Llama 2 benchmarks reddit. It's this Reddit post's title that was super misleading.

Llama 2 benchmarks reddit 328 users here now. I have a MSI X670E Carbon Wifi, which has 2 PCI-E slots connected directly to the PSU (PCI-E 5. 8 on llama 2 13b q8. You could make it even cheaper using a pure ML cloud computer Subreddit to discuss about Llama, the large language model created by Meta AI. way more stock in benchmarks than they deserve You have unrealistic expectations. " Our benchmarks show the tokenizer offers improved token efficiency, yielding up to 15% fewer tokens compared to Llama 2. Deaddit: Run a local Reddit-clone with AI View community ranking In the Top 5% of largest communities on Reddit. Zero-shot Trivia QA is harder than few-shot HellaSwag, but they are testing the same kinds of behavior. cpp. /r/StableDiffusion is back open after the protest of Reddit killing open API access, which will bankrupt app developers Someone just reported 23. AI: Yi (6B, 34B) Made by WordPress 6. 5-AshhLimaRP-Mistral-7B, Noromaid-v0. Members Online • blueSGL . 636 GBps), while it Saw this tweet by Karpathy explaining how LLMs are fast enough on local machines because "we" are very interested in batch 1 inference. 5. com Open. Or check it out in the app stores   Honestly all of these benchmarks for waifu-anime-XXL56-7b models is extremely uninteresting. Or check it out in the app stores   At the end of the day, what are the benchmarks. I run an AI startup and I'm using GPT 3. There was another LLaMa based model where they claimed it reached ChatGPT Get the Reddit app Scan this QR code to download the app now. But even they've omitted details too (e. 8 8. Though if i remember correctly, the oobabooga UI can use as backend: llama-cpp-python (similar to ollama), Exllamav2, autogptq, autoawq and ctransformers To add some of my (very limited) experience with Nous Hermes Llama 2 13B. Llama 2 q4_k_s (70B) performance without GPU NewHope creators say benchmark results where leaked into the dataset, which explains the HumanEval score. Welcome to the rotting corpse of a dying Using 2. Tried llama-2 7b-13b-70b and variants. You really do have to make judgement calls based on your use case and general vibes. This way the accuracy measure would be more representative of any situation, as there may be specific nuances to this I have been trying to fine-tune Llama 2 (7b) for a couple of days and I just can’t get it to work. Commercial-scale ML with distributed compute is a skillset best developed using a cloud compute solution, not two 4090s on your desktop. 3t/s a llama-30b on a 7900XTX w/ exllama. Try running it with temperatures below 0. Really need a robust benchmark for all these models comparing to GPT4. SqueezeLLM got strong results for 3 bit, but interestingly decided not to push 2 bit. 2 in the paper: We demonstrate the possibility of fine-tuning a large language model using landmark's token and therefore extending the model's context length. Meta, your move. A few BramVanroy/Llama-2-13b-chat-dutch · Hugging Face Here it is: NewHope creators say benchmark results where leaked into the Interesting that it does better on STEM than Mistral and Llama 2 70b, but does poorly on the math and logical skills considering how linked those subjects should be. It's quality, diversity and scale is unmatched in the current OS LM landscape. 0. 1. Do you run generic benchmarks on each model first, and then try to see if the final merged model is doing similar or better? Or, do you merge models that they cover each other's weaknesses and see if the final model will be the best of two worlds? Zero-Trust AI APIs for Llama 2 70b Integration upvotes · comment. Any model that has more context is infinitely more useful, I had great results from context retrieval tests at 40k+ tokens on Qwen2. The new benchmarks dropped and shows that Puffin beats Hermes-2 in Winogrande, Arc-E and Hellaswag. Adding the 3060Ti as a 2nd GPU, even as eGPU, does improve performance over not adding it. The License of WizardLM-2 70B is Llama-2-Community. Dutch Llama 2 13b chat. 2% 14 11 50 6 Gemini Pro 10. Anything more than that seems unrealistic. 70B at 2. After weeks of waiting, Llama-2 finally dropped. Instead of using GPU and long training times to get a conversation format, you can just use a long system prompt. Members Online • Initial-Image-1015. 7 billion parameter Phi-2. 5 bits *loads* in 23GB of VRAM, indicating that it isn't actually usable on a 24GB card. g. TABLE HERE What would be really neat is to do it with 3 or even 5 different combinations of information to extract for each test. 5 on mistral 7b q8 and 2. Benchmarks seem promising several benchmarks got worse vs base llama2 this is not a slam dunk here. The original 34B they did had worse results than Llama 1 33B on benchmarks like commonsense reasoning and math, but this new one reverses that trend with better scores across everything. I've just updated it to add three models: GPT-4-turbo I posted my latest LLM Comparison/Test just yesterday, but here's another (shorter) comparison/benchmark I did while working on that - testing different formats and quantization levels. 