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    • Llama 2 benchmarks reddit.

  • Llama 2 benchmarks reddit Worked with coral cohere , openai s gpt models. For groq (mixtral-8x7b-32768) and other OSS models it assumes you have the specific machine like 4*A100 80GB for 70b llama-2 16-bit or 2*A100 80GB for Mixtral and load it up at about 10 concurrent requests at any time. bitsandbytes - arlo-phoenix fork - there are a half dozen forks all in various states, but I found one that seems to fully work and be pretty up-to-date. If you're using llama. Original report: Link I use an A770 but I use the Vulkan backend of llama. You can review the answers and see, e. cpp, use llama-bench for the results - this solves multiple problems. Yes, though MMLU seems to be the most resistant benchmark to "optimization. 8 ts/s using tinyllama FX-8350 at 16. In terms of reasoning, code, natural language, multilinguality and machines it can run on. 2 ts/s using tinyllama GTX 970 at 26. Standardizing on prompt length (which again, has a big effect on performance), and the #1 problem with all the numbers I see, having prompt processing numbers along with inference speeds. This results in the most capable Llama model yet, which supports a 8K context length that doubles the capacity of Llama 2. ikawrakow of llama. 65 ms / 64 runs ( 174. 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. It started off strong with the unicorn question: <s>[INST]How many horns does a two-headed unicorn have?[/INST] A two-headed unicorn would theoretically have two horns, one on each head. Just did a small inference speed benchmark with several deployment frameworks, here are the results: Setup : Ryzen 9 3950X… We would like to show you a description here but the site won’t allow us. Maybe related to Phi-2's partial_rotary_factor? - Phi-2 's rotary_percentage is 40%, so it looks like for Nemotron, only 50% of the Q, K matrices apply RoPE, and the rest don't use RoPE. 174K subscribers in the LocalLLaMA community. Get the Reddit app Scan this QR code to download the app now At the end of the day, what are the benchmarks. Would it be possible to do something like this: I put list of models: OpenHermes-2. cpp and koboldcpp recently made changes to add the flash attention and KV quantization abilities to the P40. But I think you're misunderstanding what I'm saying anyways. 5, set at 128k context with 8-bit cache. RMS Layernorm removes the Was looking through an old thread of mine and found a gem from 4 months ago. (A single-turn superset benchmark) 74 votes, 31 comments. For GPTQ-for-LLaMa: --layers-dist: Distribution of layers across GPUs. XAI then honed the prototype model’s reasoning and coding capabilities to create Grok-1. I think most anyone who has two GPUs knows that inference is slower when split between two GPUs vs one when a single GPU would be enough to run inference. While they aren't 100% reflecting on what you might specifically want, they provide an overall framework on what you might want to try. 25 to 2. 87 ms per Our benchmarks show the tokenizer offers improved token efficiency, yielding up to 15% fewer tokens compared to Llama 2. In only one out of eleven benchmarks does Llama-3-8B outperform Llama-2-70B. But I haven't found any resources that pulled these into a combined overview with explanations. Traditional pre-LLM benchmarks: These are the ones used in NLU or CV in pre-LLM world. Normal layernorm unlike Llama RMS LN. 2 and 2-2. Also happened for me with LLaMA (1) models beyond 2K, like SuperHOT merges, so it's been an issue for a long time. Despite its modest 3 billion parameters, this model is a powerhouse, delivering top-notch results in various tasks. In the context of RAG related evaluations without actual retrieval going on, i found RGB benchmark link, which aims to test LLM by providing noisy or irrelevant context in order to test model's robustness and trustworthiness. We would like to show you a description here but the site won’t allow us. Tied also used in Apple's on device LLM to save VRAM. text-generation-webui (using GPTQ-for-LLaMa): --pre_layer: The number of layers to allocate to the GPU. 0 - if all you need is PyTorch, you're good to go. The benchmark I pay most attention to is needle-in-a-haystack. Its most popular types of products are: We would like to show you a description here but the site won’t allow us. Yeeeep. 