Llama 2 benchmarks reddit. 128k Context Llama 2 Finetunes Using YaRN Interpolation .
- Llama 2 benchmarks reddit They give a sense of how the LLMs compare against traditional ML models benchmarked against same dataset. 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. (A single-turn superset benchmark) Llama. Total 13 + I benchmarked Llama 3. 5 The current gpt comparison for each Open LLM leaderboard benchmark is: Average - Llama 2 finetunes are nearly equal to gpt 3. We follow the exactly same preprocessing steps and training hyperparameters as the original LLaMA paper, including model architecture, context length, training steps, learning rate schedule, and optimizer. Subreddit to discuss about Llama, the large language model created by Meta AI. ikawrakow of llama. My favorite models have occupied the lower midrange of the scale -- 11B, 13B, and 20B. * Additionally, some folks have done slightly less scientific benchmark tests that have shown that 70bs tend to come out on top as well. cpp, huggingface or some other framework? Does llama even support qwen? Commercial-scale ML with distributed compute is a skillset best developed using a cloud compute solution, not two 4090s on your desktop. twitter. HuggingFace recently did a RAG based LLM benchmark as well for exactly this reason. r/LocalLLaMA A chip A close button. get reddit premium. Did some calculations based on Meta's new AI super clusters. If you really wanna use Phi-2, you can use the URIAL method. cpp(1900) 2*3090 12. Also, if you want to Llama 2 models were trained with a 4k context window, happen to know of any benchmarks, /r/StableDiffusion is back open after the protest of Reddit killing open API access, which will bankrupt app developers, hamper moderation, Hi LocalLlama! I’m working on an open-source IDE extension that makes it easier to code with LLMs. Mistral-small seems to be well-received in general testing, beyond its performance in benchmarks. More info: Maybe the turbo version is a quantized version of gpt4 (I'm really not sure), but I would think benchmark scores would differentiate between turbo and non-turbo. Also, Group Query Attention (GQA) now has been added to Llama 3 8B as well. But the biggest difference is that its free even for commercial usage. No matter how good someone can make a 7B model, it’s not going to give you perfect code or instructions and you will waste more time debugging it than it would have taken you to learn how to write the code. Expand user menu Open settings ARC PRIZE ARC Prize is a $1,000,000+ public competition to beat and open source a solution to the ARC-AGI benchmark. Not only does it do well in benchmarks, but also in unmeasured capabilities, like Roleplaying, Tasks, and more. 5 vs Claude-2 at 3. For my eval: GPT-4 at 4. Any advise? LLaMA 2 outperforms other open-source models across a variety of benchmarks: MMLU, TriviaQA, HumanEval and more were some of the popular benchmarks used. 5 and StarlingLM. Or they just have bad reading Like many of you, I've been very confused on how much quality I'm giving up for a certain quant and decided to create a benchmark to specifically test for this. Llama 2 vs ChatGPT . Or check it out in the I established on another thread, thanks to some Naysaying, that I won't be able to beat GPT3. From section 4. 7 Claude 3 Sonnet: 7. fr) and while ChatGPT is able to follow the instructions perfectly in German Is Llama2 I prefer mistral-7b-openorca over zephyr-7b-alpha and dolphin-2. Which is not as speedy as the A770 can be. It's this Reddit post's title that was super misleading. 5 as well as Llama 2 13b models to do zero shot classification on each sentence and find which categories 'Choose your own adventure' templates, each carrying on to the next Come here for discussion and fanworks from the reddit Jumpers! Members Online. For Llama 2, there are already quantized models uploaded on hf that you can use. Time The home of the simracing community on Reddit. View community ranking In the Top 5% of largest communities on Reddit. The fine-tuned model, Llama Chat, leverages publicly available instruction datasets and over 1 million human annotations. The License of WizardLM-2 70B is Llama-2-Community. Notably, it achieves better performance compared to 25x larger Llama-2-70B We recently integrated Llama 2 into Khoj. However, benchmarks are also deceptive. LLaMA 2 outperforms other open-source models across a variety of benchmarks: MMLU, TriviaQA, HumanEval and more were some of the popular benchmarks used. I'm downloading the model rn, and have enough vram to run the full fp16 version. My goal was to find out which format and quant to focus on. Someone just reported 23. 5 bits *loads* in 23GB of VRAM, 45 votes, 28 comments. 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. 