Fine tune llama 3090 reddit. somehow it makes the model's output kind .

Fine tune llama 3090 reddit Useful table for loras/fine-tunes. I use the Autotrainer-advanced single line cli command. 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 I have looked at a number of fine tuning examples, but it seems like they are always using examples input/output to fine tune. 62gb, One other note is that llama. I know about Axolotl and it's a easy way to fine tune. You can run 7B 4bit on a potato, ranging from midrange phones to low end PCs. Ollama works quite well however trying to get the tools for fine-tuning to work is quite a pain specifically Get the Reddit app Scan this QR code to download the app now. 4 tokens/second on this synthia-70b-v1. 0 speed, which theoretical maximum is 32 GB/s. This was confirmed on a Korean site. Not saying it's worth it (mostly because way slower, personally would go 2x used 3090 or first only one 3090 for trying it /r/StableDiffusion is back open after the protest of Reddit killing open API access, which will bankrupt app Subreddit to discuss open source ai developments, news, new models, LLMs (esp. I am using qlora (brings down to 7gb of gpu memory) and using ntk to bring up context length to 8k. gguf model. I'm also using PEFT lora for fine tuning. I am using 3090 + 1 4060ti 16gb I got recently and it works great. You might be able to squeeze a QLoRA in with a tiny sequence length on 2x24GB cards, but you really need 3x24GB cards. Hi! Oh yes we've had a load of discussions on Galore on our server (link in my bio + on Unsloth's Github repo). While I don't have access to information specific to LLaMA 3, Get the Reddit app Scan this QR code to download the app now. and your 3090 isn't anywhere close to what you'd need, you'd need about 4-5 3090s for a 7b model. 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 My aim is to use qlora to fine tune a 34B model, and I see that the requirement for fine tuning a 34B model using a single card from qlora is 24g vram, and the price of 4090x2 is about equal to 3080 20g x8. I am thinking of: First finetune QLora on next token prediction only. tldr: while things are progressing, the keyword there is in progress, which 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. 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. Very high-level, you show the LLM an input and a desired output. It uses grouped query attention and some tensors have different shapes. I personally prefer to do fine tuning of 7B models on my RTX 4060 laptop. It just takes 5 hours on a 3090 GPU for fine-tuning llama-7B. Im using the WSL2 inside W11 (i like linux more than windows), could that be the reason for the response delay? i have a 3090 and to do joepenna dreambooth I needed all 24gb, this way I could 37 votes, 13 comments. Less data and less computation needed for better results and longer context. Basically you need to choose It depends on your fine tuning models and configs. # Set supervised fine-tuning parameterstrainer so take my advice with a grain of salt but I was having the same problems as you when I was testing my QLora fine-tune of Llama 2 and after I made some changes it worked properly. I have no idea if it's a reasonable fine-tune task. I'll include samples of my code this time to be clearer. If you want to run and fine-tune 70B models, maybe two cards but that will already be overkill for your current skill level. Fine tuning too if possible. 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) Reddit's Loudest and Most In-Tune Community of Bassists Electric, acoustic, upright, and otherwise. It's basically the enthusiast level of machinery. 2b. People seem to consider them both as about equal for the price / performance. 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. 34b model can run at about 3tps which is fairly slow but can To uncensor a model you’d have to fine tune or retrain it, which at that point it’d be considered a different model. There is an issue, that has since been closed, that also shows which file to edit to This ruled out the RTX 3090. I recently wanted to do some fine-tuning on LLaMa-3 8B as it kinda has that annoying GPT-4 tone. The most trustworthy accounts I have are my Reddit, GitHub, and HuggingFace accounts. Or check it out in the app stores   a 3090 outperforms even the fastest m2 and is significantly cheaper, even if you buy two. I made my own batching/caching API over the weekend. Looking for suggestion on hardware if my goal is to do inferences of 30b models and larger. Has anyone tried running LLaMA inference or fine tuning on Nvidia AGX Orin boards? 