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. 7 Mistral/Mixtral/Phi-2, Sonya, TinyLlama) Other Happy New Year! 2023 was the year of local and (semi-)open LLMs, the beginning of a new AI era, and software and models are evolving at an ever increasing pace. 25 tokens/s, 132 tokens, context 48, seed 1610288737) There isn't an EXL2 version with a low enough bpw to fit inside my 4090. Not only that Llama 3 is about to be released in i believe not so distant future which is expected to be on par if not better than mistral so I recently picked up a 7900 XTX card and was updating my AMD GPU guide (now w/ ROCm info). 2% on the HumanEval coding task and 73% on the popular MMLU benchmark. There are 2 main metrics I wanted to test for this model: Throughput (tokens/second) Latency (time it takes to complete one full inference) 101 votes, 38 comments. Total 13 + inference engines and still counting. Followed instructions to answer with just a single letter or more than just a single letter in most cases. 8 8. Pre-requisites: Step 1: Deploy and set up a virtual machine on Azure . Gemma 2 was underperforming on 5 different benchmarks except LMSYS Leaderboard, compared to llama 3 70b. And at the benchmarks of course. Very briefly, this means that you can possibly get some speed increases and fit much larger context sizes into VRAM. cpp benchmarks on various Apple Silicon hardware. So if you train for the best answers on lmsys-chat-1m, you'll get better responses on LMSYS Leaderboard, thus it'll inflate your scores. 1b. PyTorch - works OOTB, you can install Stable (2. - fiddled with libraries. Subreddit to discuss about Llama, the large language model created by Meta AI. 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. openhermes-2. You should think of Llama-2-chat as reference application for the blank, not an end product. 7 or Preview (Nightly) w/ ROCm 6. Q8_0, 59. Untied embeddings like Llama. with full context message at 6k that takes 3 to 5 minutes. 1% overall for the average GPT4ALL Sota score with Hermes-2. I would be interested to use such thing (especially if it's possible to pass custom options to llama. But my first concern appeared when I saw Starling-LM-7B-beta surpass models like Gemini Pro, Yi-34B, GPT-3. I'm also curious about the correct scaling for alpha and compress_pos_emb. LLaMa 70B tends to hallucinate extra content. I thinK it was back in 2015 that GPT 1 or 2 came out, and they weren't releasing it due to ethical concerns. But Llama 3 70B is a very strong contender. Anyone got advice on how to do so? Are you using llama. Note this is not a proper benchmark and I do have other crap running on my machine. true. If I only offload half of the layers using llama. cpp equivalent for 4 bit GPTQ with a group size of 128. For the first time ever we've got a model that's powerful enough to be useful, yet efficient enough to run entirely on edge devices - the privacy implications for this are absolutely huge! So e. 4bpw EXL2 version of Llama-3 that makes it require more memory than any other 70b at the same bpw. Here are the timings for my Macbook Pro with 64GB of ram, using the integrated GPU with llama-2-70b-chat. I still find that Airochronos 33B gives me better / more logical / more constructive results than those two, but it's usually not enough of a difference to warrant the huge speed increase I get from being able to use ExLlama_HF via Ooba, rather than llama. I tried running the 7b-chat-hf variant from meta (fp16) with 2*RTX3060 (2*12GB). I can no longer support this view, as people make ridiculous claims based on this benchmark about LLama-3 8B and 70B surpassing GPT-4. 11 ts/s using nous-hermes2:34b Ryzen 5 1600 at 42. Only 30XX series has NVlink, that apparently image generation can't use multiple GPUs, text-generation supposedly allows 2 GPUs to be used simultaneously, whether you can mix and match Nvidia/AMD, and so on. But hopefully shows you can get pretty usable speeds on an (expensive) consumer machine. +-5 years access to technology is doing pretty good, especially given that patents are typically in the 15 year range. 