2's text generation still seems better Groq achieves 240 tokens per second per user for LLM, Llama-2 70B. Would love to see this applied to the 30B LLaMA models. If you don’t know how to code, I would really recommend working with GPT4 to help you. Reply reply Adaptable: Built on the same architecture and tokenizer as Llama 2, TinyLlama seamlessly integrates with many open-source projects designed for Llama. https: While that was on Llama 1, you can also see similarly with folks doing Llama2 perplexity tests. codm be like: a fully reproducible open source LLM matching Llama 2 70b 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. cpp and koboldcpp recently made changes to add the flash attention and KV quantization abilities to the P40. 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. Benchmarks + Optimization Trick 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. Hi there, just an small post of appreciation to exllama, which have some speeds I NEVER expected to see. 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. 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 I asked ChatGPT3. 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 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 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 Get the Reddit app Scan this QR code to download the app now. Not only did it answer, but it also explained the solution so well that even a complete German beginner could understand. 1M subscribers in the singularity community. Llama 2 on Amazon SageMaker a Benchmark. It’s good, on my 16gb m1 I can run 7b models easily and 13b models useably Subreddit to discuss about Llama, the large language navigation Go to Reddit Home. Benchmarks several benchmarks got worse vs base llama2 this is not a slam dunk here. The new benchmarks dropped and shows that Puffin beats Hermes-2 in Winogrande, Arc-E and Hellaswag. Yea. Log In / Sign Up; Advertise on Reddit; LLaMA (65B), Llama 2 (7B, 13B, 70B) Mistral AI: Mistral (7B) Mixtral (8x7B) WordPress 6. "Open Hermes 2. LLaMA-13B outperforms GPT-3 (175B) on most benchmarks, and LLaMA-65B Your comment suggests a misunderstanding in what MoE models are made for. But this here is only inspired on it. Log In / Sign Up; what is the best Llama-2 model to run? 7B vs 13B 4bit vs 8bit vs 16bit GPTQ vs GGUF vs bitsandbytes exllamav2 benchmarks. 527 - 0. Really need a robust benchmark for all these models comparing to GPT4. 1-mistral-7b Between this three zephyr-7b-alpha is last in my tests, but still unbelievable good for 7b. cpp benchmarks on various Apple Silicon hardware. 1% overall for the average GPT4ALL Sota score with Hermes-2. While the benchmarks then to be cheated, especially by small models, I honestly think something is wrong with how you run it. cpp at investigating QuIP# and while the 2-bit is impressively small, it has the associated PPL cost you'd expect. DeepSeek V3 has 850% more total parameters than Qwen 2. There are already some existing tests like WolframRavenwolf's, and oobabooga's The problem is that people rating models is usually based on RP. Yi-34B trades blows with Lllama 2 70B from my personal tests, making it do novel tasks invented by me, not the gamed benchmarks. WizardLM-2 8x22B is just slightly falling behind GPT-4-1106-preview WizardLM-2 70B is better than GPT4-0613 The License of WizardLM-2 8x22B and WizardLM-2 7B is Apache2. I assume there isn't a convenient webui for chumps to run benchmarks with (apart from ooba perplexity, which I assume isn't the same thing?). ) are not tuned for evaluating this I want to see if some presets and custom modifications work well in benchmarks, but running HellaSwag or MMLU looks too complicated for me, and it takes 10+ hours to upload 20GBs of data. 0. 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). For casual, single card use I wouldn't recommend one. We train our models on the RedPajama dataset released by Together, which is a reproduction of the LLaMA training dataset containing over 1. 1/5 vs GPT-3. But then again Claude 2 got 71. 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. With 0. Please use our Discord server instead of supporting a company that acts against its users and unpaid I am running a LLaMA 2 7b model on a AWS Sagemaker instance. " 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. With benchmarks like MMLU being separated from real-world quality, we’re hoping that Continue can serve as the easiest Get the Reddit app Scan this Extensive LLama. 5 ARC - Open source models are still far 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. I only got 70 tok/s on 1 card using a 7b model (albiet at MLC's release, not recently so performance has probably improved) and 3090 TI benchmarks around that time were getting 130+. Meta, your move. As a result, we observed that despite the model having 1B more parameters compared to Llama 2 7B, the improved tokenizer efficiency and GQA contribute to maintaining Going off the benchmarks though, this looks like the most well rounded and skill balanced open model yet. 5 72B Chat: 10. With the ctx_len at 32K, fully used, that'd be around 43GB of There is a lot of decline in capability that's not quite reflected in the benchmarks. 