5B-v2 and sadly it mostly produced gibberish. LLaMA 2 outperforms other open-source models across a variety of benchmarks: MMLU, TriviaQA, HumanEval and more were some of the popular benchmarks used. (HF links incl in post) Discussion So far I've found: /r/StableDiffusion is back open after the protest of Reddit killing open . Discussion Hi, I had posted three months back a small benchmark comparing some OpenAI and Mistral models in three categories: general knowledge, logic and hallucination. 5 on HumanEval, which is bad news for people who hoped for a strong code model. Reaches within 0. ) are not tuned for evaluating this Llama 2 is the first offline chat model I've tested that is good enough to chat with my docs. It's this Reddit post's title that was super misleading. Hi. r/simracing. gguf --ignore-eos --ctx-size 1024 --n-predict 1024 --threads 10 --random-prompt --color --temp 0. Even for the toy task of explaining jokes, it sees that PaLM >> ChatGPT > LLaMA (unless PaLM examples were cherry-picked), but none of the benchmarks in the paper show huge gaps between LLaMA and PaLM. Llama 1 was intended to be used for research purposes and wasn’t really open source until it was leaked. dev. ADMIN MOD Reproducing LLM benchmarks Discussion I'm running some local benchmarks (currently MMLU and BoolQ) on a Has anyone tested out the new 2-bit AQLM quants for llama 3 70b and compared it to an equivalent or slightly higher GGUF quant, like around IQ2/IQ3? It mostly depends on your ram bandwith, with dual channel ddr4 you should have around 3. In actual usage I swear it's better then Llama-3 from my playing around with it, but I guess specific use cases these benchmarks do are not what I do. I wasn't aware that metas chat fine-tune was made with RLHF. Reply reply More replies More replies Get the Reddit app Scan this QR code to download the app now. 5, providing a comprehensive outlook, while GPT-4 earned a 9 for incorporating additional points about automation tools and meta-learning. This benchmark is mainly intended for future LLMs with better reasoning (GPT-5, Llama3, etc. 1% overall for the average GPT4ALL Sota score with Hermes-2. 9 and WizardLM at 3. If i got it right, then does that mean that most of available speed benchmarks are only for the first response of the Our company Petavue is excited to share our latest benchmark report comparing the performance of the newest 17 LLMs (including GPT-4 Omni) across a variety of metrics including accuracy, cost, throughput, and latency for SQL generation use cases. Just use the cheapest g. MHA), it "maintains inference efficiency on par with Llama 2 7B. 5 in my small use case Generation I was solving my German Arbeitsbuch and got a doubt. Well I remember there being some mistakes in the benchmarks table of Phi-3-mini model card compared to the published research paper so I avoided adding Phi-3-mini. While fine tuned llama variants have yet to surpassing larger models like chatgpt, they do have some Get a motherboard with at least 2 decently spaced PCIe x16 slots, maybe more if you want to upgrade it in the future. small/medium/large instead of the actual parameter count). Llama 2 models were trained with a 4k context window, if that’s what you’re asking. 3-2. Any remaining layers will be assigned to your last GPU. You can review the answers and see, e. Also, Group Query Attention (GQA) now has been added to Llama 3 8B as well. The problem is that people rating models is usually based on RP. Or check it out in the app stores   are giving me a hard time. Jesus Christ man, 3 A100's? What do you use to browse reddit, a small HPC cluster??? It's a fun-sized 13B model, my consumer ass RTX4090 will run it just 74 votes, 31 comments. 1 vs Vicuna-13B at 2. cpp and python and accelerators - checked lots of benchmark and read lots of paper (arxiv papers are insane they are 20 years in to the future with LLM models in quantum computers, increasing logic and memory with hybrid models, its Get an ad-free experience with special benefits, and directly support Reddit. 2. r/simracing Right now, it's using a llama-cpp-python instance as it's generation backend, but I think native Python using CTransformers would also work with comparable performance and a decrease in project code complexity. So then it makes sense to load balance 4 machines each running 2 cards. 0 (so equivalent to X16 PCI-E 3. Or check it out in the app stores   From the perplexity curves on the llama 2 paper (see page 6 here), you can see roughly that a 7B model can match the performance Outside of benchmarks, people generally judge models on their conversational abilities. StabilityAI released FreeWilly2. 1-13B I am running gemma-2-9b-it using llama. What's the current "Best" LLaMA LoRA? or moreover what would be a good benchmark to test these against. Q4_0. Traditional pre-LLM benchmarks: These are the ones used in NLU or CV in pre-LLM world. Get the Reddit app Scan this QR code to download the app now. GPT4/Mistral/Claude3 mini-benchmark . my subreddits and directly support Reddit. - fiddled with libraries. GO items. 55 LLama 2 70B to Q2 LLama 2 70B and see just what kind of difference that makes. 9 tokens/second on 2 x 7900XTX and with the same model running on 2xA100 you only get 40 tokens/second? Why would anyone buy an a100. This is the most comprehensive fully open-source benchmark to date. jump to content. huggingface. 