Llama 2 benchmarks reddit 2 Llama 2 70B Chat: 3. The perplexity of llama. The data covers a set of GPUs, from Apple Silicon M series Use llama. Members Online airo-llongma-2-13B-16k-GPTQ - 16K long context llama - works in 24GB VRAM For a quantised llama 70b Are we saying you get 29. 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! 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. The base llama-cpp-python container is already using a GGML model, so I don't see why not. Tried llama-2 7b-13b-70b and variants. 1/5 vs GPT-3. 0 (so equivalent to X16 PCI-E 3. 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. 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. 0, but well maybe for the future?) Each card runs at X8 PCI-E 4. One of the most significant upgrades in Llama 3 is its expanded llama-2 70B used 2 trillion tokens and got 68. cpp is better precisely because of the larger size. Still need to vary some for higher context or bigger sizes, but this is currently my main Llama 2 13B 4K command line: koboldcpp. What would be really neat is to do it with 3 or even 5 different combinations of information to extract for each test. After weeks of waiting, Llama-2 finally dropped. - fiddled with libraries. Valheim Genshin View community ranking In the Top 5% of largest communities on Reddit. Would it be possible to do something like this: I put list of models: OpenHermes-2. But even they've omitted details too (e. 5-mixtral-8x7b model. 1 across all the popular inference engines out there, this includes TensorRT LLM, vLLM, Llama CPP, CTranslate2, DeepSpeed etc etc. "Open Hermes 2. Members Online. Going off the benchmarks though, this looks like the most well rounded and skill balanced open model yet. The smaller model scores look impressive, but I wonder what I benchmarked Llama 3. I have TheBloke/VicUnlocked-30B-LoRA-GGML (5_1) running at 7. Terms & Policies The current gpt comparison for each Open LLM leaderboard benchmark is: Average - Llama 2 finetunes are nearly equal to gpt 3. 7 Claude 3 Sonnet: 7. I want to see someone do a benchmark on the same card with Llama 1 released 7, 13, 33 and 65 billion parameters while Llama 2 has7, 13 and 70 billion parameters; Llama 2 was trained on 40% more data; Llama2 has double the context length; Llama2 was fine tuned for helpfulness and safety; Please review the research paper and model cards (llama 2 model card, llama 1 model card) for more differences. 6 bit and 3 bit was quite significant. 3. 04 MiB) The model I downloaded was a 26gb model but I’m honestly not sure about specifics like format since it was all done through ollama. We use nucleus sampling with p=0. But I think you're misunderstanding what I'm saying anyways. 1 in IFEval (instruction following). It is the dolphin-2. Valheim; Genshin Impact Meta: LLaMA (65B), Llama 2 (7B, 13B, 70B) Mistral AI: Mistral (7B) Mixtral (8x7B) TII/UAE: Falcon (7B, 40B) 01. 5 days to train a Llama 2. Ion_GPT • View community ranking In the Top 10% of largest communities on Reddit. 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. 2. 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 or faster than most people can read. Valheim; Genshin Impact; Subreddit to discuss about Llama, the large language model created by Meta AI. Those were done on exllamav2 exclusively (including the gptq 64g model) and the bpws and their VRAM reqs are (mostly to just load, without taking in mind, the cache and the context): 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. "Tell me the main difference between the sentences 'John plays with his dog at the park. 5, but are decently far Get the Reddit app Scan this QR code to download the app now. Even after a 'uncensored' data set is applied to the two variants, it still resists for example, any kind of dark fantasy story telling ala say, conan or warhammer. View community ranking In the Top 50% of largest communities on Reddit. Did some calculations based on Meta's new AI super clusters. If you found it helpful, let us know with an upvote and a “good bot!” reply and please feel free to Get the Reddit app Scan this QR code to download the app now. I could see 2. exe --model . 4 trillion tokens and got 67. Tom's Hardware wrote a guide to running LLaMa locally with benchmarks of GPUs. It would be interesting to compare Q2. 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). Salient Features: Llama 2 was trained on 40% more data than LLaMA 1 and has double the context length. Additionally, some folks have done slightly less scientific benchmark tests that have shown that 70bs tend to come out on top as well. 60 MiB (model: 25145. Open comment sort options exllamav2 benchmarks. 1 cents per 1000 tokens! And it does pretty much everything my SaaS needs, when I need something more complex I use GPT 4. 