Fine tune llama 3090 reddit You don't necessarily have to use the same model, you could ask various Llama 2 based models for questions and answers if you're fine-tuning a Llama 2 based model. I am using qlora (brings down to 7gb of gpu memory) and using ntk to bring up context length to 8k. Quantization technology has not significantly evolved since then either, you could probably run a two-bit quant of a 70b in vram using EXL2 with speeds upwards of 10 tk/s, but that's In this subreddit: we roll our eyes and snicker at minimum system requirements. I just found this PR last night, but so far I've tried the mistral-7b and the codellama-34b. true. I can fine tune model by MLX and run inference on llama. Looking for suggestion on hardware if my goal is to do inferences of 30b models and larger. A full fine tune on a 70B requires serious resources, rule of thumb is 12x full weights of the base model. The base model is so good but until it's fine tuned properly midnight Miqu is still significantly better at RP at least. cpp has so many dedicated conversion scripts. Now, we need to calculate how many A100 GPUs are required to fine-tune LLaMA-7B to a 32k context. cpp. For training: would the P40 slow down the 3090 to its speed if the tasks are split evenly between the cards since it would be the weakest link? I'd like to be able to fine-tune 65b locally. Well this is a prompting issue not fine tuning. com/unslothai/unsloth. Playing with text gen ui and ollama for local inference. It is possible to fine-tune (meaning LoRA or QLoRA methods) even a non quantized model on a RTX 3090 or 4090, up to 34B models. I asked BingGPT if this entire Reddit post including comments said ANYTHING specific about what the fine-tunings of Llama 7B consists of, and it said no this whole thread is shit: "No, it doesn’t say anything about what specifically those fine-tunings consist of. Then instruction-tune the model to generate stories. Fine tuning is a different story, right now most of the tutorials assume 16GB or more of vram. We would like to show you a description here but the site won’t allow us. Llama-3 70b is 1. I had to get creative with the mounting and assembly, but it works perfectly. 7gb model with llama. This is a community for anyone struggling to find something to play for that older system, or sharing or seeking tips for how to run that shiny new game on yesterday's hardware. It is not about money, but still I cannot afford a100 80GB for this hobby. gguf model. While LLaMa now works with Apple's Metal, for instance, I feel like it's more of a port, and for complete control over LLMs as well as the ability to fine-tune models, using a Linux PC with an Nvidia GPU seems like the best approach. Tried lora and adapters and with my dataset 16bit went NaN pretty quickly. I know you can do main memory offloading, but I want to be able to run a different model on CPU at the same time and my motherboard is maxed out at 64gb. Before you needed 2x GPUs. I have a 3090 in an EGPU to I'm also working on the finetuning of models for Q&A and I've finetuned llama-7b, falcon-40b, and oasst-pythia-12b using HuggingFace's SFT, H2OGPT's finetuning script and lit-gpt. This means it can train models too large to fit onto a single GPU. 5K that trains 50% faster using 30% less memory, inferences faster, and has support for all the software you'd want to use (or go for a $8K A6000 Ada that trains over 3X faster at the same power budget). Subreddit to discuss about Llama, the large language model created by Meta AI. There's a lot more details in the README. The accuracy of Llama 3 roughly matches that of Mixtral 8x7B and Mixtral 8x22B. Any cards pre ampere don't support bfloat16 which was a nuisance to figure out. I do think a creative writing fine tune with no guardrails would do really well. Also I had to run 5 epochs instead of 3 to achieve similar results as performing qlora fine-tune of llama-33b. PS: Now I have an RTX A5000 and an RTX 3060. For training, fine-tune, will the difference be bigger? My use case for now is mostly inference, should I buy rtx3090 or rtx4090 for my 3rd card? Or if there is something i do wrongly which cause this similar in speed then can let me know. This post also conveniently leaves out the fact that CPU and hybrid CPU/GPU inference exists, which can run Llama-2-70B much cheaper then even the affordable 2x TESLA P40 option above. There was a recent paper where some team fine tuned a t5, RoBERTa, and Llama 2 7b for a specific task and found that RoBERTA and t5 were both better after fine tuning. The llama 2 base model