64GB of unified memory w/ Ampere GPU for ~$2k. Since llama 30b is properly the best model that fits on an rtx 3090, I guess, this Subreddit to discuss about Llama, the large language model created by Meta AI. Reddit's most popular camera brand-specific subreddit! Subreddit to discuss about Llama, the large language model created by Meta AI. I have a 3090 in an EGPU to connect my work laptop and a 4090 in my gaming pc (7950X. I’m currently trying to fine tune the llama2-7b model on a dataset with 50k data rows from nous Hermes through huggingface. cpp Dual 3090 = 4. 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 Subreddit to discuss about Llama, the large language model created by Meta AI. 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. I just bought a 3090 and i want to test some good models wich would be the best for assistent purposes like asking when A 34b codellama 4bit fine tune with short context is another. The cheapest way of getting it to run slow but manageable is to pack something like i5 13400 and 48/64gb of ram. Still pricey as hell, LLama and others do fine on academic benchmarks but OpenAI has a very very tight feedback loop that nobody else has that's not talked about enough. Like how Mixtral is censored but someone released DolphinMixtral which is an uncensored version of Mixtral. Running Mixtral in fp16 doesn't make much sense in my opinion. Isn't that almost a five-fold advantage in favour of 4090, at the 4 or 8 bit precisions typical with local LLMs? I'm a 2x 3090 as well. In terms of speed the 4060ti by itself is about 72% as fast as 3090. There is a soft cap of how large the amount information you feed it can be as the more information it needs to process, the longer it will take. 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 I would like to start from guanaco and would like to fine-tune it and experiment. cpp would both work just fine. Can confirm. You can also train a fine-tuned 7B model with fairly accessible hardware. Personally I prefer training externally on RunPod. But at the moment the Llama 3 fine tuning scene is more in the starting R&D phase, so most of them are not fantastic atm. I am considering the following graphics cards: A100 (40GB) A6000 ada A6000 RTX 4090 RTX 3090 (because it supports NVLINK) If I buy an RTX 4090 or RTX 3090, A6000 I can buy multiple GPUs to fit my budget. 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. I have been doing that with 2xRTX 3090 First goal is to run and possibly Adapter fine tune LLaMA 65B. 99 per hour. somehow it makes the model's output kind In my last post reviewing AMD Radeon 7900 XT/XTX Inference Performance I mentioned that I would followup with some fine-tuning benchmarks. So you can tune them with the same tools you were using for Llama. What all LLMs do is that they continue a specific text. I‘ll report back here. Reply reply OP did you ever find a good service, which allows uploads of custom fine tuned models (fine tuned llama-3 8b for example), 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. Or check it out in the app stores Subreddit to discuss about Llama, Yes. Like 30b/65b vicuña or Alpaca. 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. I have a 3090 and software experience. When used together to load big models I am seeing maybe only a 10% drop or less in tk/s. Community resources, and Subreddit to discuss about Llama, the large language model created by Meta AI. Gook about 46G of my 48G, but seems to run fine. arrow format can be a bit of a process. I am strongly considering buying it but before I do, I would like to know if it'll be able to handle fine-tuning 1558M. 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). 4 x 3090 Build Info: Some Lessons Learned Zephyr 141B-A35B, an open-code/data/model Mixtral 8x22B fine-tune I want fast ML inference(Top priority), and I may do fine-tuning from time to time. ) Training is doable on a 3090, but the process of extracting, tokenizing, and formatting the data you need, then turning it into an actual dataset in . Each of my RTX 3090 GPUs has 24 GB of vRAM with a total of 120 GB of vRAM. I found this GPU that I like from NVIDIA, the RTX 3090. Train LORA for smaller Llama models and fine-tune those models. And I don't think another 3090 is going to give you that much more than memory. A Sub-Reddit dedicating to fences & barriers; showing your Here is the repo containing the scripts for my experiments with fine-tuning the llama2 base model for my grammar corrector app. ADMIN MOD Best current tutorial for training your own LoRA? Also I've got a 24GB 3090, so which models would you recommend fine tuning on? Question | Help I'm assuming 4bit but correct me if I'm wrong there. 