5 TB/s bandwidth on GPU dedicated entirely to the model on highly optimized backend (rtx 4090 have just under 1TB/s but you can get like 90-100t/s with mistral 4bit GPTQ) Since 13B was so impressive I figured I would try a 30B. Competitive models include LLaMA 1, Falcon and MosaicML's MPT model. 2-7b Using llama-index ReAct Agent. 2-1B GGUF quantizations to find the best balance between speed and accuracy using the IFEval dataset. Help Almost 4 months ago a user posted this extensive benchmark about the effects of different ram speeds and core count/speed and cache for both prompt processing and text generation: /r/StableDiffusion is back open after the protest of Reddit killing open Here are my first round benchmarks to compare: Not that they are in the same category, but does provide a baseline for possible comparison to other Nvidia cards. 4K subscribers in the DevTo community. AMD RX 7900 GRE 16Gb $540 new and Nvidia GTX 1070 8Gb about $70 used Here are the initial benchmarks and 'eval rate tokens per second' is the measuring standard. I was wondering has anyone worked on a workflow to have say a opensource or gpt analyze docs from say github or sites like docs. View community ranking In the Top 10% of largest communities on Reddit. to's best submissions. Also somewhat crazy that they only needed $500 for compute costs in training if their results are to be believed (versus just gaming the benchmarks). Was looking through an old thread of mine and found a gem from 4 months ago. 5 for some things, its only . Should I benchmark Llama 3. 1 cents per 1000 tokens! It allows to run Llama 2 70B on 8 x Raspberry Pi 4B View community ranking In the Top 5% of largest communities on Reddit. The benchmark I pay most attention to is needle-in-a-haystack. This is pretty great for creating offline, privacy first applications NEW RAG benchmark including LLaMa-3 70B and 8B, CommandR, Mistral 8x22b So e. Mistral 7B Beats Llama 2 13B on All Benchmarks. Get the Reddit app Scan this QR code to download the app now Extensive LLama. 0) Didn't knew about the discussion, gonna go there, thanks. Or check it out in the app stores ARC PRIZE ARC Prize is a $1,000,000+ public competition to beat and open source a solution to the Base model: The base model outperforms every similar sized open source base model by a huge margin, the difference is so big, that Falcon-40B gets outperformed by llama-2-7b on some benchmarks. /r/StableDiffusion is back open after the protest of Reddit killing open API access, which will bankrupt app developers, hamper moderation, and exclude blind users from the site. 5/4 in terms of benchmarks or cost. Many should work on a 3090, the 120b model works on one A6000 at roughly 10 tokens per second. 3 21. Hopefully that holds up. Then when you have 8xa100 you can push it to 60 tokens per second. 2, and Claude-100k at 3. To get 100t/s on q8 you would need to have 1. This chart showcases a range of benchmarks for GPU performance while running large language models like LLaMA and Llama-2, using various quantizations. In the meantime I’ve also trained a baby llama on Eric Faldore's Samantha database from scratch. ADMIN MOD I don’t rely much on benchmarks, but on hands-on experience. Llama-2-70b-Guanaco-QLoRA becomes the first model on the Open LLM Leaderboard to beat gpt3. I've mostly been goofing around with the Llama 1 33B q5k_M models, testing them in dungeon crawl scenarios, I found Guanaco pretty nice for the descriptions, but ultimately found it to be too long winded, it keeps pouring out token after token and I constantly found I had to edit parts out because it From Microsoft's technical report on Phi-2 "Secondly, we use innovative techniques to scale up, starting from our 1. main. That is why I find this upscaling thing very interesting. 4 Llama-1-33B 5. They give a sense of how the LLMs compare against traditional ML models Get the Reddit app Scan this QR code to download the app now. See how Llama 3 beats GPT-3. 5bpw models. ). A mirror of dev. Advertisement Coins. 8 bit! That's a size most of us probably haven't even tried. Expecting to use Llama-2-chat directly is like expecting 2. Good Progress: Check out our intermediate checkpoints and their comparisons with baseline Pythia in our github. But, I just checked and the mistakes were resolved. We evaluate CAA's effectiveness on Llama 2 Chat using both multiple-choice behavioral question datasets and open-ended generation tasks. cpp and ask for custom models to be loaded). Once you have LLama 2 running (70B or as high as you can make do, NOT quantized) , then you can decide to invest in local hardware. 