Llama 3 v reddit. the 32k in llama 2. g. GroqCloud's LLaMa 3. 6% 36 5 37 3 Gemini Ultra 35. (AFAIK Llama 3 doesn't officially support other languages, but I just ignored that and tried anyway) What I have learned: Older models, including Mixtral 8x7B: some didn't work well, others were very acceptable. On LMSYS Chatbot Arena Leaderboard, Llama-3 is ranked #5 while current GPT-4 models and Claude Opus are still tied at #1. For if the largest Llama-3 has a Mixtral-like architecture, then so long as two experts run at the same speed as a 70b does, it'll still be sufficiently speedy on my M1 Max. ) At At Meta on Threads: It's been exactly one week since we released Meta Llama 3, in that time the models have been downloaded over 1. 0000800, thus leaving no difference in the quantized model. 5 native context) and 16K (x2 native context)? I'm getting things to work at 12K with a 1. Our use case doesn’t require a lot of intelligence (just playing the role of a character), so YMMV. Looking at the GitHub page and how quants affect the 70b, the MMLU ends up being around 72 as well. The lower the texture resolution, the less VRAM or RAM you need to run it. As usual, making the first 50 messages a month free, so everyone gets a chance to try it. However, when I try to load the model on LM Studio, with max offload, is gets up toward 28 gigs offloaded and then basically freezes and locks up my entire computer for minutes on end. Max supported "texture resolution" for an LLM is 32 and means the "texture pack" is raw and uncompressed, like unedited photos straight from digital camera, and there is no Q letter in the name, because the "tex Yesterday, I quantized llama-3-70b myself to update gguf to use the latest llama. OpenAI makes it work, it isn't naturally superior or better by default. ⏤⏤⏤⏤⏤⏤⏤⏤ 🔥 ⏤⏤⏤⏤⏤⏤⏤ Join us here at Firefly Mains to learn more and theorize about Firefly, experience precious fan arts of her (or sick mecha art), build discussions, leaks, community talks, and just We would like to show you a description here but the site won’t allow us. cpp pretokenization. 1 405B is in a class of its own, with unmatched flexibility, control, and state-of-the-art capabilities that rival the best closed source models. 1 405b, Claude Sonnet 3. fb. All prompts were in a supported but non English language. Llama-2 I don't think they are lying, and I don't think Microsoft lies either with their Llama 3 numbers. Or check it out in the app stores Based on Meta-Llama-3-8b-Instruct, and is governed by Meta Llama 3 Comparisons with current versions of Sonnet, GPT-4, and Llama 3. I don't use GPT-4o so don't know if it has a better personality than llama 3 Instruct tune. 5; it will almost certainly be replicated or surpassed very shortly. 7 tokens/s after a few times regenerating. Especially when it comes to multilingual, Mistral NeMo looks super promising but I am wondering if it is actually better than Llama3. 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. i tried grammar from llama cpp but struggle to make the proper grammar format since i have constant value in the json, got lost in the syntax even using the typescript grammarbuild and the built in grammar from llama cpp server. 32K if what you're saying is true) Honestly I'm not too sure if the vocab size being different is significant, but according to the Llama-3 blog, it does yield 15% fewer tokens vs. Math is not "up for debate", this equation has only one solution, your is wrong, Llama got it wrong, and Mistral got it right. In theory Llama-3 should thus be even better off. It's as if they are really speaking to an audience instead of the user. And we are still talking about probably 2 AI Renaissance away, looking at the improvement so far, this seems feasible. Get the Reddit app Scan this QR code to download the app now. Mixture of Experts - Why? This literally is useless to us. More on the exciting impact we're seeing with Llama 3 today ️ go. I see personality in popular LLMs as an evolution, what we have is ChatGPT > Mixtral 8x7B Instruct > Llama 3 70B. Yes and no, GPT4 was MOE where as Llama 3 is 400b dense. 2M times, we've seen 600+ derivative models and the repo has been starred over 17K times. We would like to show you a description here but the site won’t allow us. I'm not expecting magic in terms of the local LLMs outperforming ChatGPT in general, and as such I do find that ChatGPT far exceeds what I can do locally in a 1 to 1 comparison. 06%. 8. . MonGirl Help Clinic, Llama 2 Chat template: The Code Llama 2 model is more willing to do NSFW than the Llama 2 Chat model! But also more "robotic", terse, despite verbose preset. What are the VRAM requirements for Llama 3 - 8B? Llama 3 rocks! Llama 3 70B Instruct, when run with sufficient quantization (4-bit or higher), is one of the best - if not the best - local models currently available. me/q08g2… Nah but here's how you could use ollama with it: Download lantzk/Llama-3-Instruct-8B-SimPO-ExPO-Q4_K_M-GGUF off of huggingface. 