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. Or check it out in the app stores     TOPICS Subreddit to discuss about Llama, the large language model created by Meta AI. 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). If only I was able to train with my given hardware. It better runs on a dedicated headless Ubuntu server, given there isn't much VRAM left or the Lora dimension needs to be reduced even further. In my opinion, it outperforms GPT-4, plus it's free and won't suffer from unexpected changes because they randomly To uncensor a model you’d have to fine tune or retrain it, which at that point it’d be considered a different model. 3b) fine-tuning using the dataset lfqa to have a small LLM that have interesting Rag properties. 65b EXL2 with ExllamaV2, or, full size model with transformers, load in 4bit and double quant in order to train. gguf variant. Wonder when the first RLHF chat fine tuned version will come out. You can fine-tune them even on modern CPU in a reasonable time (you really never train those from scratch). If you want, and if your fine-tuning dataset doesn't have any proprietary data or anything, I'd be happy to run the fine tuning for you. Can datasets from huggingface pe This sub is for tool enthusiasts worldwide to talk about tools, professionals and hobbyists alike. 55bpw quant of llama 3 70B at reasonable speeds. Expand user menu Open settings menu. What I'm trying to figure out is, Some graphs comparing the RTX 4060 ti 16GB and the 3090 for LLMs 3. There's a lot of data transfer happening when you do this, so it is a bit slow, but it's a very valid option for fine tuning LLMs. However, I'm a bit unclear as to requirements (and current capabilities) for fine tuning, embedding, training, etc. Llama2-70b is different from Llama-65b, though. I have 256 GB of memory on the motherboard and a hefty CPU with plenty of cores. By the way, HuggingFace's new "Supervised Fine-tuning Trainer" library makes fine tuning stupidly simple, SFTTrainer() class basically takes care of almost everything, as long as you can supply it a hugging face "dataset" that you've prepared for fine tuning. Using the latest llama. I have a 3090 and I can get 30b models to load but it's sloooow. I just found this PR last night, but so far I've tried the mistral-7b and the codellama-34b. However, we are still struggling with building the pc. But alas, I have not given up the chase! For if the largest Llama-3 has a Mixtral-like architecture, then so long as two experts run at the same speed as a 70b does, it'll still be sufficiently speedy on my M1 Max. However, this is the hardware setting of our server, less memory can also handle this type of experiments. I'd like to see someone fine-tune it on the OpenOrca and no-robots datasets, and then fine-tune it further on the Starling-RM-7B-alpha reward model (RLAIF). I tried both the base and chat model (I’m leaning towards the chat model because I could use the censoring), with different prompt formats, using LoRA (I tried TRL, LlamaTune and other examples I found). Costs $1. Hello, I have 2 rtx 3090, and I'm doing 4bit fine tuning on llama2 13b, I need the model to specialize in some legal information, I have a dataset with 400 data, but the model can't get anything right, when to give you training I need so that the model is able to answer adequately ? Instead you want to explore continued pretraining and better fine-tuning. I'd like at least 8k context length, and currently have a RTX 3090 24GB. Reply reply If you are just doing inference, why not just grab a quant? Exllama2 and llama. But on 1024 context length, fine tuning spikes to 42gb of gpu memory used, so evidently it won’t be feasible to I have been trying to fine-tune Llama 2 (7b) for a couple of days and I just can’t get it to work. Skip to main content. I use a single A100 to train 70B QLoRAs. One of my goals was to establish a 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. to adapt models to personal text corpuses. I have a dataset of student essays and their teacher grading + comments. Dual 3090/4090s. My company would like to fine-tune the new Llama 2 model on a list of Q/A that our customers use to ask our support client. They learn quickly enough that it's not a huge hindrance. However, I'm not really like with the results after applying DPO alignment