1 across all the popular inference engines out there, this includes TensorRT LLM, vLLM, Llama CPP, CTranslate2, DeepSpeed etc etc. For summarization and document information extraction it would be Command-R. I was able to load the model shards into both GPUs using "device_map" in AutoModelForCausalLM. 2. Not even ChatGPT gets that one right. 3 21. 78 tokens per second) llama_print_timings: prompt eval time = 11191. cpp or llama. 24 votes, 39 comments. What it means that every time the chat goes to llama. I'm using only 4096 as the sequence length since Llama 2 is naturally 4096. Expect inferencing to be slow, particularly if you want more than 2k context. I am running gemma-2-9b-it using llama. It will be easier for any member to then just have a look at the ranking from the post. Work is being done in llama. 2-2. 5-mistral-7b. When I embed about 400 records, mpnet seems to outperform llama-2 but my gut tells me this is because the larger llama-2 dimensions are significantly diluted to the point that "near" vectors are not relevant. cpp, in itself, obviously. 2 tokens/s We would like to show you a description here but the site won’t allow us. 5 Pro. 5 days to train a Llama 2. There is no direct llama. cpp, huggingface or some other framework? Does llama even support qwen? The current gpt comparison for each Open LLM leaderboard benchmark is: Average - Llama 2 finetunes are nearly equal to gpt 3. And create a pinned post with benchmarks from the rubric testing over the multiple 7B models ranking them over different tasks from the rubric. Hopefully that holds up. Newer LLM benchmarks: New benchmarks are popping up everyday focused on LLM predictions only. I can run 70bq4 at 20-30 second response time with llama cop. This website has benchmarks & comparisons of models & of different host platforms, https://artificialanalysis. cpp is better precisely because of the larger size. Like, for me the benchmarks suggested that Yi-34b models are cool, so I've tried an original one, and then a finetuted one, and so far it works great for me. But it seems like it's not like that anymore, as you mentioned 2 equals 8192. Going off the benchmarks though, this looks like the most well rounded and skill balanced open model yet. Mar 27, 2024 · In this document, one will find the steps to reproduce the results with the model Llama 2 from MLPerf Inference v4. Q4_K_M. 6 ts/s using tinyllama i7-2630QM at 14. g. Disappointing in comparison to Nous Hermes Llama 2 and Mythomax. 6 Was looking through an old thread of mine and found a gem from 4 months ago. Multiple leaderboard evaluations for Llama 2 are in and overall it seems quite impressive. 5 ts/s using dolphin-phi GTX 970 at 60. Now that we have a basic understanding of the optimizations that allow for faster LLM inferencing, let’s take a look at some practical benchmarks for the Llama-2 13B model. 5 Pro has now a huge 2-million token context window (10 books of 600 pages) and new code execution capabilities. This is the most popular leaderboard, but not sure it can be trusted right now since it's been under revision for the past month because apparently both its MMLU and ARC scores are inaccurate. However, seems like this 180K subscribers in the LocalLLaMA community. Hey everyone, I've been testing out Phi-3-mini, Microsoft's new small language model, and I'm blown away by its performance. Mar 27, 2024 · The MLPerf Inference v4. It mostly depends on your ram bandwith, with dual channel ddr4 you should have around 3. The difference between 64% and 68% is just 2 correct answers. Huh that's interesting to know. I’ve been using custom LLaMA 2 7B for a while, and I’m pretty impressed. 3 and Mistral 7B OpenOrca, but the original version of Mistral 7B OpenOrca was broken (outputting title and commentary after every message and adding broken ChatML We would like to show you a description here but the site won’t allow us. 0) w/ ROCm 5. 518 votes, 45 comments. Posted by u/malicious510 - 20 votes and 26 comments Subreddit to discuss about Llama, the large language model created by Meta AI. 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. when not at 4K context of Llama 2 models. 