5's MMLU benchmark The current gpt comparison for each I've received a freelance job offer from a company in the banking sector that wants to host their own LLAMA 2 model in-house. For GPTQ-for-LLaMa: --layers-dist: Distribution of layers across GPUs. So I looked further into the Palm 2 numbers, and it seems like maybe there's some foul play involved with tricks such as chain-of-thought or multiple attempts being used to inflate the benchmark scores when the corresponding scores from GPT-4 didn't use these techniques. xxx instance on AWS with two GPUs to play around with; it will be a lot cheaper, and you'll learn the actual infrastructure that this technology revolves around. Gemini Pro: 14. Reply reply nuketro0p3r I've created a new benchmark for myself to test model ability to follow instructions. If you really wanna use Phi-2, you can use the URIAL method. is there an easy way to run all the typical benchmarks I see models being tested against? Share Add a Comment. ARC took roughly an hour on a 3090. There are clearly biases in the llama2 original data, from data kept out of the set. View community ranking In the Top 20% of largest communities on Reddit. I don’t know which is best but I have had some multi-turn success with Airoboros-mistral2. You can also track the training loss here:🔗 Track Our Live Progress. Did some calculations based on Meta's new AI super clusters. Adaptable: Built on the same architecture and tokenizer as Llama 2, TinyLlama seamlessly integrates with many open-source projects designed for Llama. I've been having some trouble getting the llama 2 models to do some more complex The Hermes 2 model was trained on 900,000 instructions, and surpasses all previous versions of Hermes 13B and below, and matches 70B on some benchmarks!Hermes 2 changes the game with strong multiturn chat skills, system prompt capabilities, and uses ChatML format. The repository can run all the benchmarks in the Open LLM Leaderboard however, I only tested with the ARC dataset, since it would take a long time to do all four. I found this upscaled version of Llama 3 8b: Llama-3-11. cpp with --rope-freq-base 160000 and --ctx-size 32768 and it seems to hold quality quite well so far in my testing, better than I thought it would actually. vllm inference did speed up the inference time but it seems to only complete the prompt and does not follow the system prompt instruction. 5-q4_1 which is 29gb to fit within 16gb x 2 Although the Dual P100 is consistently 9 tokens/s and Dual P40 is 11 tokens/s, It takes only 11 seconds to load 29gb in to P100 (2. The questions can be split half-half in 2 possible ways: First split: sfw / nsfw. Or they just have bad reading comprehension. We just released Llama-2 support using Ollama (imo the fastest way to setup Llama-2 on Mac), and would love to get some feedback "Open Hermes 2. One thing is for a model to perform robustly on a benchmark, another is to perform only if the prompt is exactly right. 5 Nous Hermes 2 Yi 34B: 1. So for a smaller Note how the llama paper quoted in the other reply says Q8(!) is better than the full size lower model. Changing the size of the model could affects the weights in a way that make it better at certain tasks than other sizes of the same models. 7 billion parameters, Phi-2 surpasses the performance of Mistral and Llama-2 models at 7B and 13B parameters on various aggregated benchmarks. Specifically, we performed more robust data cleaning, updated our data mixes, trained on 40% more total tokens, doubled the context length, and used grouped-query attention (GQA) to improve inference scalability for our larger models. 1-mistral-7b Between this three zephyr-7b-alpha is last in my tests, but still unbelievable good for 7b. Hey everyone! I've been working on a detailed benchmark analysis that explores the performance of three leading Language Learning Models (LLMs): Gemma 7B, Llama-2 7B, and Mistral 7B, across a variety of libraries including Text Generation Inference, vLLM, DeepSpeed Mii, CTranslate2, Triton with vLLM Backend, and TensorRT-LLM. 5 Partial credit is given if the puzzle is not fully solved There is only one attempt Benchmark details. So look in the github llama. 3 and Mistral 7B OpenOrca, but the original version of Mistral 7B OpenOrca was broken (outputting title and commentary after every message and adding Here is a collection of many 70b 2 bit LLMs, quantized with the new quip# inspired approach in llama. For something like the ai2-arc benchmark where the model should output a, b, c, or d answers, is the convention to add a final linear layer to output 4 probabilities and fine tune on the training data, or is it to provide the question context then simply ask it to "say only a, b, c, or d" and evaluate on Here is Dual P100 16gb, but using dolphin-mixtral:8x7b-v2. One month ago I’ve showed a similar idea here on this subreddit. cpp benchmark & more speed on CPU, 7b to 30b, Q2_K, to Q6_K and FP16, X3D, DDR-4000 and DDR-6000 Other TL;DR. My goal was to find out which format and quant to focus on. e. Scripts used to create the benchmarks: Bench script lets you choose the gguf, context, and whether to use rowsplit, flash attention, and kv quant and type. However, benchmarks are also deceptive. llama-2 will have context chopped off and we will only give it the most relevant 3. true. 131K subscribers in the LocalLLaMA community. Baby Samantha is only 22 MB :D During inference, these steering vectors are added at all token positions after the user's prompt with either a positive or negative coefficient, allowing precise control over the degree of the targeted behavior. PHP Llama 2 scored an 8. Would it be possible to do something like this: I put list of models: OpenHermes-2. View community ranking In the Top 5% of largest communities on Reddit. 2 Mixtral 8x7B Instruct: 4. exe --model . Three model sizes available - 7B, 13B, 70B. cpp to test the LLaMA models inference speed of different GPUs on RunPod, 13-inch M1 MacBook Air, 14-inch M1 Max MacBook Pro, M2 Ultra Mac Studio and 16-inch M3 Max MacBook Pro for LLaMA 3. 