that aligns with human preferences. , i. Open menu Open navigation Go to Reddit Home. You can try fine tuning a 7b model with Unsloth and get a feel of it. My goal is to get phi2 (or tinyllama!) to respond to a natural language request like "Look up the weather and add a todo with what to wear. Members Online • DeltaSqueezer. Another question is whether dual 3090 with nvlink is faster for training (for inference nvlink is not needed). specifically computer vision. cpp on a CPU but not fine tuning? I have been using open source models from around 6 month now by using ollama. If you want to integrate the "advice" field, you'll have to find a special way to accommodate Neat, signed up for an account, but I don't have anything to fine-tune on yet haha My interest is to fine-tune to respond in a particular way. So soon you'll be buying another 3090 to finetune, lol. 5 model on a setup with 2 x 3090? Other specs: I9 13900k, 192 GB RAM. The P40 and P100 cards are not a good choice IMO because they are quite slow and you would most definitely be better off with one or two used RTX 3090 cards. I have a rtx > fine-tuning on textbooks or something unstructured)? In this case what is the end goal? To have a Q/A system on the textbook? In that case, you would want to extract questions and answer based on different chunks of the text in the textbook. 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. I am fine-tuning yi-34b on 24gb 3090 ti with ctx size 1200 using axolotl. true. cpp (Though that might have improved a lot since I last looked at it). 701 votes, 228 comments. As a person who worked with Falcon I can tell you that it is an INCREDIBLY bad model compared to LLaMa for fine-tuning I've been trying to fine tune the llama 2 13b model (not quantized) on AWS g5. Members Online EleutherAI releases the calculated weights for GPT-J-6B (Open Source language model) 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. If your text is in Question/Response format, like ChatGPT, they will complete with what it thinks should follow (if you ask a question, it will give an answer) If it's not a Q&A, then it will just complete the text with the most likely output it Hi I have a dual 3090 machine with 5950x and 128gb ram 1500w PSU built before I got interested in running LLM. M1 Ultra and 3x3090 owners would be fine up to 140b though. How practical is it to add 2 more 3090 to my machine to get quad 3090? I'm currently working on a phi_1_5 (1. The fine-tuning can definitely change the tone as well as writing style. I'm a newbie too, so take my advice with a grain of salt but I was having the same problems as you when I was testing my QLora fine-tune of Llama 2 and after I made some changes it worked properly. As per your numbers, 500k words in 1000 topics would seem like an average topic would be 500 words, which could fit the 800 tokens just fine. In the I'm about to get a second 3090, what's the largest model you can train with two? the llama-2-70b-orca-200k. I have no experience with the P100, but I read the Cuda compute View community ranking In the Top 5% of largest communities on Reddit “Hello World” of fine tuning . For heavy workloads, I will use cloud computing. And on 3090 you also run Q3 version? Yes, but I can also run this with Q4_K (24. Or Alternatively save 600$ and get a used 3090. 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. The Alpaca data set is at https: It just takes 5 hours on a I have a dataset of approximately 300M words, and looking to finetune a LLM for creative writing. Is it better? Depends on what you're trying to do. People in the Discord have also suggested that we fine-tune Pygmalion on LLaMA-7B instead of GPT-J-6B, I hope they do so because it would be incredible. how much vRAM do I need to fine tune? Minstral 7B works fine on inference on 24GB RAM (on my NVIDIA rtx3090). I know 4090 doesn't have any more vram over 3090, but in terms of tensor compute according to the specs 3090 has 142 tflops at fp16 while 4090 has 660 tflops at fp8. If you go the 2x 3090 route you have 48GB VRAM locally, which is 'good enough' for most things currently without breaking the bank. Well this is a prompting issue not fine tuning. Or check it out in the app stores     TOPICS. About 1/2 the speed at inference. Or check it out in the app stores   Single 3090 = 4_K_M GGUF with llama. The field you are diving into is so complex and intricate that you could easily spend the next two years just playing around with fine-tuning 7B models. Then research the common bottle necks. But in order to want