3 ts/s using tinyllama (DDR3 based) Phenom II 955 at 2. I am looking for a 13B llama-2 based GGML model (q4_k_s preferrably) for a simple AI assistant with tweaked personality of my choice (I use oobabooga character chat settings). Nothing extremely hard but I want my AI to be consistent to the context assigned to them while being an AI assistant (ie: tsundere or mischievous personality etc). However the problem surfaces if you are in a chat and your chat is longer than the context size. Not sure of the software support, but you could get 2 brand new cards, 32gb of vram, for what people are frequently recommending buying second hand. I'm a programmer, and if I ask it a programming question, I'm going to get an answer from 2 years ago. 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. ). cpp achieves across the M-series chips and hopefully answer questions of people wondering if they should upgrade or not. I've been having some trouble getting the llama 2 models to do some more complex instruction tasks, I'll have to give the official Chat version a shot. Due to a faulty filter (or so they say) 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. You really do have to make judgement calls based on your use case and general vibes. Llama-2-70B-chat-GGUF Q4_0 with official Llama 2 Chat format: Gave correct answers to only 15/18 multiple choice questions! Often, but not always, acknowledged data input with "OK". 5 ARC - Open source models are still far Within the last 2 months, 5 orthagonal (independent) techniques to improve reasoning which are stackable on top of each other that DO NOT require the increase of model parameters. 🐺🐦‍⬛ LLM Comparison/Test: Brand new models for 2024 (Dolphin 2. I think a 2. LLM Leaderboard - Comparison of GPT-4o, Llama 3, Mistral, Gemini and over 30 models. The problem is that people rating models is usually based on RP. 70B LLaMA-2 benchmarks, the biggest improvement of this model still seems the commercial license (and the increased context size). However, benchmarks are also deceptive. The eval rate of the response comes in at 8. So if i compare the value there, half the price for a new card , 2/3rds of the VRAM, seems a lot better preposition. 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 Here is a sample of QwenTess 2. 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. The bnb devs are actively working on 247 votes, 175 comments. The new benchmarks dropped and shows that Puffin beats Hermes-2 in Winogrande, Arc-E and Hellaswag. It's a work in progress. 5 on mistral 7b q8 and 2. Any model that has more context is infinitely more useful, I had great results from context retrieval tests at 40k+ tokens on Qwen2. I know Open LLM LeaderboardOpen LLM Leaderboard with many models trained on contaminated data but even here I don't see phi medium or new mistral or smaug 70b. 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. But IMO this is a bad benchmark, I think perplexity is a better measurement of model degradation. 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. Which is not as speedy as the A770 can be. I built an AI workstation with 48 GB of VRAM, capable of running LLAMA 2 70b 4bit sufficiently at the price of $1,092 for the total end build. 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. We account for different cost of input and output tokens. It can be useful to compare the performance that llama. cpp gets above 15 t/s. My suggestion is to check benchmarks for the 7900 XTX, or if you are willing to stretch the budget, get a 4090. This is my main point of confusion with this post. llama. Did NOT follow instructions to acknowledge data input with "OK". 5 family on 8T tokens (assuming Llama3 isn't coming out for a while). On linux it would be worse since you are using 2 different environments and pytorch versions. Regarding strange grammar or misspellings, I usually see that with non-standard scaling, e. 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. To get 100t/s on q8 you would need to have 1. The dev also has an A770 and has benchmarks of various GPUs including the A770. 6/2. when MoE becomes the norm, another architecture or format replaces all older models, or Llama 3 releases. 29 seconds (16. As a result, we observed that despite the model having 1B more parameters compared to Llama 2 7B, the improved tokenizer efficiency and GQA Output generated in 2. 