is essentially a text completion model, because it lacks instruction training. I'd like at least 8k context length, and currently have a RTX 3090 24GB. 25bpw while I can run midnight at 4. Training is compute bound, while inference is memory bandwidth bound, however the A100 should have 2x the memory bandwidth of a 4090. openllama is a reproduction of llama, which is a foundational model. You CAN fine-tune a model with your own documents, but you don't really need to do that. For my use case 48gb of vram doesnt seem to be enough to fine tune mistral 7b so I've just ended up using cloud gpus instead. , i. Is it worth the extra 280$? Using gentoo linux. I tested Unsloth for Llama-3 70b and 8b, and we found our open source package allows QLoRA finetuning of Llama-3 8b to be 2x faster than HF + Flash Attention 2 and uses 63% less VRAM. 3090 spot is around 23 cents per hour on vastAI I don't recommend interruptable on vastAI, it actually gets interrupted as it works on bids. 99 per hour. Google released a blog post a few days ago, but I'm still having hard time implementing it using their approach with Keras. i tried to fine tune 3. Running on a 3090 and this model hammers hardware, eating up nearly the entire 24GB VRAM & 32GB System RAM, while pushing my 3090 to 90%+ utilisation alongside pushing my 5800X CPU to 60%+ so beware! With the recent updates with rocm and llama. LLaMA is quantized to 4-bit with GPT-Q, which is a post-training quantization technique that (AFAIK) does not lend itself to supporting fine-tuning - the technique is all about finding the best discrete approximation for a floating point model after Most people here don't need RTX 4090s. cpp and that 15GB ram plus whatever layers you can fit on the GPU. To uncensor a model you’d have to fine tune or retrain it, which at that point it’d be considered a different model. GPU models with this kind of VRAM get prohibitively expensive if you're wanting to experiment with these models locally. to adapt models to personal text corpuses. My goal with this was to better understand how the process of fine-tuning worked, so I wasn't as concerned with the outcome. I imagine some of you have done QLoRA finetunes on an RTX 3090, or perhaps on a pair for them. There’s pros and cons to both. I will have a second 3090 shortly, and I'm currently happy with the results of Yi34b, Mixtral, and some model merges at Q4_K_M and Q5_K_M, however I'd like to fine-tune them to be a little more focused on a specific franchise for roleplaying. I am thinking of: First finetune QLora on next token prediction only. But the 3090 still is going to do fine for gaming, too, so not like you're going to have a poor gaming performance with it or anything. Fine tuning too if possible. It is based around Deepspeed's pipeline parallelism. Put as many cheap memories as possible. 3090 is a good cost effective option, if you want to fine tune or train models yourself (not big LLMs of course) then a 4090 will make a difference. If you go Apple, you can run 65b llama with 5 t/s using llama. I use a single A100 to train 70B QLoRAs. 5bpw. However most people use 13b-33b (33b already getting slow on commercial hardware) and 70b requires more than just one 3090 or else it's a molasses town. Since I’m on a Windows machine, I use bitsandbytes-windows which currently only supports 8bit quantisation. 12x instance which has 4*24gb A10GPUs, and 192gb ram. With dual 4090 you are limited with the PCIe 4. Here's the axolotl config file: base_model: meta-llama/Llama-2-70b-hf base_model_config: meta-llama/Llama-2-70b-hf model_type: LlamaForCausalLM I did a fine tune using your notebook on llama 3 8b and I thought it was successful in that the inferences ran well and I got ggufs out, but when I load them into ollama it just outputs gibberish, I'm a noob to fine tuning wondering what I'm doing wrong After many failed attempts (probably all self-inflicted), I successfully fine-tuned a local LLAMA 2 model on a custom 18k Q&A structured dataset using QLoRa and LoRa and got good results. The goal is a reasonable configuration for running LLMs, like a quantized 70B llama2, or multiple smaller models in a crude Mixture of Experts layout. cpp repo, here are some tips: use --prompt-cache for summarization This is a training script I made so that I can fine tune LLMs on my own workstation with 4 4090s. So if training/fine-tuning on multiple GPUs involves huge amount of data transferring between them, two 3090 with NVLink will most probably outperform dual 4090. 