to fine tune the un quantized model how much Gpu memory will I need? 48gb or 72gb or 96gb? does anyone have a code or a YouTube video tutorial to fine tune the model on AWS or Google Colab? Thanks in advance! I've seen multiple threads about fine tuning code llama, Quantizing with linear post fine tune makes sense to me based off of what I've read. I double checked the cuda installation and everything seems fine. For inferencing (and likely fine-tuning, which I'll test next), your best bang/buck would likely still be 2 If you want to Full Fine Tune a 7B model for so it's more like a 4090 is 3x 4070. For reference, a 30B 4 bit llama can be finetuned up to about 1200 tokens on a single 3090 but this figure will drop to about 800 tokens if eval loss is measured during the finetuning. Any advice would be appreciated. so what would be a better choice for a multi-card? 4090x2 or 3080 20g x8?. You're not going to be able to do fine-tunes of any type, for that you need to rent H100s online. Llama 70B - Do QLoRA in on an A6000 on Runpod. You can also find it in the alpaca-Lora github that I linked. You should watch some videos on fine-tuning dataset creation, as that's the crux of what you're asking. That's what standard alpaca has been fine-tuned to do. Consider using cloud platforms like Google Colab (offering free tier GPUs) or exploring libraries like Unsloth that optimize memory usage. py that worked well although they might have changed it. 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. . Are you satisfied with 30b? Because 65b with 8k can happen with 3 cards. I'm thinking about an 8x70B MoE, but I know it's a big undertaking. With dual 4090 you are limited with the PCIe 4. 03 HWE + ROCm 6. org On a more positive note: If this model performs well, it means that with actual high-quality, diverse training data, an even better LLaMA fine-tune is possible while still only using 7B parameters. I have a 3090 (now) is it possible to play with training 30B Models? I'd like to learn more about this and wondering if there's an organised place of such knowledge. Then instruction-tune the model to generate stories. A second 3090 is only worth it if you exactly know what to do with it. Reply reply You can try paid subscription of one of Cloud/Notebook providers and start with fine-tuning of Llama-7B. Still would be better if one could fine tune even with just a good CPU. /r/StableDiffusion is back open after the protest of Reddit killing open API access, which will bankrupt app developers, 27 votes, 11 comments. Run stable diffusion and LLMs like llama 30b or below at high speed. But I just bought a 3090, so my possibilities are higher now. It fits on one to at least 512 or 1024 if you use smaller batches. Reply reply I will consider them, but first I want to try out my method. I want to fine-tune LLaMA with it to create a model which knows how to rate essays, and is able to use that implicit knowledge to respond to instructions other than directly outputting grades + comment, like commenting from a specific aspect only, or generate sample paragraphs of a specific level. We're now read-only indefinitely due to Reddit Incorporated's poor management and decisions related to third party platforms and content management. I've been giving some thought to trying my hand at building a Mixture of Experts (MoE) model using Llama 3. It also matters very much which specific model and fine-tune you're talking about. Like many suggest, ooba will work but at some point you might want to look at things like axolotl for better control of fine-tuning Reply reply Top 1% Rank by size I currently have a 3090, looking to add another card or two, issue with the 3090 is that I'm trying to build a voice assitant and use Whisper Streaming but it's just too slow and can't load larger models on a single 3090. 5ghz, 16gb ddr4 ram and only a radeon pro 575 4gb graca. 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. Problem is that once formatted, my data sample are mostly around 2048 token long, what makes rather large sequences. How to run this AI model ? How should be tuned to work good on the Oobabooga to work with no issue of the output, tokens VRAM and RAM ? Do you think it will be better to run this in Kobold ? Hi, out of curiosity: how did you setup your build with 2x 3090's and nvlink? My team is planning on doing just the same; using 2x 3090's chained together with nvlink in order to run and fine-tune llama2 70b models. With just 1 batch size of a6000 X 4 (vram 196g), 7b model fine tuning was possible. Both trained fine and were obvious improvements over just 2 layers. Is it bigger? No, alpaca-7B and 13B are the same size as llama-7B and 13B. For Kaggle, this should be absolutely enough, those competitions don't really concern generative models, but rather typical supervised learning problems. 