5-Mistral-7B, Toppy-7B, OpenHermes-2. As can be expected, faster than Llama3 and Command-R-Plus. 2-1B GGUF quantizations to find the best balance between speed and accuracy using the IFEval dataset. Here is a post I made about my system with some benchmarks from a few weeks ago, in case you want any more data. Table 1 compares the attributes of the new Llama 2 models with the Llama 1 models 2 trillion tokens Gave correct answers to only 2+2+0+0=4/18 multiple choice questions! Just the questions, no previous information, gave correct answers: 3+1+1+6=11/18 Did NOT follow instructions to acknowledge data input with "OK". See full list on github. Most LLM benchmarks today focus on capabilities like understanding, reasoning and Q&A. I have a similar system to yours (but with 2x 4090s). The infographic could use details on multi-GPU arrangements. 5-4. Zero-shot Trivia QA is harder than few-shot HellaSwag, but they are testing the same kinds of behavior. cpp and see what you get first. 12x 70B, 120B, ChatGPT/GPT-4. They give a sense of how the LLMs compare against traditional ML models benchmarked against same dataset. I wasn't aware that metas chat fine-tune was made with RLHF. Card runs quietly and efficiently (backed by 2 comments) Card delivers fast performance for 3d and gpu-intensive work (backed by 2 comments) Users disliked: Product is overpriced for its quality (backed by 1 comment) According to Reddit, PNY is considered a reputable brand. Reaches within 0. 21 seconds (21. Whenever new LLMs come out , I keep seeing different tables with how they score against LLM benchmarks. 😊 Do you like reading books? But subjectively it handles most requests as well as llama-2 34b, as you would expect based on the benchmarks. Anyway, I load up a midnight miqu variant 70b 2. Unfortunately, I can’t use MoE (just because I can’t work with it) and LLaMA 3 (because of prompts). 8 ts/s using tinyllama (2009 cpu lacks AVX/AVX2 DDR3 based) The fact that a 7b model is coming close , so so close to a 70b model is insane, and I'm loving it. You already have the cards and the system, it's just some work to test it. 161K subscribers in the LocalLLaMA community. g. Mistral-small seems to be well-received in general testing, beyond its performance in benchmarks. The questions in those benchmarks have flaws and are worded in specific ways. Llama-index provides a lot of interesting stuff to test RAG pipelines. 5 or LLama-2 70B. 1. cpp, I only get around 2-3 t/s. Comparison and ranking the performance of over 30 AI models (LLMs) across key metrics including quality, price, performance and speed (output speed - tokens per second & latency - TTFT), context window & others. Commercial-scale ML with distributed compute is a skillset best developed using a cloud compute solution, not two 4090s on your desktop. A few weeks ago, I commented that LMSYS is becoming less useful. (Nothing wrong with llama. 5k tokens (allowing 512 tokens output). Benchmarks just dropped, it may be worse in certain single turn situations but better in multi-turn, long context conversations. 2. For example deepeval. 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 We would like to show you a description here but the site won’t allow us. Llama-2-13B 13. Llama 2 (70b) required fine-tuning to beat GPT 3. cpp. cpp at investigating QuIP# and while the 2-bit is impressively small, it has the associated PPL cost you'd expect. 57 ms llama_print_timings: sample time = 229. They often overlook performance on specific nlp tasks like text classification, NER, etc. 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. Note how it's a comparison between it and mistral 7B 0. from_pretrained() and both GPUs memory is almost full (11GB~, 11GB~) which is good. 89 ms / 328 runs ( 0. Expecting to use Llama-2-chat directly is like expecting to sell a code example that came with an SDK. cpp with metal enabled) to test. Obviously, Increases inference compute a lot but you will get better reasoning. I might try running the eval-lm-harness on it after I get it set up, since we have a lot of benchmarks released from meta on llama 2. Uh, from the benchmarks run from the page linked? Llama 2 70B M3 Max Performance Prompt eval rate comes in at 19 tokens/s. This finally compelled me to do some research and put together a list of the 21 most frequently mentioned benchmarks. ai/ (Note: I am a creator of this site - happy to answer any questions regarding methodology, etc. 1-13B We would like to show you a description here but the site won’t allow us. ) HOWEVER, I'm majorly drawn to local for 2 reasons, one of which you hit on: * A) ChatGPT is super out of date. Llama. Google has unveiled major AI advancements by releasing the new Gemma 2 open-source models and several upgrades to Gemini 1. com It benchmarks Llama 2 and Mistral v0. 