128k Context Llama 2 Finetunes Using YaRN Interpolation (successor to NTK-aware interpolation) and Flash Attention 2 cool, i have trained RWKV 16k ~ 128k models, do you have a benchmark to share, so i can test these models. It isn’t a perfect benchmark by any means, but I figured it would be a good starting place for some sort of standardized Below it actually says that thanks to (1) 15% less tokens and (2) GQA (vs. Uh, from the benchmarks run from the page linked? Llama 2 70B M3 Max Performance Prompt eval rate comes in at 19 tokens/s. It would probably be better to stick to 30b models with higher precision. Worked with coral cohere , openai s gpt models. But the biggest difference is that its free even for commercial Benchmarks just dropped, it may be worse in certain single turn situations but better in multi-turn, long context conversations. Pretrained on 2 trillion tokens and 4096 context length. 6 Llama-1-70B 3. Llama2 is a GPT, a blank that you'd carve into an end product. The author argues that smaller models, contrary to prior assumptions, scale better with respect to training compute up to an unknown point. sfw: 50% are safe questions that should not trigger any guardrail Subreddit to discuss about Llama, the large language model created by Meta AI. Going off the benchmarks though, this looks like the most well rounded and skill balanced open model yet. Or check it out in the app stores   Subreddit to discuss about Llama, the large language model created by Meta AI. The normal raw llama 13B gave me a speed of 10 tokens/second and llama. Members Online • happensmith. It benchmarks Llama 2 and Mistral v0. 2-3B next? Benchmark different quantization method like AWQ? Suggestions to improve this benchmark are welcome! Let me know your thoughts! 52 comments; share Subreddit to discuss about Llama, the large language model created by Meta AI. 4 tokens/second. Does anyone have any benchmarks to share? At the moment, m2 ultras run 65b at 5 t/s but a dual 4090 set up runs it at 1-2 t/s, which makes the m2 ultra a significant leader over the dual 4090s! Two 4090s can run 65b models at a speed of 20+ tokens/s on either llama. It should perform close to that (the W7900 has 10% less memory bandwidth) so it's an option, but seeing as you can get a 48GB A6000 (Ampere) for about the same price that should both outperform the W7900 and be more widely compatible, you'd probably be better off with the Nvidia card. 5, a model trained on the Open Hermes 2 dataset but with an added ~100k code instructions created by Glaive AI Not only did this code in the dataset improve HumanEval, it also surprisingly improved almost every other benchmark! Get the Reddit app Scan this QR code to download the app now. I don't know how to properly calculate the rope-freq-base when extending, so I took the 8M theta I was using with llama-3-8b-instruct and applied the Subreddit to discuss about Llama, the large language model created by Meta AI. 5B-v2, with GGUF quants here. I profiled it using pytorch profiler with a tensorboard extension (it can also profile vram usage), and then did some stepping through the code in a vscode debugger. Or check it out in the app stores Meta: LLaMA (65B), Llama 2 (7B, 13B, 70B) Mistral AI: Mistral (7B) Mixtral (8x7B) TII/UAE: Falcon (7B, 40B) 01. get reddit premium. 3. You should think of Llama-2-chat as reference application for the blank, not an end product. If you are looking to incorporate SQL in your AI stack, look /r/StableDiffusion is back open after the protest of Reddit killing open API access, which will bankrupt app developers, hamper moderation, and exclude blind users from the site. cpp, huggingface or some other framework? Does llama even support qwen? * Source of Llama 2 tests But, as you've seen in your own test, some factors can really aggravate that, and I also wouldn't be shocked to find that the 13b wins in some regards. 0, but well maybe for the future?) Each card runs at X8 PCI-E 4. 182K subscribers in the LocalLLaMA community. 5 at 3. I use Eval+ in particular adds thousands of test cases to the same 163 problems in HumanEval to cover more edge cases. Llama-2-13B 13. In these benchmarks we only measure if the LLM can get the correct fact, but do not check if the LLM gave a good explanation or if it hallucinated extra content. 0 2. (Info / ^Contact) This actually matters a bit, since llama 1 and 2 7b do not use Grouped Query Attention (GQA) while mistral 7b (and llama 3 8b and 70b) do use it, and it has quite an impact on both training and inference. Mistral-small seems to be well-received in general testing, beyond its performance in benchmarks. (this is visible in the footnotes) llama-2-70b-chat 16. There are 24 questions, some standalone, other follow-ups to previous questions for a multi-turn conversation. The questions in those benchmarks have flaws and are worded in specific ways. In terms of performance, Grok-1 achieved 63. Trained baby lama from scratch on Goethe poems and just MacBook m1 as hardware for the training. rs and spin around the provided samples from library and language docs into question and answer responses that could be used as clean training datasets For my eval: GPT-4 at 4. to. Looks like a better model than llama according to the benchmarks they posted. Commercial and open-source Llama Model. 