145K subscribers in the LocalLLaMA community. I have a rather long and complex prompt that I use together with data to be processed by my normal (not fine tuned model) and I would like to not have to send the long set of instructions every time, when I need it to process the data. Performing a full fine-tune might even be worth it in some cases such as in your business model in Question 2. I don't know if this is the case, though, only tried fine-tuning on a single GPU. With the RTX 4090 priced over **$2199 CAD**, my next best option for more than 20Gb of VRAM was to get two RTX 4060ti 16Gb (around $660 CAD each). 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. Send me a DM here on Reddit. Internet Culture (Viral) Amazing It may be faster to fine tune a 4-bit model but llama-recipes only has instructions for fine tuning the base model. With single 3090 I got only about 2t/s and I wanted more. Get the Reddit app Scan this QR code to download the app now. Its unique qualities, especially at the 7B size, are facilitating significant progress in multilingual and multimodal tasks. It's about the fact that I have the following specifications i5 @3. Also don't just get more RAM for no reason. I fine-tune and run 7b models on my 3080 using 4 bit butsandbytes. So, I wanted to see if anyone here would be interested in collaborating on this project. 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. Second reason is local fine-tuning. cpp segfaults if you try to run the 7900XT + 7900XTX together, but ExLlamaV2 seems to run multi-GPU fine (on Ubuntu 22. Members Online • theredknight. the training software would need to be modified to tune One way is to use Data Parallel (DP) training. What’s a good guide to fine tune with a toy example? The qlora fine-tuning 33b model with 24 VRAM GPU is just fit the vram for Lora dimensions of 32 and must load the base model on bf16. So far, the performance of llama2 13b seems as good as llama1 33b. (40 tokens/s m2, 120 on 2x 3090) This was a few months ago, though. This splits the model between both GPUs, and essentially behaves as one bigger 48 GB GPU. 0). Of course you can still ask questions and chat with raw GPT-3 or LLaMa, but arguably it's the fine-tuning that made ChatGPT take the world by storm - not only can you get answers out of it, but the *manner* in which you can talk to it makes it seem almost human since this is We're focusing on the enhancement of quantization structure and partial native 4-bit fine-tuning: We are deeply appreciative of the GPTQ-Llama project for paving the way in state-of-the-art LLM quantization. Sadly, a lot of the libraries I was hoping to get working didn't. I'm running older hardware, i9-7960x CPU, 64G RAM on a x299 MB with 2 3090s. I Subreddit to discuss about Llama, the large language model created by Meta AI. Thanks! Reply reply I’m new to this but hope to learn to fine tune a model. You could definitely experiment significantly with such a machine, and it would likely remain relevant for smaller-model use for several years. My P40 is about 1/4 the speed of my 3090 at fine tuning. Not sure I understand. From my own experience I can tell you that all you need to get started is a single RTX 3090 / RTX 4090. 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. I don't know when HF releases support for int4 fine tuning. Hence some llama models suck and some suck less. 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. Do you want to do fine You're going to be able to run the biggest model of SD 3 (not out yet) and a small 2. Is it possible to fine tune Phi-1. Think about what exactly you want to do with the system after the upgrade that you currently cannot do. e. 12x instance which has 4*24gb A10GPUs, and 192gb ram. Or check it out in the app stores   Subreddit to discuss about Llama, the large language model created by Meta AI. The model shows that it is 79 GB when I execute ollama list but when I 7gb model with llama. Galore combined with Unsloth could allow anyone to pretrain and do full finetuning of 7b models extremely quickly and efficiently :) Finally, I managed to get out from my addiction to Diablo 4 and found some time to work on the llama2 :p. In fine-tuning there are lots of trial and errors so be prepared to spend time & money if you opt online option. 