4bpw 70B compares with 34B quants. Meta, your move. Tried llama-2 7b-13b-70b and variants. It runs the benchmark and dumps it into a text file named wth datestamp Now, I sadly do not know enough about the 7900 XTX to compare. People ask similar questions on lmsys leaderboard. Finally! After a lot of hard work, here it is, my latest (and biggest, considering model sizes) LLM Comparison/Test: This is the long-awaited follow-up to and second part of my previous LLM Comparison/Test: 2x 34B Yi (Dolphin, Nous Capybara) vs. 4 Llama-1-33B 5. 8 on llama 2 13b q8. Ryzen 5 5600X at 2. In certain cases GPT4 did better. 6B format: Gave correct answers to only 3+2+0+1=6/18 multiple choice questions! Just the questions, no previous information, gave correct answers: 0+1+0+2=3/18. You can use this simple formula to find out: books left=books yesterday−books read today In your case, you can plug in the numbers: books left=9−2 books left=7 I hope this helps you understand how to solve this kind of problem. 1 not even the most up to date one, mistral 7B 0. 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. The Brazilian community on Reddit. The perplexity of llama. cpp have it as plug and play. Full offload on 2x 4090s on llama. Also, Group Query Attention (GQA) now has been added to Llama 3 8B as well. According to xAI’s website, Grok-0 boasts comparable performance capabilities to Meta’s Llama 2, despite being half its size. cpp k-quant fame has done a preliminary QuIP# style 2-bit quant and it looks good, and made some test improvements to quant sizes in the process. It's been a month since my last big model comparison/test - so it's high time to post a new one! In the meantime, I've not only made a couple of models myself, but I've also been busy testing a whole lot as well - and I'm now presenting the results to you here: 17 models tested, for a total of 64 models ranked! We would like to show you a description here but the site won’t allow us. Just use the cheapest g. Just ran a few queries in FreeChat (llama. cpp q4_0 should be equivalent to 4 bit GPTQ with a group size of 32. " 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. Considering the 65B LLaMA-1 vs. This is a collection of short llama. 0. So that's probably best for later, e. Members Online Exceptional Mistral 7B 0. Makes you wonder what was even a point in releasing Gemma if it's so underwhelming. So, is Qwen2 7B better than LLaMA 2 7B and Mistral 7B? Also, is LLaVA good for general Q&A surrounding description and text extraction? Nov 22, 2023 · Description. 5 tokens/s. Any remaining layers will be assigned to your last GPU. Good point about having Llama 2 70B as a baseline. 0 round adds Llama 2 70B model as the flagship “larger” LLM for its latest benchmark round. Gemma 2 did exactly this. Gave correct answer but wrong letter once. If you don't have 2x 4090s/3090s, it's too painful to only offload half of your layers to GPU. 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) WizardLM 2 8x22B as a normal assistant. 2:1:1 for 2 layers on GPU 0, 1 layer on GPU 1, and 1 layer on GPU 2. Doesn't entirely follow the guidelines that I set for the scene in question, but the 160b self-merge of CR+ also fails at that. 25bpw and was getting around 35 to 40t/s. Feel free to post in English or Portuguese We would like to show you a description here but the site won’t allow us. This was also discovered with Stable Diffusion 2. There are 2 types of benchmarks I see being used. I agree with both of you - in my recent evaluation of the best models, gpt4-x-vicuna-13B and Wizard-Vicuna-13B-Uncensored tied with GPT4-X-Alpasta-30b (which is a 30B model!) and easily beat all the other 13B and 7B models including WizardLM (censored and uncensored variants), Vicuna (censored and uncensored variants), GPT4All-13B-snoozy, StableVicuna, Llama-13B-SuperCOT, Koala, and Alpaca. ggml: llama_print_timings: load time = 5349. 