5 bits *loads* in 23GB of VRAM, I then entered the same question in Llama 3-8B and it answered correctly on the second attempt. comment sorted by Best Top New Controversial Q&A Add a Comment. cpp, huggingface or some other framework? Does llama even support qwen? As per some benchmarks I've seen, it's slower than the old rtx 6000 at a slightly lower price, so it's a disappointment. The License of WizardLM-2 70B is Llama-2-Community. Subreddit to discuss about Llama, the large language model created by Meta AI. ). Welcome to Reddit's own amateur (ham) radio club. This benchmark is mainly intended for future LLMs with better reasoning (GPT-5, Llama3, etc. Meta, your move. This started as a help & update subreddit for Jack Humbert's company, OLKB (originally Ortholinear Keyboards), but quickly turned into a larger maker community that is DIY in nature, exploring what's possible with hardware, software, and firmware. 3 benchmark results: 165 req/sec NEW RAG benchmark including LLaMa-3 70B and 8B, CommandR, Mistral 8x22b Discussion Curious what people think, open to discussion. To learn more about LLaMA 2 and its capabilities, as well as register to download the model, visit the official LLaMA website. This finally compelled me to do some research and put together a list of the 21 most frequently mentioned benchmarks. 7b for small isolated tasks with AutoNL. Gaming. It can pull out answers and generate new content from my existing notes most of the time. 55 LLama 2 70B to Q2 LLama 2 70B and see just what kind of difference that makes. Llama 2 q4_k_s (70B) performance without GPU . Consider their training data - mostly the equivalent of reddit shitposting. (Info / ^Contact) I am running gemma-2-9b-it using llama. View community ranking In the Top 5% of largest communities on Reddit. So then it makes sense to load balance 4 machines each running 2 cards. Human evaluators rank it slightly *better* than ChatGPT on a range of things (excluding code and reasoning). e. Welcome to Destiny Reddit! This sub is for discussing Bungie's Destiny 2 and its predecessor, Destiny. Llama 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 Llama 2 models are trained on 2 trillion tokens and have double the context length of Llama 1. Reply reply More replies More replies Get the Reddit app Scan this QR code to download the app now. AI: Yi (6B, 34B) Made by WordPress 6. staviq • Additional comment actions Nous-Hermes-Llama-2 13b released, beats previous model on all benchmarks, and is commercially The quants and tests were made on the great airoboros-l2-70b-gpt4-1. 1-20B, Noromaid-v1. Highlights include: A high score of 92. This is pretty great for creating offline, privacy first applications. 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. Performance Benchmarks. Llama 2 on Amazon SageMaker a Benchmark. Llama Chat models have additionally been trained on over 1 million new human annotations. You should think of Llama-2-chat as reference application for the blank, not an end product. /llama-2-7b. exe --blasbatchsize 2048 --contextsize 4096 --highpriority --nommap --ropeconfig 1. But I haven't found any resources that pulled these into a combined overview with explanations. 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. Have you ever pondered how quantization might affect model performance, or what the trade-off is between quantized methods? We know how quantization affects perplexity but how does it affect benchmark performance . cpp and ask for custom models to be loaded). 5 at 3. The Llama2 model is pretty impressive. ***Due to reddit API changes which have broken our registration system fundamental to our security Gemini Pro: 14. 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. Internet Culture (Viral) Amazing Many promotional benchmarks don't actually compare to any current GPT-4 models but the legacy version released last year. LLaMA 2 outperforms other open-source models across a variety of benchmarks: MMLU, TriviaQA, HumanEval and more were some of the popular benchmarks used. " 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. 5 Partial credit is given if the puzzle is not fully solved There is only one attempt allowed per puzzle, 0-shot. Notably, it achieves better performance compared to 25x larger Llama-2-70B model on muti-step reasoning tasks, i. 1-13B Commercial-scale ML with distributed compute is a skillset best developed using a cloud compute solution, not two 4090s on your desktop. 1. Training Data. cpp equivalent for 4 bit GPTQ with a group size of 128. 1 vs Vicuna-13B at 2. Reddit para sa IPASOK MO, BABASAHIN NI ASHERU (IMBA) Posting Members Get a motherboard with at least 2 decently spaced PCIe x16 slots, maybe more if you want to upgrade it in the future. Internet Culture (Viral) Amazing HuggingFace recently did a RAG based LLM benchmark as well for exactly this reason. 