3090: 106 Now to test training I used them both to finetune llama 2 using a small dataset for 1 epoch, Qlora at 4bit precision. Both trained fine and were obvious improvements over just 2 layers. If you go dual 4090, you can run it with 16 t/s using exllama. (Dual 3090 shouldn't be much slower. With the 3090 you will be able to fine-tune (using LoRA method) LLaMA 7B and LLaMA 13B models (and probably LLaMA 33B soon, but quantized to 4 bits). 5 model on a setup with 2 x 3090? Other specs: I9 13900k, 192 GB RAM. Using the latest llama. I've only assumed 32k is viable because llama-2 has double the context of llama-1 Tips: If your new to the llama. Fine-tuning Technique: Choose a fine-tuning technique: Supervised Fine-tuning (SFT): Train the model on your dataset using labeled examples where the desired outputs are However, I'm a bit unclear as to requirements (and current capabilities) for fine tuning, embedding, training, etc. Llama-2 7b and possibly Mistral 7b can finetune in under 8GB of VRAM, maybe even 6GB if you reduce the batch size to 1 on sequence lengths of 2048. You can squeeze in up to around 2400 ctx when training yi-34B-200k with unsloth and something like 1400 with axolotl. That said, the 5-epoch version is pretty decent, and since the base model was trained on 1t tokens instead of llama's 1. To the best of my knowledge, a Lora-R of 64 is theoretically equivalent to a full fine-tune and is what Tim Dettmers used when training Guanaco (but there's ongoing debate about this equivalence). What’s a good guide to fine tune with a toy example? I tried using the HuggingFace library without knowing what I was doing and not sure if it worked. run a few epochs on my own data) for medium-sized transformers (500M-15B parameters)? I do research on proteomics and I have a very specific problem where perhaps even fine-tuning the weights of a trained transformer (such as ESM-2) might be great. Recently, I got interested in fine-tuning low-parameter models on my low-end hardware. " We would like to show you a description here but the site won’t allow us. If you are working with a rather popular model, like Mixtral or Llama 3, want to fine tune a LORA/QLORA adapter and dont need to add some custom serving logic, check out Fireworks AI - you only pay for data used in fine tuning, can swap out adapters (so multiple tunes) without paying for either storage, network or idle. This approach allows me to take advantage of the best parts of MLX and Llama. You can already fine-tune 7Bs on a 3060 with QLoRA. One of the latest comments I found on the topic is this one which says that QLoRA fine tuning took 150 hours for a Llama 30B model and 280 hours for a Llama 65B model, and while no VRAM number was given for the 30B model, there was a mention of about 72GB of VRAM for a 65B model. Each of my RTX 3090 GPUs has 24 GB of vRAM with a total of 120 GB of vRAM. HuggingFace's SFT is the slowest among them. I am thinking about buying two more rtx 3090 when I am see how fast community is making progress. ) So there's not really a competition here. Although I've had trouble finding exact VRAM requirement profiles for various LLMs, it looks like models around the size of LLaMA 7B and GPT-J 6B require something in the neighborhood of 32 to 64 GB of VRAM to run or fine tune. Llama 4 Maverick (17B, 128 experts) surpasses GPT-4o % rivals DeepSeek v3 in reasoning and coding. What we really need now is a set of Llama models with this extended pre-training that we can use as a base for longer fine-tunes. I'm also using PEFT lora for fine tuning. Has anyone measured how much faster are some other cards at LoRA fine tuning (eg 13B llama) compared to 3090? 4090 A6000 A6000 Ada A100-40B I have 3090s for 4-bit LoRA fine tuning and am starting to be interested in faster hardware. Interestingly, they also show that extending pre-training by ~1000 steps with the new DOPE encodings works better than just fine-tuning with them. For Kaggle, this should be absolutely enough, those competitions don't really concern generative models, but rather typical supervised learning problems. You only pay for the time the instance is running so you can keep it stopped (via the dashboard or API) around for free until you need it again. They've been working on converting refact for over 2 weeks now and there's even a $2000 bounty on it. What size of model can I fit in a 3090 for finetuning? Is 7B too much for that card? With just 1 batch size of a6000 X 4 (vram 196g), 7b model fine tuning was possible. You can also find it in the alpaca-Lora github that I linked. I'm a huge nerd about Star Trek, please don't judge. It won’t be