0 model was so poorly trained that fine-tunes couldn't fix it. Both were still overall outperformed by RoBERTa. Reply reply BitterAd9531 But if you must, I suggest a GGML model with llama Cpp loader (or hf). Aug 9, 2023 · Llama 2 Benchmarks. Did some calculations based on Meta's new AI super clusters. In terms of performance, Grok-1 achieved 63. 39 seconds (12. e. llama-2 will have context chopped off and we will only give it the most relevant 3. 2 base model fine-tuning performance stablelm-2-zephyr-1_6b 4K context, Zephyr 1. When these parameters were introduced back then, it was divided by 2048, so setting it to 2 equaled 4096. 5 in some tasks. As another user mentioned elsewhere there's something different about the 2. GPT4 from SwiftKey keyboard - If you had 9 books yesterday and you read 2 of them today, then you have 7 books left. 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. Q4_K_M, 18. 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). cpp and ask for custom models to be loaded). 0 on the new NC H100 v5 virtual machines. 8-1. 7 tokens/s TinyDolphin-2. 203 votes, 100 comments. . Gemma 2 offers top-tier performance in 9B and 27B sizes, with 27B surpassing Llama-3 70B, while Gemini 1. 182K subscribers in the LocalLLaMA community. MAE is interesting because the model tends to append some extra numbers to the answer. This benchmark is mainly intended for future LLMs with better reasoning (GPT-5, Llama3, etc. The dimensionality of mpnet is 768 and the dim of llama-2-7B is 4096. The smaller model scores look impressive, but I wonder what questions these models are willing to answer, considering that they are so inherently 'aligned' to 'mitigate potentially There are about 8k input tokens and up to 1k output tokens. 5-AshhLimaRP-Mistral-7B, Noromaid-v0. Why did I choose IFEval? It’s a great benchmark for testing how well LLMs follow instructions, which is key for most real-world use cases like chat, QA, and summarization. I got decent stable diffusion results as well, but this build definitely focused on local LLM's, as you could build a much better and cheaper build if you were planning to do fast and only stable However, with some prompt optimization I've wondered how much of a problem this is - even if GPT-4 can be more capable than llama 3 70b, that doesn't mean much of it requires testing a bunch of different prompts just to match and then hopefully beat llama 3 70b, when llama 3 just works on the first try (or at least it often works well enough). It’s been trained on our two recently announced custom-built 24K GPU clusters on over 15T token of data – a training dataset 7x larger than that used for Llama 2, including 4x more code. Also considering enhanced tests, but as soon as I make any change, that would invalidate the old tests and prevent direct comparisons like I can do now. 70 ms per token, 1426. I should have used RMSE to see it better. 1-20B, Noromaid-v1. You have unrealistic expectations. Yeah, I'm interested if any work has been done to evaluate GPTQ for more recent llama models. cpp it needs to be tokenized it cannot use cache. 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. In general I am fan of LMSys but now it has mostly closed models, only open source model in top is Llama 3 now. You'll have to experiment with how many layers you offload to the P40. You can now easily surpass that on low-medium level hardware with basically no restrictions. I haven't finished tested yet, but it has vast and fairly accurate knowledge about both coding any many other things. Hello guys. 71 tokens/s, 55 tokens, context 48, seed 1638855003) Output generated in 6. Gives me hope that eventually huge knowledge models, some even considered to be AGI, could be ran on consumer hardware one day, hell maybe even eventually locally on glasses. Sep 27, 2024 · I benchmarked Llama 3. I actually updated the previous post with my reviews of Synthia 7B v1. 56 tokens/s, 30 tokens, context 48, seed 238935104) Output generated in 3. It requires ROCM to emulate CUDA, tought I think ooba and llama. Llama2 is a GPT, a blank that you'd carve into an end product. That's only on the 50 additions OP provided. Try pure kobold. Gemma tied. lpklfxu lkmvk ralrrmb ifpll esarv iiuwigmq pwonlo khjtib ixvase tyzn