5k tokens (allowing 512 tokens output). co. Table 1 compares the attributes of the new Llama 2 models with the Llama 1 models 2 trillion tokens Get the Reddit app Scan this QR code to download the app now. cpp, leading the exl2 having higher quality at lower bpw. 7 Claude 3 Sonnet: 7. Namely, we fine-tune LLaMA 7B [36] for 15000 steps using our method. Premium Explore Gaming View community ranking In the Top 5% of largest communities on Reddit. For a quantised llama 70b Are we saying you get 29. The data covers a set of GPUs, from Apple Silicon M series Use llama. Nous-Hermes-Llama-2 13b released, beats previous model on all benchmarks, and is commercially usable. If you wanna try fine-tuning yourself, I would NOT recommend starting with Phi-2 and starting for with something based off llama. Or check it out in the app stores   Many promotional benchmarks don't actually compare to any current GPT-4 models but the legacy version released last year. (Notably, it's much worse than GPT-3. I prefer mistral-7b-openorca over zephyr-7b-alpha and dolphin-2. Every week there's a model claiming to be as good or better than GPT4 with no basis. 2% on the HumanEval coding task and 73% on the popular MMLU benchmark. sfw: 50% are safe questions that should not trigger any guardrail As far as tokens per second on llama-2 13b, it will be really fast, like 30 tokens / second fast (don't quote me on that but all I know is it's REALLY fast on such a slow model). we make Code Llama - Instruct safer by fine-tuning on outputs from Llama 2, including adversarial prompts with safe responses, as well as prompts addressing code-specific risks, we perform evaluations on three widely-used automatic safety benchmarks from the perspectives of truthfulness, toxicity, and bias, respectively. This paper looked at 2 bit-s effect and found the difference between 2 bit, 2. . Members Online airo-llongma-2-13B-16k-GPTQ - 16K long context llama - works in 24GB VRAM The License of WizardLM-2 8x22B and WizardLM-2 7B is Apache2. Currently I have 8x3090 but I use some for training and only 4-6 for serving LLMs. This is a follow-up to my LLM Chat/RP Comparison/Test: Mistral 7B Base + Instruct to take a closer look at the most popular new Mistral-based finetunes. Members Online • and you can train monkeys to do a lot of cool stuff like write my Reddit posts. cpp OpenCL support does not actually effect eval time, so you will need to merge the changes from the pull request if you are using any AMD GPU. It would be interesting to compare Q2. 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). Let it go 1-2 months before we see real world use. The base llama-cpp-python container is already using a GGML model, so I don't see why not. Would love to see this applied to the 30B LLaMA models. 2 Qwen 1. (I'm really not sure), but I would think benchmark scores would differentiate between turbo and non-turbo. 3 billion parameter model, Phi-1. Listed time is just i'm not really using it since my experiments are focused toward flows rather than conversation agents. Tom's Hardware wrote a guide to running LLaMa locally with benchmarks of GPUs. If you don't mind me sharing my benchmarks, for an intel i5-6600k (4. If Microsoft's WizardLM team claims these two models to be almost SOTA, then why did their managers allow them to release it for free, considering that Microsoft has invested into OpenAI? 101 votes, 38 comments. This scaled knowledge transfer not only accelerates training convergence but shows clear boost in Phi-2 benchmark scores" I haven't seen any fine-tunes yet. comments sorted by Best Top New Controversial Q&A Add a Comment. a fully reproducible open source LLM matching Llama 2 70b The TLDR: DZPAS is an adjustment to MMLU benchmark scores that takes into account 3 things: (1) scores artificially boosted due to multiple choice guessing, (2) data contamination, and (3) 0-shot adjustment to more accurately score With only 2. 1. I have TheBloke/VicUnlocked-30B-LoRA-GGML (5_1) running at 7. 5-Mistral-7B, Toppy-7B, OpenHermes-2. If the latter is the case, chances are the model was "overoptimized" for a specific prompt, and probably the results are much less Subreddit to discuss about Llama, the large language model created by Meta AI. A couple of comments here: Note that the medium post doesn't make it clear whether or not the 2-shot setting (like in the PaLM paper) is used. 5 72B Chat: 10. Log In / Sign Up; Advertise on Reddit; Shop Collectible Avatars; Get the Reddit app Scan this QR code to download the app now. 1 across all the popular inference engines out there, this includes TensorRT LLM, vLLM, Llama CPP, CTranslate2, DeepSpeed etc etc. 5 family on 8T tokens (assuming Llama3 isn't coming out for a while). This is the most popular leaderboard, but not sure it can be trusted right now since it's been under Llama 2 70B benches a little better, but it's still behind GPT-3. Though some of the below benchmarks could be pretty good if additional testing confirms their correlation. LocalLLaMA join leave 272,247 readers. Notably, it achieves better performance compared to 25x larger Llama-2-70B The standard benchmarks (ARC, HellaSwag, MMLU etc. XAI then honed the prototype model’s reasoning and coding capabilities to create Grok-1. 