9 and WizardLM at 3. Anyone got advice on how to do so? Are you using llama. Reaches within 0. 6 Using 2. 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. They represent an excellent tradeoff between capability and performance. LLM360 has released K2 65b, a fully reproducible open source LLM matching Llama 2 70b Work is being done in llama. Note how the llama paper quoted in the other reply says Q8(!) is better than the full size lower model. 2-3B next?. The base llama-cpp-python container is already using a GGML model, so I don't see why not. 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. 2% on the HumanEval coding task and 73% on the popular MMLU benchmark. Or check it out in the (Same applies to the viceversa process). 5/4 in terms of benchmarks or cost. Premium Explore View community ranking In the Top 5% of largest communities on Reddit. 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 They pre-trained the llama 2 base - which is pretty decent so far. $11K - Rackmount M2 Ultra Mac Pro w/ 192GB RAM / View community ranking In the Top 10% of largest communities on Reddit. 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. look at llama-2-chat and see how much mmlu they lose trying to fine tune for interaction. PHP 8. g. View community ranking In the Top 1% of largest communities on Reddit [N] Llama 2 is here. Can GPUs still catch up? Groq currently outperforms the competition in #AI & #ML, including Nvidia. I can even run fine-tuning with 2048 context length and mini_batch of 2. You can review the answers and see, e. 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. A few BramVanroy/Llama-2-13b-chat-dutch · Hugging Face Here it is: NewHope creators say benchmark results where leaked into the dataset, Get app Get the Reddit app Log In Log in to Reddit. 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. You can check the score anyways on the first benchmark in the pastebin at the end of this post GPT-3. Get app Get the Reddit app Log In Log in to Reddit. If you ask them about most basic stuff like about some not so famous celebs model would just halucinate and said something without any sense. 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. 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. 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? Skip to main content Open menu Open navigation Go to Reddit Home I agree with this sentiment, even though L3-8B doesn't really outperform L2-70B outside of benchmarks. 128k Context Llama 2 Finetunes Using YaRN Interpolation cool, i have trained RWKV 16k ~ 128k models, do you have a benchmark to share, /r/StableDiffusion is back open after the protest of Reddit killing open API access, which will bankrupt app developers, Get app Get the Reddit app Log In Log in to Reddit. 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. You could make it even cheaper using a pure ML cloud computer Since 13B was so impressive I figured I would try a 30B. 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. 78 ms / 520 runs ( 0. New RAG benchmark with Claude 3, Gemini Pro, MistralAI vs 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. 2 Llama 2 70B Chat: 3. 0 and it starts looping after 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. Dearest u/faldore, . I need the model just for providing me summaries of long documents, and i am using Langchain to do a map reduce on the data and get a summary of it. 6 bit and 3 bit was quite significant. 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. TABLE HERE Main thing is that Llama 3 8B instruct is trained on massive amount of information,and it posess huge knowledge about almost anything you can imagine,while in the same time this 13B Llama 2 mature models dont. 5 Nous Hermes 2 Yi 34B: 1. If we don't count the coherence of what the AI generates (meaning we assume what it writes is instantly good, no need to regenerate), 2 T/s is the bare minimum I tolerate, because less than that means I could write the stuff faster myself. It can be useful to compare the performance that llama. If you wanna try fine-tuning yourself, I would NOT recommend starting with Phi-2 and starting for with something based off llama. StabilityAI released FreeWilly2. If you don't mind me sharing my benchmarks, for an intel i5-6600k (4. 4B claims performance comparable to the thrice larger Mistral-7B, beating the many times larger Llama2-13B, MPT-30B, and Falcon-40B on multiple benchmarks at a fraction of the size With only 2. 3, Claude+ at 3. 2 10. I recently picked up a 7900 XTX card and was updating my AMD GPU guide (now w/ ROCm info). 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. You can also track the training loss here:🔗 Track Our Live Progress. cpp, leading the exl2 having higher quality at lower bpw. Yes, though MMLU seems to be the most resistant benchmark to "optimization. 