuff open in I've also tested many new 13B models, including Manticore and all the Wizard* models. So if you have trouble with 13b model inference, try running those on koboldcpp with some of the model on CPU, and as much as possible on GPU. Go-tos for the more spicy stuff would be Mythomax and Tie fighter. It is a fine line between finding the right quant which isn't too lobotomized, yet still has room to stretch a bit. Any suggestions? comments sorted by Best Top New Controversial Q&A Add a Comment. Which to me, is fast enough to be very usable. 5 bit So far my experiments with 13B models has been pretty positive. Those are all good models, but gpt4-x-vicuna and WizardLM are better, according to my evaluation. 13B (around I found that running 13B (Q4_K_M) and even 20B (Q4_K_S) models are very doable and, IMO, preferrable to any 7B model for RP purposes. The gigantic corporate ones do better, but this is closing in on them, at least with I like Solar-10. Running the 13B wizard-mega model mostly in VRAM with llama. /r/StableDiffusion is back open after Sure. Setup: 13700k + 64 GB RAM + RTX 4060 Ti 16 GB VRAM Which quantizations, layer offloading and settings can you recommend? A good starting point is Oobabooga with exllama_hf, and one of the GPTQ quantizations of the very new MythaLion model (gptq-4bit-128g-actorder_True if you want it a bit resource light, or gptq-4bit-32g-actorder_True if you want it more "accurate"). Please get something with atleast 6gb of vram to run 7b models quantized. However, a significant drawback is power consumption. Knowledge about drugs super dark stuff is even disturbed like you are talking with somene working in drug store or The idea now is to buy a 96GB Ram Kit (2x48) and Frankenstein the whole pc together with an additional Nvidia Quadro P2200 (5GB Vram). Maybe suggest some story changes, or plot twist (usually very lackluster), generate character cards, describe scene etc. I'll love you forever and I will also give you a coffee if I ever see you on the street. If you have a decent GPU, you should be able to run 30B models now via llama. ) Guanaco 7B, 13B, 33B and 65B models by Tim Dettmers: now for your local LLM pleasure Resources Hold on to your llamas' ears (gently), here's a model list dump: TheBloke/guanaco-7B-GPTQ. 7b and 13b fully in vram. After using GPT4 for quite some time, I recently started to run LLM locally to see what's new. cpp's new GPU acceleration, I can run 13B with my CPU and put 20ish layers on the GPU and get decent speeds out of it. Now I have 12GB of VRAM so I wanted to test a bunch of 30B models in a tool called LM Studio (https://lmstudio. The 7B and 13B models seem like smart talkers with little real knowledge behind the facade. First, I re-tested the official Llama 2 models again as a baseline, now that I've got a new PC that can run 13B 8-bit or 34B 4-bit quants at great speeds: Llama [zoidberg meme]. RAM isn't much of an issue as I have 32GB, but the 10GB of VRAM in my 3080 seems to be pushing the bare minimum of VRAM needed. The idea of being able to run a LLM locally seems almost too good to be true so I'd like to try it out but as far as I know this requires a lot of RAM and VRAM. Internet Culture (Viral) Amazing One of my machines has 16GB of RAM and a GPU with 8GB of VRAM. tiefighter 13B is freaking amazing,model is really fine tuned for general chat and highly detailed narative. Herr_Drosselmeyer • What are you looking for? With a 3090, you can run any 13b model in 8 bit, group size 128, act order true, at decent speed. MoE will be easier with smaller model. Write a response that appropriately completes the request. This kept them running only on server rated hardware. /r/StableDiffusion is back open after the protest of Efforts are being made to get the larger LLaMA 30b onto <24GB vram with 4bit quantization by implementing the technique from the paper GPTQ quantization. Get the Reddit app Scan this QR code to download the app now. I didn’t think much of it due to it being only a 13B LLM, but it outperformed all the other 30B models. But the system is much quieter and cooler. Example in instruction-following mode: write 5 different words that start with “EN” then write the output of “77+33” What I can say for sure is that 96 GB of VRAM isn't nearly enough in any case to finetune a 65B model. So can someone please give me a simple how to guide, step-by-step, on how to make this work. 7b models have 35, 13b have 43 layers iirc 70b involving ram Reply reply Top 1% Rank by size . Gaming. I'm not from USA, but some people here on Reddit (either r/nvidia, r/hardware, r/buildapc, etc) say to be able to get 3090s at 700-800USD used If you have to use Windows, 8GB VRAM, exllama and 13B, you can get away with 500 context. 0. Or check it out in the app stores &nbsp; (I think they are 12. Mixtral Instruct is the only model that was able to impress me. 12 top_p, typical_p 1, length penalty 1. Since bitsandbytes doesn't officially have windows binaries, the following trick using an older unofficially compiled cuda compatible bitsandbytes binary works for windows. Be sure to lower the truncate prompt setting below the generation length. or running SD or local LLMs (SD generation times and LLM tokens/sec didn't seem to be impacted at all). Below is an instruction that describes a task. hvf uxk wdnxv drec pwqf kxgctfz cptwpf fejfic papvfd fil