blisteringly quick, but it should be fast enough to have a conversation etc. However, on executing my CUDA allocation inevitably fails (Out of VRAM). This was confirmed on a Korean site. I bought a p40 and regret not just getting another 3090. Most likely, another conversion script dedicated to phi-1 will be needed. As far as I know you can't train with that though. and your 3090 isn't anywhere close to what you'd need, you'd need about 4-5 3090s for a 7b model. Struggling with AI model fine-tuning? I can help. I know Nvidia Jetson boards are used to train in other domains all the time, specifically computer vision. , 2023b), and we confirm the importance of modifying the rotation frequencies of the rotary position embedding used in the Llama 2 foundation models (Su et al. Like the graph above shows a bunch of options but you're not gonna run on an Apple in production. If we scale up the training to 4x H100 GPUs, the training time will be reduced to ~1,25h. Galore combined with Unsloth could allow anyone to pretrain and do full finetuning of 7b models extremely quickly and efficiently :) I have a 3090 and software experience. Costs $1. 5 32b and cohere's 35b command-r have both been released recently and score very well in the chat arena (practically neck and neck), and significantly above yi 34b, which I think while a little lukewarm at first has finally started to become a great model to use with all the good finetunes it has now. cpp docker image I just got 17. You can also train a fine-tuned 7B model with fairly accessible hardware. I know about Axolotl and it's a easy way to fine tune. To add, I want to learn how to fine tune models on this small cluster and then use the learning to fine tune on my own small setup that i wish to build ( preferably with 1/2 x 3090) Reply reply More replies More replies What hardware would be required to i) train or ii) fine-tune weights (i. I’m currently trying to fine tune the llama2-7b model on a dataset with 50k data rows from nous Hermes through huggingface. 2t/s. The primary advantage is being about to fine tune on your hardware, both in terms of actual fine tuning, and dataset creation, as your overall throughput is at least 10x more on GPU. The speeds of the 3090 (IMO) are good enough. Even if someone trained a model heavily on just one language, it still wouldn't be as helpful or attentive in a conversation as Llama. You can use a local files + AI tool, like LocalGPT, that indexes your docs in a vector database and then connects the vectors to the AI's vector space for queries. I have a fairly simple python script that mounts it and gives me a local server REST API to prompt. Minimizing loss is not always the only thing you need to have to have a nice fine-tune. 34b model can run at about I’m building a dual 4090 setup for local genAI experiments. I am building a PC for deep learning. Get the Reddit app Scan this QR code to download the app now post. Fine-tuning at home may still be possible for small scale projects/models though, but if you start with a 40B model, this may require serious Can't wait for command r plus to get fine tuned for rp. Since one A100 GPU has 40 GB of memory: 140 GB (total memory requirement) / 40 GB (A100 GPU memory) ≈ 3. Personally I prefer training externally on RunPod. In the context of Chat with RTX, I’m not sure it allows you to choose a different model than the ones they allow. The official Phi-2 model, as described in its Hugging Face model card, is a Transformer model boasting a modest 2. After the initial load and first text generation which is extremely slow at ~0. However, if I were to do it again, I would have gotten a fully specced mac and rented A100 clusters for fine tuning tasks instead. Like 30b/65b vicuña or Alpaca. I recently wanted to do some fine-tuning on LLaMa-3 8B as it kinda has that annoying GPT-4 tone. Even with this specification, full fine tuning is not possible for the 13b model. I'm mostly concerned if I can run and fine tune 7b and 13b models directly from vram without having to offload to cpu like with llama. Our strategy is similar to the recently proposed fine-tuning by position interpolation (Chen et al. cpp go 30 token per second, which is pretty snappy, 13gb model at Q5 quantization go 18tps with a small context but if you need a larger context you need to kick some of the model out of vram and they drop to 11-15 tps range, for a chat is fast enough but for large automated task may get boring. Meta's new Llama 4 models can now be fine-tuned & run with using Unsloth. 