4 trillion tokens and got 67. 3-2. Any recommendations older than 1 month should be mostly disregarded anyway, although my advice of looking at benchmarks stands. dev. Competitive models include LLaMA 1, Falcon and MosaicML's MPT model. In terms of performance, Grok-1 achieved 63. Three model sizes available - 7B, 13B, 70B. I understood "batch 1 inference" as just prompting the LLM at the start and getting a result back, vs continuing the conversation. GPT4 from SwiftKey keyboard - If you had 9 books yesterday and you read 2 of them today, then you have 7 books left. This is the most comprehensive fully open-source benchmark to date. LocalLLaMA join leave 272,247 readers. Or check it out in the I want to see someone do a benchmark on the same card with both vLLM & TGI to see how much throughput can be achieved with multiple instances of TGI running 1. 488 - 0. * Sigh, fine! I guess it's my turn to ask u/faldore to uncensor it: . I run an AI startup and I'm using GPT 3. 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). But I haven't found any resources that pulled these into a combined overview with explanations. 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 Get the Reddit app Scan this QR code to download the app now. cpp. llama-2 will have context chopped off and we will only give it the most GO items. wywywywy • Additional Subreddit to discuss about Llama, the large language model created by Meta AI. to's best submissions. Unlike Llama-2, this model doesn't use efficient KV-cache (doesn't re-use key heads), so the memory use scales much more with context length (it uses 8x Llama's memory for the same length). Our benchmarks show the tokenizer offers improved token efficiency, yielding up to 15% fewer tokens compared to Llama 2. Whenever new LLMs come out , I keep seeing different tables with how they score against LLM benchmarks. You can use this simple formula to find out: books left=books yesterday−books read today In your case, you can plug Saw this tweet by Karpathy explaining how LLMs are fast enough on local machines because "we" are very interested in batch 1 inference. Try running it with temperatures below 0. Here is Dual P100 16gb, but using dolphin-mixtral:8x7b-v2. However, the primary thing that brings its score down is its refusal to respond to questions Ah, didn't realise they published a paper for PaLM 2 as well. Makes llama 2 feel like alpha pre-release And create a pinned post with benchmarks from the rubric testing over the multiple 7B models ranking them over different tasks from the rubric. Worked with coral cohere , openai s gpt models. 70B at 2. 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. cpp on GPU via Metal, some folks seem to have llama-2-70b-chat. 2 on HumanEval but did the best on the new benchmark, so I don't know if the much larger context helped out or what. 55 LLama 2 70B to Q2 LLama 2 70B and see just what kind of difference that makes. Log In / Sign Up; Many promotional benchmarks don't actually compare to any current GPT-4 models but the legacy version released last year. All anecdotal, but don't judge an LLM by their quantized versions. 8sec I use an A770 but I use the Vulkan backend of llama. llama-2 But subjectively it handles most requests as well as llama-2 34b, as you would expect based on the benchmarks. I love the idea, but there's some work to be done in the actual implementation from my perspective - a good chunk of this behavior is, as you say, just because of how the different models tokenize - llama3 has a 'vocabulary' of 128k different tokens, gemma 256k, mistral 32k - some of these tests just really aren't going to suit an LLM and are more like regex patterns. - fiddled with libraries. How does this compare to NVidia high-end GPUs? Discussion Share but this LLM benchmark is _at best Note: Reddit is dying due to terrible leadership from CEO /u/spez. Expand user menu Open settings menu. Get the Reddit app Scan this QR code to download the app now. Llama 2 q4_k_s (70B) performance without GPU . 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. I actually updated the previous post with my reviews of Synthia 7B v1. Sort by: Reply reply Version467 • It's not that far behind Llama 2 70B, which is just wild. 9 on MMLU llam-2 7B used 2 trillion tokens and got 45. 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! This paper looked at 2 bit-s effect and found the difference between 2 bit, 2. Are you getting this just for inferencing (eg, "getting an A100 to test some more models") or do you want to do training as well? I think that the biggest weakness of the Mac atm, is that you're going to be fighting upstream there and you won't be able to use a lot of the standard libs (anything with custom CUDA kernels). cpp achieves across the M Multiple NVIDIA GPUs or Apple Silicon for Large Language Model Inference? 