200+ tk/s with Mistral 5. I haven't tried unsloth yet but I am a touch sceptical. 18 votes, 24 comments. Subreddit to discuss open source ai developments, news, new models, LLMs (esp. 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. We welcome posts about "new tool day", estate sale/car boot sale finds, "what is this" tool, advice about the best tool for a job, homemade tools, 3D printed accessories, toolbox/shop tours. I really wonder why you can have good inference with llama. 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. A full fine tune on a 70B requires serious resources, rule of thumb is 12x full weights of the base model. If you want some tips and tricks with it I can help you to get up to what I am getting. r/LocalLLaMA A chip A close button. Question | Help I'm trying to get my head around LORA fine-tuning. openllama is a reproduction of llama, which is a foundational model. I have a 24gb Card and i want to fine tune an llm on a dataset i created whats the best way? And is it even possible Here is I have a CSV full of text that is more of the style of how I’d like the model to communicate. 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. The final intended use case of the fine-tuned model will help us understand how to finetune the model. Do you think my next upgrade should be adding a third 3090? How will I fit the 3rd one into my Fractal meshify case? Hey everyone! This is Justus from Haven. I am relatively new to this LLM world and the end goal I am trying to achieve is to have a LLaMA 2 model trained/fine-tuned on a text document I have so that it can answer questions about it. Help 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. 0bpw esl2 on an RTX 3090. Even though this text is not in the prompt/response format people would usually use to increase a model’s functionality, can I still use this data to fine-tune the model to You should watch some videos on fine-tuning dataset creation, as that's the crux of what you're asking. Even with this specification, full fine tuning is not possible for the I had posted this build a long time ago originally with dual RTX 3090 FEs but I have now upgraded it to dual MSI RTX 3090 To Suprim X GPUs and have done all the possible Just bought second 3090, to run Llama 3 70b 4b quants. So I’m very new to fine tuning llama 2. cpp docker image I just got 17. Q4_K_M. But since I saw how fast alpaca runs over my cpu and ram on my computer, I hope that I could also fine-tune a llama model with this equipment. If I don’t forget to, that is. Q6 or 6bpw should give you almost the full performance of the full model on transformers. Two used NVIDIA 3090 GPUs can handle LLaMA-3 70B at 15 tokens per second. An RTX 4090 is definitely faster for inference than an RTX 3090, but I honestly haven't seen tests/benchmarks for fine tuning apeed. I feel like you could probably fine tune an LLM with the AGX Orin (in addition to inference), probably more like 1/10 of a 3090 in terms of performance. 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. 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. Is this something reasonable to do with an RTX 3090 or would I be better off on 2x A4000's or 2x A5000's on an nvlink? If none of the above is reasonable then I will probably just train on the cloud and then download the newly trained custom model. I was doing those kinds of fine-tunes on Mistral and Yi-34B. Ideally, the model would only be able to answer questions about the specific information I give it so it can't answer incorrectly or respond with Get the Reddit app Scan this QR code to download the app now. I personally don't think a dual 4060TI build would be bad, but of course it won't be quite as fast as 3090s. What size of model can I fit in a 3090 for finetuning? Is 7B too much Has anyone measured how much faster are some other cards at LoRA fine tuning (eg 13B llama) compared to 3090? - 4090 - A6000 - A6000 Ada - Full parameter fine-tuning of the LLaMA-3 8B model using a single GTX 3090 GPU with 24GB of graphics memory? Please check out our tool for fine-tuning, inferencing, and evaluating GreenBitAI's low-bit LLMs: We've successfully run Llama 7B finetune in a RTX 3090 GPU, on a server equipped with around ~200GB RAM. /r/StableDiffusion is back open after the protest of Reddit killing open API access, which will bankrupt app Subreddit to discuss about Llama, the large language model created by Meta AI. ADMIN MOD Fine-tuning LORA/QLORA on a 3090 . Playing with text gen ui and ollama for local