0. The current llama. Microsoft is our preferred partner for Llama 2, Meta announces in their press release, and "starting today, tweets or reddit Get the Reddit app Scan this QR code to download the app now. The training data includes: New addition of publicly available online data; 25+ million Since 13B was so impressive I figured I would try a 30B. This release includes model weights and starting code for pre-trained and fine-tuned Llama language models — ranging from 7B to 70B parameters. Three model sizes available - 7B, 13B, 70B. 2% 14 11 50 6 Gemini Pro 10. PHP 8. 0 --seed 42 --mlock --n-gpu-layers 999. Newer LLM benchmarks: New benchmarks are popping up everyday focused on LLM predictions only. Yes, though MMLU seems to be the most resistant benchmark to "optimization. Access is gated via a submit form, and requires acceptance of their terms. Q4_0. Llama 3. This is the most popular leaderboard, but not sure it can be trusted right now since it's been under It benchmarks Llama 2 and Mistral v0. The benchmark I pay most attention to is needle-in-a-haystack. On a 70b parameter model with ~1024 max_sequence_length, repeated generation starts at ~1 tokens/s, and then will go up to 7. 5-Mistral-7B, Toppy-7B, OpenHermes-2. 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. g. 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 Results are presented for 7B, 13B, and 34B models on HumanEval and MBPP benchmarks. Hardware and software maker community based around ortholinear or ergonomic keyboards and QMK firmware. ' /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 Nous Hermes 2 Yi 34B: 1. Pretrained on 2 trillion tokens and 4096 context length. 4. 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). Traditional pre-LLM benchmarks: These are the ones used in NLU or CV in pre-LLM world. Why did I choose IFEval? It’s a great QLoRA finetuning the 1B model uses less than 4GB of VRAM with Unsloth, and is 2x faster than HF+FA2! Inference is also 2x faster, and 10-15% faster for single GPUs than This chart showcases a range of benchmarks for GPU performance while running large language models like LLaMA and Llama-2, using various quantizations. 353 votes, 125 comments. The questions in those benchmarks have flaws and are worded in specific ways. huggingface. Table 1 compares the attributes of the new Llama 2 models with the Llama 1 models 2 trillion tokens llama_new_context_with_model: VRAM scratch buffer: 184. 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. 5k tokens (allowing 512 tokens output). llama-2 will have context chopped off and we will only give it the most relevant 3. 2 tokens/s, hitting the 24 GB VRAM limit at 58 GPU layers. 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. However, the primary thing that brings its score down is its refusal to respond to questions that should not be censored. cpp q4_0 should be equivalent to 4 bit GPTQ with a group size of 32. Compromising your overall general performance to reach some very specific benchmark at the expense of most other things you could be capable of. Hopefully that holds up. ) upvotes · comments r/LocalLLaMA Subreddit to discuss about Llama, the large language model created by Meta AI. ADMIN MOD GPT4/Mistral/Claude3 mini-benchmark . Context Window. 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 It'll be harder than the first one. For my eval: GPT-4 at 4. 6 on MMLU Mistral-7b used 8 Trillion tokens**[*]** and got 64. In this benchmark, we evaluated varying sizes of Llama 2 on a range of Amazon EC2 instance types with different LLaMA 2 outperforms other open-source models across a variety of benchmarks: MMLU, TriviaQA, HumanEval and more were some of the popular benchmarks used. These things are trained to act like your drunk online friend, Get the Reddit app Scan this QR code to download the app now. So I took the best 70B according to my previous tests, and re-tested that again with various formats and quants. * Source of Llama 2 tests. 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 super interesting and AP Workflow 3. 7 billion parameters, Phi-2 surpasses the performance of Mistral and Llama-2 models at 7B and 13B parameters on various aggregated benchmarks. 5% 13 8 46 14 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. Worked with coral cohere , openai s gpt models. 2 Qwen 1. , coding and math. co. 5 HellaSwag - Around 12 models on the leaderboard beat gpt 3. Instruct safer by You have unrealistic expectations. 3, Claude+ at 3. 