5-4. The current llama. You can definitely handle 70b with that rig and from what I've seen other people with M2 max 64gb RAM say, I think you can expect 8/tokens per second, which is as fast Reddit Post Summary: Title: Llama 2 Scaling Laws This Reddit post delves into the Llama 2 paper that explores how AI language models scale in performance at different sizes and training durations. Its possible to use as exl2 models bitrate at different layers are selected according to calibration data, whereas all the layers are the same (3bit for q2_k) in llama. Problem-Solving Skills: In terms of problem-solving capabilities, GPT-4 outshone Llama 2 with a perfect 10 for accurately solving a puzzle, as opposed to Llama 2's score of 6 due to a Ah, didn't realise they published a paper for PaLM 2 as well. 2 and 2-2. 2 GHz, 4 cores, no multithreading), and 32 GB of RAM at 2900MHz, I get these I could not find any other benchmarks like this, so I spent some time crafting a single-prompt benchmark that was extremely difficult for existing LLMs to get a good grade on. According to xAI’s website, Grok-0 boasts comparable performance capabilities to Meta’s Llama 2, despite being half its size. I used LLaMA 33B before as a great compromise between quality and performance, but now with Llama 2 I'm being limited to either 13B (which comes pretty close to LLaMA 33B, but I still notice shortcomings) or 70B Get the Reddit app Scan this QR code to download the app now. Subreddit to discuss about Llama, the large language model created by Meta AI. 353 votes, 125 comments. Below are the results. Hi LocalLlama! I’m working on an open-source IDE extension that makes it easier to code with LLMs. Open comment sort options 240 tokens/s achieved by Groq's custom chips Subreddit to discuss about Llama, the large language model created by Meta AI. 5, and embedding its knowledge within the 2. I also ran some benchmarks, and considering how Instinct cards aren't generally available, I figured that having Radeon 7900 numbers might be of interest for people. I don't see any benchmark results on the llama-2 dolphin 13b huggingface, but it is in the Pending Evaluation Queue for the LLM Leaderboard so the results will probably be out fairly soon. cpp or Exllama. 2 tokens/s, hitting the 24 GB VRAM limit at 58 GPU layers. ) The real star here is the 13B model, which I benchmarked Llama 3. 5 vs Claude-2 at 3. This looks really promising. I understood "batch 1 inference" as just prompting the LLM at the start and getting a result back, vs continuing the conversation. (look at a benchmark), incur more downtime, etc, etc, etc. Salient Features: Llama 2 was trained on 40% more data than LLaMA 1 and has double the context length. View community ranking In the Top 1% of largest communities on Reddit. All anecdotal, but don't judge an LLM by their quantized versions. I can even run fine-tuning with 2048 context length and mini_batch of 2. So I really wouldn't use a rule of thumb that says "use that 13 B q2 instead of the 7B q8" (even if Yes, though MMLU seems to be the most resistant benchmark to "optimization. Benchmark similarity: The prompt->response pattern is central to the benchmarks, so the source of the prompts, and the measured outcome, are really just minor variations on a uniform test suite. However, the primary thing that brings its score down is its refusal to respond to questions that should not be censored. 5% There is a lot of decline in capability that's not quite reflected in the benchmarks. Llama 2 on the other hand is being released as open source right off the bat, is available to the public, and can be used commercially. 1-20B, Noromaid-v1. Anyone got advice on how to do so? Are you using llama. 5 tokens/s. LLaMa 70B tends to hallucinate extra content. Or check it out in the app stores   LLaMA 2 outperforms other open-source models across a variety of benchmarks: MMLU, TriviaQA, HumanEval and more were some of the popular benchmarks used. 161K subscribers in the LocalLLaMA community. " Look at the top 10 models on the Open LLM Leaderboard, then look at their MMLU scores compared to Yi-34B and Qwen-72B, or even just good Llama-2-70B fine-tunes. text-generation-webui (using GPTQ-for-LLaMa): --pre_layer: The number of layers to allocate to the GPU. happen to know of any benchmarks, does it utilize the gpu via mps? curious how much faster an ultra would be /r/StableDiffusion is back open after the protest of Reddit killing open API access, which will bankrupt app developers, hamper moderation r/LlamaModel: Llama 2 and other Llama (model) news, releases, questions and discussion - furry Llama related questions also accepted. Thanks for linking! Nice to see Google is still publishing papers and benchmarks, unlike others that came out after GPT-4 (still waiting for Amazon Titan's, Claude+'s, Pi's, etc). ***Due to reddit API changes which have broken our registration system fundamental to our security model, we are unable to Subreddit to discuss about Llama, the large language model created by Meta AI. Base llama-2 to willy seems like a poor comparison - look at llama-2-chat and see how much mmlu they lose trying