inference. However, I'd like to mention that my primary motivation to build this system was to comfortably experiment with fine-tuning. You are going to be able to do qloras for smaller 7B, 13B, 30B models. It's pretty cool to experiment with it. 146K subscribers in the LocalLLaMA community. It's only going to get better with better For BERT and similar transformer-based models, this is definitely enough. See https://jellyfin. /r/StableDiffusion is back open after the protest of Reddit killing open API access, which will bankrupt app A 70b 8k fine-tuned model is said to be in the works which should increase summarization quality I believe that the largest model will be best at interpreting context, based on the previous feedback from users here: that say 65B is a big leap in quality from 33b (If that gap no longer tangibly exists, I'd happily use 34b) Llama2-7b and 13b are architecturally identical to Llama-7b and 13b. The response quality in inference isn't very good, but since it is useful for prototyping fine-tune datasets for the bigger sizes, because you can evaluate and measure the quality of responses. If you want to integrate the "advice" field, you'll have to find a special way to accommodate that. I've tested 7B on oobabooga with a RTX 3090 and it's really good, going to try 13B with int8 later, and I've got 65B downloading for when FlexGen support is implemented. Others are alread onto building it. Log In / Sign Up; They had a fine-tune. Log In / Sign Up; Advertise on Reddit; So we can fine tune thison a 3090 gpu? So I’m very new to fine tuning llama 2. Subreddit to discuss about Llama, the large language model created by Meta AI. wake up, bro. I know of some universities that are able to successfully fine-tune 1558M on their hardware but they are all using a Tesla V100 GPU. 04. You can be either yourself or the person you were chatting to. Or check it out in the app stores But to start with and work out the kinks, I recommend fine tuning LLaMA 7B on Alpaca. I have a llama 13B model I want to fine tune. Over the weekend I reviewed the current state of training on RDNA3 consumer + workstation cards. My gf didn't like talking to her ai-self but enjoyed talking to ai-me for example, which makes some sense. I can say that alpaca-7B and alpaca-13B operate as better and more consistent chatbots than llama-7B and llama-13B. Get app You can already fine-tune 7Bs on a 3060 with QLoRA. There are many examples on Unsloth’s GitHub page, why not give I already know what techniques can be used to fine tune LLMs efficiently, but I’m not sure about the memory requirements. Many users of our open source deployment server without an ML background have asked us how to fine-tune Llama V2 on their chat datasets - so we created llamatune, a lightweight library that lets you do it without writing code!Llamatune supports lora training with 4-and 8-bit quantization, full fine-tuning and model parallelism out-of 102 votes, 30 comments. Get app Get the Reddit app Log In Log in to Reddit. Here is the repo containing the scripts for my experiments with fine-tuning the llama2 base model for my grammar corrector app. I probably would buy a server board with full 16 lanes for all GPUs if I wanted to do training. I know there is runpod - but that doesn't feel very "local". HuggingFace's SFT is the slowest among them. In the context of Chat with RTX, I’m not sure it allows you to choose a different model than the ones they allow. It should work with any model that's published properly to hugging face. Even with this specification, full fine tuning is not possible for the 13b model. I had to get creative with the mounting and assembly, but it works perfectly. As a result I'm having trouble fine-tuning that model on a 24go GPU. My knowledge on hardware is limited. It was for a personal project, and it The minimum you will need to run 65B 4-bit llama (no alpaca or other fine tunes for this yet, but I expect we will have a few in a month) is about 40GB ram and some cpu. Bonus point: if you do it, you can then set yourself as any user. I wanna fix that by using a Opus dataset I found on huggingFace and fine tuning LLaMa-3 8B. 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. I have a computer with an RTX 3090 here at home. Fine-tuning Llama-3 8B requires significant GPU resources. A 4bit 30b should train fine on 2 cards at 2k. Code Llama was developed by fine-tuning Llama 2 using a higher sampling of code. LLaMa, Mistral, fine tuning) and other related topics. bdevg aprgsb ykof fptiigp ipndd buomsq iuvmii mqhfehq fsjrh fyzhnqi