0 x16, so I can make use of the multi-GPU. 3. The model shows that it is 79 GB when I execute ollama list but when I execute the command ollama run mixtral:8x22b-instruct I get: At this time, I believe you need a 3090 (24GB of VRAM) at the minimum to fine-tune new data with at A100 (80GB of VRAM) being most recommended. Or if not, what is the largest model that can be efficiently finetuned on consumer grade GPUs. I haven't tried unsloth yet but I am a touch sceptical. 4 tokens/second on this synthia-70b-v1. Llama 70B - Do QLoRA in on an A6000 on Runpod. for folks who want to complain they didn't fine tune 70b or something else, feel free to re-run the comparison for your specific needs and report back. Run 65B model at 5 tokens/s using colab. Support for fewer models (we only fine-tune mistral-7b right now) but I think a slightly easier to use UI, and also the main thing is that we tackle automating the dataprep workflow from arbitrary documents/html/pdfs/text to question answer pairs using an LLM to generate the training data. I use the Autotrainer-advanced single line cli command. If we assume 1x H100 costs 5-10$/h the total cost would between 25$-50$. I already know what techniques can be used to fine tune LLMs efficiently, but I’m not sure about the memory requirements. 5. It is faster by a good margin on a single card (60 to 100% faster), but is that worth more than double the price of a single 3090? And I say that having 2x4090s. I have 4x3090's and 512GB of RAM (not really sure if ram does something for fine-tuning tbh). You can also fine-tune +100B models using colab. Fine-tuning usually requires additional memory because it needs to keep lots of state for the model DAG in memory when doing backpropagation. There will definitely still be times though when you wish you had CUDA. I have an Alienware R15 32G DDR5, i9, RTX4090. An experiment like the one from video should at least mention that. I do have quite a bit of experience with finetuning 6/7/33/34B models with lora/qlora and sft/dpo on rtx 3090 ti on Linux with axolotl and unsloth. I currently need to retire my dying 2013 MBP, so I'm wondering how much I could do with a 16GB or 24GB MB Air (and start saving towards a bigger workstation in the mean time). Read our Guide on How To Run Llama 4 here I've been trying to fine tune the llama 2 13b model (not quantized) on AWS g5. And all 4 GPU's at PCIe 4. 83x faster and ues 68% less VRAM. You can fine-tune them even on modern CPU in a reasonable time (you really never train those from scratch). State of the art inference for speed and memory with llama and llama based derivatives is exllama (depending on your use case in combination with oobabooga). Notably, you can fine tune even 70B parameter models using QLoRA with just two 24GB GPUs. Hi I have a dual 3090 machine with 5950x and 128gb ram 1500w PSU built before I got interested in running LLM. I have 256 GB of memory on the motherboard and a hefty CPU with plenty of cores. extrapolating from this, 1 epoch would take around 2. You'd need to understand the basics of NLP and write code to prep the data. After running 2x3090 for some months (Threadripper 1600w PSU) it feels like I need to upgrade my LLM computer to do things like qlora fine tune of 30b models with over 2k context, or 30b models at 2k with a reasonable speed. 2t/s, suhsequent text generation is about 1. Fine-tuning Process: Define Training Arguments: Set hyperparameters like learning rate, batch size, and number of training epochs using TrainingArguments from transformers. My hardware specs are as follows: i7 1195G7, 32 GB RAM, and no dedicated GPU. My question is as follows. Llama 4 Scout (17B, 16 experts) is the best model for its size with a 10M context window. Do you think my next upgrade should be adding a third 3090? How will I fit the 3rd one into my Fractal meshify case? Your best bet would be to run 2x3090s in one machine and then a 70B llama model like nous-hermes. so a full fine-tune For further fine-tuning 70B longlora if you merge the model (following the directions in their repo to include the embed/norm layers), then you can fine-tune as normal with axolotl but you won't get train the embed/norm layers like they suggest, and you won't use their shifted attention (which doesn't work with the latest transformers, so you But this fine-tune is 100% openllama, thanks for pointing out the inconsistency! I used the alpaca gpt4 dataset to proceed to the instruction fine-tuning. Absolutely! - The smallest I can get it to be is about 39GB while training, so it will have to be a A100(40GB) for sure - The hyperparameters are just the