🧐. And I like to show that even the super-censored Llama 2 Chat model can be uncensored with a good prompt and character card, and that there's a lot of NSFW content behind its We need more evals of the new 2 bit methods. Get support, learn new information, and hang out in the subreddit dedicated to Pixel, Nest, Chromecast, the Assistant, Throughput of MI300X on LLama 2 70B. 2 tokens/s, hitting the 24 GB VRAM limit at 58 GPU layers. 6 on MMLU === Given the View community ranking In the Top 50% of largest communities on Reddit. 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 . 5bpw models. GPT-4’s performance is debated, with some saying it has declined and others denying it. I then entered the same question in Llama 3-8B and it answered correctly on the second attempt. and directly support Reddit. View community ranking In the Top 1% of largest communities on Reddit. The standard benchmarks (ARC, HellaSwag, MMLU etc. Use llama. 34 ms sample time = 37. 2 Qwen 1. Tried llama-2 7b-13b-70b and variants. 2 GHz, 4 cores, no multithreading), Get app Get the Reddit app Log In Log in to Reddit. 637). Just use the cheapest g. Q6_K. 2-1B GGUF quantizations to find the best balance between speed and accuracy using the IFEval dataset. It would be interesting to compare Q2. After weeks of waiting, Llama-2 finally dropped. 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 MiniCPM-2. Using Anthropic's ratio (100K tokens = 75k words), it means I write 2 tokens per second. that's the more reasonable comparison, View community ranking In the Top 5% of largest communities on Reddit. ADMIN MOD Reproducing LLM benchmarks Discussion I'm running some local benchmarks (currently MMLU and BoolQ) on a variety of models. gguf: load time = 68357. 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 (which is even slower than 65B was and so for me not an option right now on my puny laptop). The current llama. Llama 3-70B answered correctly on the first attempt only. We hope the benchmark will help companies deploy Llama 2 optimally based on their needs. So I took the best 70B according to my previous tests, and re-tested that again with various formats and quants. Or check it out in and LLama2 (Llama 2 70B online demo (stablediffusion. Reddit para sa IPASOK MO, BABASAHIN NI ASHERU (IMBA) Posting Members Online. Dutch Llama 2 13b chat. Or check it out in the app stores Subreddit to discuss about Llama, Members Online • Initial-Image-1015. Hopefully that holds up. Mistral 7B Beats Llama 2 13B on All Benchmarks. I have no idea how to interpret this! On HELM (which I claimed above should be most similar to chat), not only does llama-65b beat falcon-40b, but even llama-30b slightly edges out falcon-40b! That's a Llama 2 was pretrained on publicly available online data sources. 07 ms per token, 13763. 636 GBps), while it This looks really promising. . We trust this letter finds you in the pinnacle of your health and good spirits. 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). Compromising your overall general performance to reach some very specific benchmark at the expense of most other things you could be capable of. Salient Features: Llama 2 was trained on 40% more data than LLaMA 1 and has double the context length. 5 Partial credit is given if the puzzle is not fully solved There is only one attempt allowed per puzzle, 0-shot. Why did I choose IFEval? It’s a great Llama 2, a new AI model that beats others on benchmarks, is released with usage limits. 5 family on 8T tokens (assuming Llama3 isn't coming out for a while). Llama-2-70b-Guanaco-QLoRA becomes the first model on the Open LLM Leaderboard to beat gpt3. LLaMa 70B tends to hallucinate extra content. And it has had support for WizardLM-13B-V1. I wanted to share a short real-world evaluation of using Llama 2 for the chat with docs use-cases and hear which models have worked best for you all. Hello everyone,I'm currently running Llama-2 70b on an A6000 GPU using Exllama, Get the Reddit app Scan this QR code to download the app now. Last, Llama 2 performed incredibly well on this open leaderboard. navigation Go to Reddit Home. Sort by: /r/StableDiffusion is back open after the protest of Reddit killing open API access, which will bankrupt app developers, I benchmarked Llama 3. Good Progress: Check out our intermediate checkpoints and their comparisons with baseline Pythia in our github. The home of MacroFactor on Reddit Members Online. 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). Advertisement View community ranking In the Top 20% of largest communities on Reddit. 7 billion parameters, Phi-2 surpasses the performance of Mistral and Llama-2 models at 7B and 13B parameters on various aggregated benchmarks. Newer LLM benchmarks: New benchmarks are popping up everyday focused on LLM predictions only. Get a motherboard with at least 2 decently spaced PCIe x16 slots, maybe more if you want to upgrade it in the future. 