to fine tune for Subreddit to discuss about Llama, the large language model created by Meta AI. The training dataset is roughly 2T token and nearly completely unfiltered (besides some personal data which was removed). While Llama 3 8b and 70b are cool, I wish we also had a size for mid-range PCs (where are 13b and 30b versions Meta?). Share Sort by: Best. 1/5 vs GPT-3. It runs the benchmark and dumps it into a text file named wth datestamp Get the Reddit app Scan this QR code to download the app now. It can pull out answers and generate new content from my existing notes most of the time. 6 bit and 3 bit was quite significant. I use two servers, an old Xeon x99 motherboard for training, but I serve LLMs from a BTC mining motherboard and that has 6x PCIe 1x, 32GB of RAM and a i5-11600K CPU, as speed of the bus and CPU has no effect on inference. EVGA Z790 Classified is a good option if you want to go for a modern consumer CPU with 2 air-cooled 4090s, but if you would like to add more GPUs in the future, you might want to look into EPYC and Threadripper motherboards. spooknik/hermes-2-pro-mistral-7b:q6_k 710 314 If I were giving a suggestion to a rando looking to run inference on 65B models, personally, for best bang-per-buck atm, I'd recommend 2 x 3090s for $1500, or 2 x P40s for $400. Or check it out in the app stores   Compromising your overall general performance to reach some very specific benchmark at the expense of most other things you could be capable of. cpp gave almost 20toknes/second. Total 13 + Multiple leaderboard evaluations for Llama 2 are in and overall it seems quite impressive. Or check it out in the app stores   beating the many times larger Llama2-13B, MPT-30B, and Falcon-40B on multiple benchmarks at a fraction of the size New Model github. Action Movies & Series; Animated Movies & Series; Comedy Movies & Series; Crime, Mystery, & Thriller Movies & Series; Documentary Movies & Series; Drama Movies & Series Interesting, in my case it runs with 2048 context, but I might have done a few other things as well — I will check later today. Or check it out in the app stores thanks to some Naysaying, that I won't be able to beat GPT3. Note, both those benchmarks runs are bad in that they don't list quants, context size/token count, or other relevant details. 2 Llama 2 70B Chat: 3. Or check it out in the app stores   what is the best Llama-2 model to run? 7B vs 13B 4bit vs 8bit vs 16bit GPTQ vs GGUF vs bitsandbytes exllamav2 benchmarks. 5 Turbo: 4. cpp discussions for real performance number comparisons (best compared using llama-bench with the old llama2 model, Q4_0 and its A 2-bit quant of a 70b could fit, but that sacrifices a lot of "model intelligence", at least for my use cases. 2:1:1 for 2 layers on GPU 0, 1 layer on GPU 1, and 1 layer on GPU 2. I actually updated the previous post with my reviews of Synthia 7B v1. how to train your own llama from scratch. It's cost prohibitive to do pass10 with gpt4 but with llama it might be something common, maybe using the "gap filling" mode to improve faulty code. Even after a 'uncensored' data set is applied to the two variants, it still resists for example, any kind of dark fantasy story 169 votes, 44 comments. (A single-turn superset benchmark) They confidently released Code Llama 34B just a month ago, so I wonder if this means we'll finally get a better 34B model to use in the form of Llama 2 Long 34B. The eval rate of the response comes in at 8. I tried to do something similar. Also, it will cost a lot less than gpt4 API. 2-2. Here is a post I made about my system with some benchmarks from a few weeks ago, in case you want any more data. From section 4. 6 GPT-3. Big difference between GSM8K correlation in HELM and Open Leaderboard (it seems most of the models that do well at GSM8K are small ones created after GSM8K was added as an Open Leaderboard benchmark hmmm). 0 --seed 42 --mlock --n-gpu-layers 999 Bonus benchmark: 3080Ti alone, offload 28/51 layers (maxed out VRAM again): 7. Why did I choose IFEval? It’s a great Subreddit to discuss about Llama, the large language model created by Meta AI. In terms of reasoning, code, natural language, multilinguality and machines it can run on. Llama 2 on Amazon SageMaker a Benchmark. 3, Claude+ at 3. I tried this Llama-3-11. Hiya, I've been reading the papers for some of the big benchmarks that are out there. I'm downloading the model rn, and have enough It'll be harder than the first one. /llama-2-7b. Someone has linked to this thread from another place on reddit: [r/datascienceproject] Llama2 inference in a single file of pure Mojo (r/MachineLearning) If you follow any of the above links, please respect the rules of reddit and don't vote in the other threads. 5 days to train a Llama 2. So I took the best 70B according to my previous tests, and re-tested that again with various formats and quants. Sort by: /r/StableDiffusion is back open after the protest of Reddit killing open API access, which will bankrupt app developers, hamper moderation I would be interested to use such thing (especially if it's possible to pass custom options to llama. And 2 cheap secondhand 3090s' 65b speed is 15 token/s on There are 2 types of benchmarks I see being used. gfnjy qsyx ensy fsz yxrz qckd xamjifk pgbku nrznmc uultn