starting point, mamba has been difficult to train for sure, The losses are different than what I am used to, so it'll be some experimentation If you want to now bring the idea of the best card for "literally only gaming" and nothing else - then maybe, yea, sure. cpp rupport for rocm, how does the 7900xtx compare with the 3090 in inference and fine tuning? In Canada, You can find the 3090 on ebay for ~1000cad while the 7900xtx runs for 1280$. If you need a GPU with 24G vmem you could rent a 3090 instance on Genesis Cloud. I think dataset is the most important when it comes to fine-tuning. cpp is better than MLX for inference as for now. I was able to load 70B GGML model offloading 42 layers onto the GPU using oobabooga. If you want to Full Fine Tune a 7B model for example, that's absolutely nothing, you would require up to 10x more depending on what you want I am using the (much cheaper) 4 slot NVLink 3090 bridge on two completely incompatible height cards on a motherboard that has 3 slot spacing. It's also why llama. I have a dataset of approximately 300M words, and looking to finetune a LLM for creative writing. Any advice would be appreciated. How practical is it to add 2 more 3090 to my machine to get quad 3090? 3090 is 19 cents per hour on runpod if you accept it being interruptable. You can use it for things, especially if you fill its context thoroughly before prompting it, but finetunes based on llama 2 generally score much higher in benchmarks, and overall feel smarter and follow instructions better. The base fine tune it currently has, has a ton of issues sadly. cpp can support fine tuning by Apple Silicon GPU. I feel like you could probably fine tune an LLM with the AGX Orin (in addition to inference), but it's not like I have a few to play with. And I have been thinking that llama. You might be able to squeeze a QLoRA in with a tiny sequence length on 2x24GB cards, but you really need 3x24GB cards. 0 speed, which theoretical maximum is 32 GB/s. For example, I have a test where I scan a transcript and ask the model to divide the transcript into chapters. I don't know if this is the case, though, only tried fine-tuning on a single GPU. Basically, llama at 3 8B and llama 3 70B are currently the new defaults, and there's no good in between model that would fit perfectly into your 24 GB of vram. Is it possible to fine tune Phi-1. Q4_K_M. Runpod is basically idiotproof if you use the "TheBloke Local LLMs One-Click UI and API" template they have. turboderp_Llama-3-70B-Instruct-exl2 on Oobabooga fine tune question My hardware is 3090 NVIDIA 24 GB VRAM and 4080 NVIDIA 18 GB VRAM , RAM 160 GB and Processor Indeed, I just retried it on my 3090 in full fine-tuning and it seems to work better than on a cloud L4 GPU (though it is very slow) Though this doesn't really solve the case of context extension for bigger models, do you know any tricks that can increase the possible seq len during fine tuning? I tried finetuning a QLoRA on a 13b model using two 3090 at 4 bits but it seems like the single model is split across both GPU and each GPU keeps taking turns to be used for the finetuning process. In conclusion, you would need at least 4 A100 GPUs to fine-tune LLaMA-7B with a 32k context. I assume more than 64gb ram will be needed. 2x TESLA P40s would cost $375, and if you want faster inference, then get 2x RTX 3090s for around $1199. Llama 7B - Do QLoRA in a free Colab with a T4 GPU Llama 13B - Do QLoRA in a free Colab with a T4 GPU - However, you need Colab+ to have enough RAM to merge the LoRA back to a base model and push to hub. 145K subscribers in the LocalLLaMA community. The more people adopt Petals, the easier and faster it will be to work with large models with minimal resources. Jul 23, 2024 · This sounds expensive but allows you to fine-tune a Llama 3 70B on small GPU resources. I've successfully fine tuned Llama3-8B using Unsloth locally, but when trying to fine tune Llama3-70B it gives me errors as it doesn't fit in 1 GPU. Members Online AMD Develops ROCm-based Solution to Run Unmodified NVIDIA's CUDA Binaries on AMD Graphics LLaMA's success story is simple: it's an accessible and modern foundational model that comes at different practical sizes. Choose from our collection of models: Llama 4 Maverick and Llama 4 Scout. At the beginning I wanted to go for a dual RTX 4090 build but I discovered NVlink is not supported in this generation and it seems PyTorch only recognizes one of 4090 GPUs in a dual 4090 setup and they can not work together in PyTorch for training purposes( Although I would go with QLoRA