571) while llama-65b varies much more (0. New models have been released that perform better for all of these categories. 142 votes, 82 comments. 2. 169K subscribers in the LocalLLaMA community. Benchmarks just dropped, it may be worse in certain single turn situations but better in multi-turn, long context conversations. 2 trillion tokens. It's a pure Python-based low-level API to assist you in assembling LLMs. 328 users here now. Pretrained on 2 trillion tokens and 4096 context length. 4K subscribers in the DevTo community. Human evaluators rank it slightly *better* than ChatGPT on a range of things (excluding Getting Nah, was just curious how my "assistant" would answer. A mirror of dev. Commercial and open-source Llama Model. This finally compelled me to do some research and put together a list of the 21 most frequently mentioned benchmarks. 2 on Apple Silicon macs with >= 16GB of RAM for a while now. Can you Nous-Hermes-Llama-2 13b released, beats previous model on all benchmarks, and is commercially usable. The questions in those benchmarks have flaws and are worded in specific ways. 3t/s a llama-30b on a 7900XTX w/ exllama. It far surpassed the other models in 7B and 13B and if the leaderboard ever tests 70B (or 33B if it is released) it seems It benchmarks Llama 2 and Mistral v0. 5 at 3. Then they fine-tuned a "demo" Llama chat to showcase the capabilities of the model and because this is a huge public company, they baked in a lot of ethics to avoid 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 I am currently quantizing LLaMA-65B, 30B and 13B | logs and 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. Log LLaMa. 9 10. falcon-40b's scores across the different eval methods are relatively stable (0. I end up having a hard time understanding how good or bad the new LLaMA alternatives are, or how they compare to OpenAI's models. 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. You really do have to make judgement calls I tried to run some benchmarks based on what I could dig up and edited my post with them (and the disclaimers around them) -- TLDR, I get around 73% of my M1 Max 64GB's 400GB/s with llama. 2, and Claude-100k at 3. comments sorted by Best Top New Controversial Q&A Add a Comment. From quip#, you can see that 2 bit can be more competitive than once thought. OpenAI This chart showcases a range of benchmarks for GPU performance while running large language models like LLaMA and Llama-2, using various quantizations. 5 days to train a Llama 2. llama-2 70B used 2 trillion tokens and got 68. I just released an update to my macOS app to replace the 7B Llama2-Uncensored base model with Mistral-7B-OpenOrca. I think the paper I linked they only had the Claude 2 web version so if this new paper used the API that could also be a big difference. Log In / Sign Up; Llama 3 benchmark is out 🦙🦙 News Share Add a Comment. As we sit down to pen these very words upon the parchment before us, we are I spent half a day conducting a benchmark test of the 65B model on some of the most powerful GPUs Open menu Open navigation Go to Reddit Home. 17 tokens The (un)official home of #teampixel and the #madebygoogle lineup on Reddit. Looks like a better model than llama according to the benchmarks they posted. 5 for It allows to run Llama 2 70B on 8 x Raspberry Pi 4B 4. 2 Mixtral 8x7B Instruct: 4. Instead I have been trying to fine-tune Llama 2 (7b) for a couple of days and I just can’t get it to work. I've used OpenChat a fair bit and I know that it's pretty good at answering coding-related questions, especially for a 7B model. It's a work in progress. 5 Turbo: 4. So a big increase in capabilities, while This is a collection of short llama. (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). Namely, we fine-tune LLaMA 7B [36] for 15000 steps using our method. Do you run generic benchmarks on each model first, and then try to see if the final merged model is doing similar or better? /r/StableDiffusion is back open after the protest of Reddit killing open API access, which will bankrupt app developers, Zero-Trust AI APIs for Llama 2 70b Integration 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. What would be really neat is to do it with 3 or even 5 different combinations of information to extract for each test. 5-turbo-instruct, PaLM-2-bison, mistral-7b, Solar-70b, Llama-2-70b and Human01 (myself) 2. 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. If you are looking to incorporate SQL in your AI stack, look Get app Get the Reddit app Log In Log in to Reddit. Log In / Sign Up; Advertise on Reddit; P40 benchmarks, Part 2: 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. You look at the perplexity or benchmark scores, compare with the original, and pick the smallest model that does not deviate "much" in scores. I want to know if there's a better way to do this or if you could share your personal experiences on summarizing efficiently. 