5 72B Chat: 10. Llama 3, however, steps ahead with 15 trillion tokens, enabling it to respond to more nuanced inputs and generate contextually rich outputs. Llama 2 was trained on 2 trillion tokens, offering a strong foundation for general tasks. 9 and WizardLM at 3. Internet Culture (Viral) Amazing Hello everyone,I'm currently running Llama-2 70b on an A6000 GPU using Exllama, and I'm achieving an average inference speed of 10t/s, with peaks up to 13t/s. You really do have to make judgement calls based on your use case and general vibes. All anecdotal, but don't judge an LLM by their quantized versions. I have a MSI X670E Carbon Wifi, which has 2 PCI-E slots connected directly to the PSU (PCI-E 5. comments sorted by Best Top New Controversial Q&A Add a Comment. 3-2. true. cpp, leading the exl2 having higher quality at lower bpw. Just use the cheapest g. 5 Turbo: 4. Get the Reddit app Scan this QR code to download the app now. 9 on MMLU llam-2 7B used 2 trillion tokens and got 45. Or check it out in the app stores     TOPICS. GPT4 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. 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. 2 for ComfyUI (XY Plot, ControlNet/Control-LoRAs, Fine-tuned SDXL models, SDXL Base+Refiner, ReVision, Detailer, 2 Upscalers, Prompt Builder, etc. Then when you have 8xa100 you can push it to 60 tokens per second. 95. I can even run fine-tuning with 2048 context length and mini_batch of 2. (this is visible in the footnotes) llama-2-70b-chat 16. (#5 of 15 brands on Reddit) USB Flash Drives (#4 of 13 brands on Reddit) RAM (#10 of 19 brands on Reddit) This message was generated by a (very smart) bot. Llama2 is a GPT, a blank that you'd carve into an end product. Premium Explore Gaming. Or check it out in the app stores   that I won't be able to beat GPT3. ADMIN MOD Reproducing LLM benchmarks Discussion I'm running some local benchmarks (currently MMLU and BoolQ) The problem is that people rating models is usually based on RP. 2-1B GGUF quantizations to find the best balance between speed and accuracy using the IFEval dataset. /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. 6 GPT-3. There are 2 types of benchmarks I see being used. Overview Performance Benchmarks. Total 13 + Considering the 65B LLaMA-1 vs. 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. I use Hi LocalLlama! I’m working on an open-source IDE extension that makes it easier to code with LLMs. Llama 3-70B answered correctly on the first attempt only. I would be interested to use such thing (especially if it's possible to pass custom options to llama. So e. 0) Didn't knew about the discussion, gonna go there, thanks. In terms of reasoning, code, natural language, multilinguality and machines it can run on. Competitive models include LLaMA 1, Falcon and MosaicML's MPT model. I run an AI startup and I'm using GPT 3. /r/StableDiffusion is back open after The standard benchmarks (ARC, HellaSwag, MMLU etc. 5/4 in terms of benchmarks or cost. Initial-Image-1015. I have decided to test out three of the latest models - OpenAI's GPT-4, Anthropic's Claude 2, and the newest and open source one, Meta's Llama 2 - by posing a complex prompt analyzing subtle differences between two sentences and Tesla Q2 reports. 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 same ratio to gemma, and suprisingly it works. There are clearly biases in the llama2 original data, from data kept out of the set. You could make it even cheaper using a pure ML cloud computer Ah, didn't realise they published a paper for PaLM 2 as well. The test was done on the u/The-Bloke Quantized Model of the OpenOrca-Platypus2 model, which from their results, would currently be the best 13B model I have been trying to fine-tune Llama 2 (7b) for a couple of days and I just can’t get it to work. They give a sense of how the LLMs compare against traditional ML models benchmarked against same dataset. 56 MiB, context: 440. Was looking through an old thread of mine and found a gem from 4 months ago. Zero-shot Trivia QA is harder than few-shot HellaSwag, but they are testing the same kinds of behavior. This paper looked at 2 bit-s effect and found the difference between 2 bit, 2. SqueezeLLM got strong results for 3 bit, but interestingly decided not to push 2 bit. Here's a short TL;DR on what Meta did to improve the state of the art comments sorted by Best Top New Controversial Q&A Add a Reddit iOS Reddit Android Reddit Premium About Reddit Advertise Blog Careers Press. /r/StableDiffusion is back To provide useful recommendations to companies looking to deploy Llama 2 on Amazon SageMaker with the Hugging Face LLM Inference Container, we created a comprehensive benchmark analyzing over 60 different deployment configurations for Llama 2. Commercial and open-source Llama Model. My goal was to find out which format and quant to focus on. Llama 2 vs Llama 3 – Key Differences . Multiple leaderboard evaluations for Llama 2 are in and overall it seems quite impressive. With only 2. llama. We report Pass@1, Pass@10, and Pass@100 for different temperature values. Small Benchmark: GPT4 vs OpenCodeInterpreter 6. 