Finetuning using the axolotl template on Runpod for this task, and yes some form of fine-tuning on a base model will let you train either adapters (such as QLoRA and LoRA) to achieve your example Cyberpunk 2077 expert bot. On 33B, you get (based on context) 15-23 tokens/s on a 3090, and 35-50 tokens/s on a 4090. Like how Mixtral is censored but someone released DolphinMixtral which is an uncensored version of Mixtral. " This opens the door for pooling our resources together to train a r/LocalLlama supermodel 😈 Subreddit to discuss about Llama, the large language model created by Meta AI. Can confirm. I have a llama 13B model I want to fine tune. This is my experience and assumption so take it for what it is, but I think Llama models (and their derivatives) have a big of a headstart in open source LLMs purely because it has Meta's data. Just google it. Inference is natively 2x faster than HF! Free OSS package: https://github. Basically you need to choose the base model, get and prepare your datasets, and run LoRA fine-tuning. Has anyone had any luck using axolotls deepspeed or fsdp support for fine-tuning LLama2-70b on multiple 3090ies? if yes, how did you do it ? I have three 3090ies without NvLink and I always run out of memory for any setup using deepspeed or fsdp. , 2021). I have been using open source models from around 6 month now by using ollama. 16 mbatch on two 3090's and getting a very stable 13G/21G VRAM usage. I need to create an adapter for an 7B LLM and wondered if this is feasible on a 3090 or 4090 and how long it would take (broadly). 2b. Is this good idea? Please help me with the decision. Best non-chatgpt experience. I'm not sure Llama. I've recently tried playing with Llama 3 -8B, I only have an RTX 3080 (10 GB Vram). 5 turbo with 50 examples of json, in user prompt each one with all available components possible, in assistant prompt (so the output expected) the actual json. It can take around 6-8 hours on average to go through this process on a A100. The only thing is I did the gptq models (in Transformers) and that was fine but I wasn't able to apply the lora in Exllama 1 or 2. I am fine-tuning yi-34b on 24gb 3090 ti with ctx size 1200 using axolotl. you are able to run fine tune on dual 3090 setup? to 5_1 with some BLAS offloaded to GPU So now that Llama 2 is out with a 70B parameter, and Falcon has a 40B and Llama 1 and MPT have around 30-35B, I'm curious to hear some of your experiences about VRAM usage for finetuning. But if you want to fine-tune an already quantized model -- yes, it is certainly possible to do on a single GPU. Reddit's most popular camera brand-specific subreddit I'm a 2x 3090 as well. If you want some tips and tricks with it I can help you to get up to what I am getting. cpp (Though that might have improved a lot since I last looked at it). If they are switching very fast, you may benefit from increasing your batch size or micro batch size or something. I can fine tune a 12b model using LoRA for 10 epochs within 20 mins on 8 x A100 but with HF's SFT it takes almost a day. I've been trying to fine-tune it with hugging face trainer along with deepspeed stage 3 because it could offload the parameters into the cpu, but I run into out of memory Hi, I love the idea of open source. Doesn't the amount of time it takes to fine-tune a model depend on how much data you are fine-tuning with? Do you mean instruction-tuning with some specific dataset? What does the "5 hours" represent? If you’re running llama 2, mlc is great and runs really well on the 7900 xtx. Anyway, it's obvious the 3090 is the way OP should go. But on 1024 context length, fine tuning spikes to 42gb of gpu memory used, so evidently it won’t be feasible to use 8k context length unless I use a ton of gpus. Nearly every successful serious fine-tuning post I have seen around here mentions something like "rented 8x A100 (8x 80GB = 640GB VRAM) for 10 hours / a few hundred bucks" or something to that tune. for the OA dataset: 1 epoch takes 40 minutes on 4x 3090 (with accelerate). There's not much difference in terms of inferencing, but yes, for fine-tuning, there is a noticeable difference. Qwen 1. The response quality in inference isn't very good, but since it is useful for prototyp I can vouch that it's a balanced option, and the results are pretty satisfactory compared to the RTX 3090 in terms of price, performance, and power requirements. Inference will be fine using llama. The fine-tuning can definitely change the tone as well as writing style. I wanna fix that by using a Opus dataset I found on huggingFace and fine