1 across all the popular inference engines out there, this includes TensorRT LLM, vLLM, Llama CPP, CTranslate2, DeepSpeed etc etc. 3 and Mistral 7B OpenOrca, but the original version of Mistral 7B OpenOrca was broken (outputting title and commentary after every message and adding Hiya, I've been reading the papers for some of the big benchmarks that are out there. 3. Subreddit to discuss about Llama, is there an easy way to run all the typical benchmarks I see models being tested against? Share Add a Comment. SqueezeLLM got strong results for 3 bit, but interestingly decided not to push 2 bit. ***Due to reddit API changes which have broken our registration system fundamental to our security model, we are unable to accept new user registrations until reddit According to xAI’s website, Grok-0 boasts comparable performance capabilities to Meta’s Llama 2, despite being half its size. Tom's Hardware wrote a guide to running LLaMa locally with benchmarks of GPUs. 1 vs Vicuna-13B at 2. Gemini Pro: 14. cpp github, there are some empirical investigations that suggest this method is comparable with quip# quality, but we need more comparisions. 8 bit! That's a size most of us probably haven't even tried. So I've tried to use a basic matrix factorization method to estimate unknown benchmark scores for models based on the known benchmark scores. I have TheBloke/VicUnlocked-30B-LoRA-GGML (5_1) running at 7. 5 72B, but it actually has 50% less activated parameters. 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. Hi. In terms of reasoning, code, natural language, multilinguality and machines it can run on. Should I benchmark Llama 3. Was looking through an old thread of mine and found a gem from 4 months ago. r/LlamaModel: Llama 2 and other Llama (model) news, releases, Get the Reddit app Looks like a better model than llama according to the benchmarks they posted. 6 GPT-3. Interview with Jonathan Ross, CEO of @GroqInc . true. Join our new [Discord](https 83 votes, 10 comments. There are 2 types of benchmarks I see being used. 3 on MMLU Chinchilla-70B used 1. /r/StableDiffusion is back open after the protest of Reddit killing open API access, which will bankrupt app developers, NEW RAG benchmark including LLaMa-3 70B and 8B, CommandR, Mistral 8x22b So e. True, they don't benchmark GPT 4, only open models But they don't use GPT-4 to benchmark the open models; they use standard LLM benchmarks like TruthfulQA which have human labelled answers, and they check that the answer matches (granted, there are still some language models involved in the checking process, but having ground truth human answers makes it much more 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. So I really wouldn't use a rule of thumb that says "use that 13 B q2 instead of the 7B q8" (even if Edit: 2 months later, this is outdated. to. Subjectively speaking, Mistral-7B-OpenOrca is waay better than Luna-AI-Llama2-Uncensored, but WizardLM-13B-V1. Mistral 7B is a better model than Llama 2 7B. XAI then honed the prototype model’s reasoning and coding capabilities to create Grok-1. The data covers a set of GPUs, from Apple Silicon M series The LLaMA original release came in four sizes: 7B, 13B, 30B, and 65B, and demonstrated outstanding performance on various benchmarks. The Llama2 model is pretty impressive. Traditional pre-LLM benchmarks: These are the ones used in NLU or CV in pre-LLM world. He could probably benchmark it by using a variety of models in a variety of sizes and using the same prompt see how many tokens per second it generates and how long it takes to generate the text, but I don't know if that would be good enough since you'd have to be set to a low temperature to get reliable responses. 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. 3 benchmark results: View community ranking In the Top 5% of largest communities on Reddit. 6 on MMLU Mistral-7b used 8 Trillion tokens**[*]** and got 64. And there are many Mistral finetunes that are even better than the base models, among these are WizardLM 2, OpenChat 3. If you want to get started deploying Llama 2 on Amazon SageMaker, check out Introducing the Hugging Face LLM Inference Container for Amazon SageMaker and Deploy Llama 2 7B/13B/70B on Amazon SageMaker blog posts. 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 Looks like a better model than llama according to the benchmarks they posted. On the BigBench reasoning benchmark, Open Hermes 2 matches Nous-Hermes 70B! On GPT4All and AGIEval, it competes well with Orca and other leading models, and handily destroys most Llama-2 13B models TheBloke I am running gemma-2-9b-it using llama. If i got it right, then does that mean that most of available speed benchmarks are only for the first response of the I haven't seen any fine-tunes yet. The dev also has an A770 and has benchmarks of various GPUs including the A770. On the llama. 32 votes, 18 comments. rxwl dgpmj mqluk sdqqf foph mlrg arwkyq kqjtkf pfhxd asu