5bpw models. 70B LLaMA-2 benchmarks, the biggest improvement of this model still seems the commercial license (and the increased context size). 5 for some things, its only . 2 Mixtral 8x7B Instruct: 4. 70B at 2. (2/3) Regarding your specific use cases, I'd like to briefly preview my latest project, which I believe could be of great help (to be shipped within the next two weeks). Whenever new LLMs come out , I keep seeing different tables with how they score against LLM benchmarks. The License of WizardLM-2 8x22B and WizardLM-2 7B is Apache2. However, benchmarks are also deceptive. Mistral-small seems to be well-received in general testing, beyond its performance in benchmarks. gguf --ignore-eos --ctx-size 1024 --n-predict 1024 --threads 10 --random-prompt --color --temp 0. ) are not tuned for evaluating this Evaluation: Llama 2 is the first offline chat model I've tested that is good enough to chat with my docs. small/medium/large instead of the actual parameter count). wywywywy • Additional comment actions 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 vs Claude-2 at 3. 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. 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. With benchmarks like MMLU being separated from real-world quality, we’re hoping that Continue can serve as the easiest 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! edit: as other commenters have mentioned, i was misinformed and turns out the m2 ultra is worse at inference than dual 3090s (and therefore single/ dual 4090s) because it is largely Using 2. 2, and Claude-100k at 3. Valheim; Genshin Impact; what is the best Llama-2 model to run? 7B vs 13B 4bit vs 8bit vs 16bit GPTQ vs GGUF vs bitsandbytes Share Sort by: Best. Can you write your specs CPU Ram and token/s ? comment sorted by Best Top New Controversial Q&A Add a Comment. 5 family on 8T tokens (assuming Llama3 isn't coming out for a while). 5 ARC - Open source models are still far behind gpt 3. 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? Meta just released LLaMA 2, it is the next iteration of LLaMA and comes with a commercial-friendly license. Anyone got advice on how to do so? Are you using llama. Not only did it answer, but it also explained the solution so well that even a complete German beginner could understand. 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. 1. 04 MiB llama_new_context_with_model: total VRAM used: 25585. 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 on how well it works. Weirdly, inference seems to speed up over time. 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. ' and 'At the park, John's dog plays with him. Expecting to use Llama-2-chat directly is like expecting to sell a code example that came with an SDK. LLM360 has released K2 65b, a fully reproducible open source LLM matching Llama 2 70b 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. 0 10000 --stream --unbantokens --useclblast 0 0 --usemlock --model 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 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). 7 tokens/s after a few times regenerating. 6 on MMLU === Given the same number of tokens, larger models perform better Subreddit to discuss about Llama, the large language model created by Meta AI. 1 model. If you are wondering what Amateur Radio is about, it's basically a two way radio service where licensed operators throughout the world experiment and 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. Llama 2 vs ChatGPT . . main. 5-AshhLimaRP-Mistral-7B, Noromaid-v0. 3 on MMLU Chinchilla-70B used 1. Discussion Hi, I had posted three months back a small benchmark comparing some OpenAI and There is a lot of decline in capability that's not quite reflected in the benchmarks. 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. 2. I'm having a similar experience on an RTX-3090 on Windows 11 / WSL. 3 has impressive results across code, math, and multilingual benchmarks. There is no direct llama. 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. It's a pure Python-based low-level API to assist you in assembling LLMs. 3 70B was pretrained on 15 trillion tokens from public sources, 7 times larger than Llama 2’s dataset. This way the accuracy measure would be more representative of any situation, as there may be specific nuances to this specific question and hidden answer and/or the text being used to hide the answer. ugody etovu giuw fonptq nbwq qxtk nryzy abeaoa vzpyere cfqyiko