tuning LLaMa-3 8B. 7 billion parameters. The 13B model ended up using about 50GB on the H100. This is not an efficient use of the GPUs. "The updated Petals is very exciting. What are the VRAM requirements for Llama 3 - 8B? 36 votes, 24 comments. Disclaimer: I'm an AI enthusiast and practitioner and very much a beginner still, not a trained expert. From what the paper says, this would result in stronger models. I have a data corpus on a bunch of unstructured text that I would like to further fine-tune on, such as talks, transcripts, conversations, publications, etc. You can run 7B 4bit on a potato, ranging from midrange phones to low end PCs. . So I’m very new to fine tuning llama 2. current hardware will be obsolete soon and gpt5 will launch soon so id just start a small scale experiment first, simple, need 2 pieces of 3090 used cards (i run mine on single 4090 so its a bit slower to write long responses) This is normal, though when I've tuned L1-65b in the past, each 3090 would spend about 10-20 seconds at full utilization. My learning comes from experimentation and community learning, especially from this subreddit. The shared graph doesn't provide much information on the testing conditions, but I have to think that it has to do with the 4090 having a a roughly 2x clock speed. Llama-2 70b can fit exactly in 1x H100 using 76GB of VRAM on 16K sequence lengths. but with 65B you require 2 of the cheapest 3090 or 4090. Performing a full fine-tune might even be worth it in some cases such as in your business model in Question 2. You can't really run it across 2 machines as your interconnect would be far too slow even if you were using 10gig ethernet. 5 hours until you get a decent OA chatbot . This may be at an impossible state rn with bad output quality. 5 hours on a single 3090 (24 GB VRAM), so 7. I'm trying to fine-tune it but I'm running into issues left and right. But keeping in mind the 33b hf model will take more than 64g memory to load, so if you are interested in the fine-tune model you may need to have more than 64g memories otherwise you may end up using mem swap. Basically it depends on your use case. Had to use mixed-precision but then I was only able to fit the 7B model on my 3090 even with 1 batch size. Currently I got 2x rtx 3090 and I amble to run int4 65B llama model. Hence some llama models suck and some suck less. I'm unsure if The point is no one would ever spend $4K for a W7900 when you when you can get an RTX A6000 for $4. I would like to train/fine-tune ASR, LLM, TTS, stable diffusion, etc deep learning models. Nvidia is a superior product for this kind of stuff but the value for the 7900 xtx was better for me personally. I had good results with constant lr and batch size of 1 - which would be heresy for you probably. Single 3090, OA dataset, batch size 16, ga-steps 1, sample len 512 tokens -> 100 minutes per epoch, VRAM at almost 100% Subreddit to discuss about Llama, the large language model created by Meta AI. There are many who still underestimate the compute required to fine tune an LLM after all. Total training time in seconds (same batch size): 3090: 468 s 4060_ti: 915 s The actual amount of seconds here isn't too important, the primary thing is the relative speed between the two. So what I gather is that they optimized llama 8b to be as logical as possible. My experience with fine-tuning a larger, 7B parameter model using LoRA on a single 4090 GPU consumed nearly 15GB of GPU memory. Might be because I can only run 3. 4t, I'm not terribly surprised that the performance is not quite on par. Hi! Oh yes we've had a load of discussions on Galore on our server (link in my bio + on Unsloth's Github repo). Reply reply For BERT and similar transformer-based models, this is definitely enough. e. You only pay for tokens The open-source AI models you can fine-tune, distill and deploy anywhere. I know there is runpod - but that doesn't feel very "local". The professional cards with 48gb or more VRAM are not needed if you only want to use inference and not train your own models. But on the other hand, MLX supports fine tune on GPU. I've tried the model from there and they're on point: it's the best model I've used so far. There is a bit of a missing middle with the llama2 generation where there isn't 30B models that run well on a single 3090. results are interesting but with mistakes, sometimes empty components, even when asking him the exact same user prompt as training, he can't output precisely the I'm trying to get my head around LORA fine-tuning. warddqzgqoaevkpjwnvwnccugvnphzxgizwawjhhhxuuqpzpqb