5-turbo tune to a Llama 3 8B Instruct tune. It generally sounds like they’re going for an iterative release. It felt much smarter than miqu and existing llama-3-70b ggufs on huggingface. So I was looking at some of the things people ask for in llama 3, kinda judging them over whether they made sense or were feasible. Prior to that, my proverbial daily driver (although it was more like once every 3-4 days) had been this model for probably 3 months previously. You tried to obfuscate math prompt (line 2), and you obfuscated it so much that both you and LLama solved it wrong, and Mistral got it right. This accounts for most of it. It is good, but I can only run it at IQ2XXS on my 3090. One thing I enjoy about Llama 3 is how stable it is. My question is as follows. 1 405B compare with GPT 4 or GPT 4o on short-form text summarization? I am looking to cleanup/summarize messy text and wondering if it's worth spending the 50-100x price difference on GPT 4 vs. 0 and v1. The EXL2 4. So I have 2-3 old GPUs (V100) that I can use to serve a Llama-3 8B model. 0000805 and 0. 0 bpw exl2, like I was going through all my past exl2 chats and hitting regenerate and getting almost identical replies, not an accurate measurement by any means but I'm The thing is, ChatGPT is some odd 200b+ parameters vs our open source models are 3b, 7b, up to 70b (though falcon just put out a 180b). All models before Llama 3 routinely generated text that sounds like something a movie character would say, rather than something a conversational partner would say. 1 405B. You're getting downvoted but it's partly true. 1-405B-Instruct (requiring 810GB VRAM), makes it a very interesting model for production use cases. Creating the prompts Jul 27, 2024 · This is a trick modified version of the classic Monty Hall problem, and both GPT-4o-mini and Claude 3. GPT-4's 87. 75 alpha_value for RoPE scaling, but I'm wondering if that's optimal with Llama-3. bot. Generally, Bunny has two versions, v1. 5 in humaneval. 101 votes, 38 comments. Jul 31, 2024 · For this experiment I’ve created 7 prompts that should push each of Llama 3. Then there's 400m more in lm head (output layer). How Jul 23, 2024 · How well does LLaMa 3. Under each set, I used a simple traffic light scale to express my evaluation of the output, and I have provided explanations for my choices. You can play with the settings and it will still give coherent replies in a pretty wide range. 5% of the values, in Llama-3-8B-Instruct to only 0. You should try it. 1-70B-Instruct, which, at 140GB of VRAM & meta-llama/Meta-Llama-3. Llama’s instruct tune is just more lively and fun. We followed the normal naming scheme of community. Meta vs OpenAI Get the Reddit app Scan this QR code to download the app now. 5/4 performance, they'll have to make architecture changes so it can still run on consumer hardware. Subreddit to discuss about Llama, the large language model created by Meta AI. Llama 3 models take data and scale to new heights. 5 Sonnet correctly understand the trick and answer correctly, while Llama 405B and Mistral Large 2 fall for the trick. Personally, I still prefer Mixtral, but I think Llama 3 works better in specialized scenarios like character scenarios. For people who are running Llama-3-8B or Llama-3-70B beyond the 8K native context, what alpha_value is working best for you at 12K (x1. 5bpw achieved perfect scores in all tests, that's (18+18)*3=108 questions. Prompt: Two trains on separate tracks, 30 miles from each other are approaching each other, each at a speed of 10 mph. Llama 2 chat was utter trash, that's why the finetunes ranked so much higher. The fine-tuning data includes publicly available instruction datasets, as well as over 10M human-annotated examples. This doesn't that matter that much for quantization anyway. 1 405B—the first frontier-level open source AI model. Artificial Analysis shows that Llama-3 is in-between Gemini-1. Putting garbage in you can expect garbage out. 1 (Modified Dolphin dataset and Llama 3 chat format) upvotes · comments r/LocalLLaMA Firefly Mains 🔥🪰 A beloved character from the game Honkai Star Rail, also known under the alias 'Stellaron Hunter Sam,' a remnant of Glamoth's Iron Cavalry. However, on executing my CUDA allocation inevitably fails (Out of VRAM). Llama 3 knocked it out of the fucking park compared to gpt-3. AFAIK then I guess the only difference between Mistral-7B and Llama-3-8B is the tokenizer size (128K vs. Can you give examples where Llama 3 8b "blows phi away", because in my testing Phi 3 Mini is better at coding, like it is also better at multiple smaller languages like scandinavian where LLama 3 is way worse for some reason, i know its almost unbelievable - same with Japanese and korean, so PHI 3 is definitely ahead in many regards, same with logic puzzles also. And you trashed Mistral for it. The even more powerful Llama-3 400B+ model is still in training and is likely to surpass GPT-4 and Opus once released. 1. Apr 19, 2024 · Llama 3 has 128k vocab vs. In your downloads folder make a file called Modelfile and put the following inside: We would like to show you a description here but the site won’t allow us. The text quality of Llama 3, at least with a high dynamic temperature threshold of lower than 2, is honestly indistinguishable. This model surpasses both Hermes 2 Pro and Llama-3 Instruct on almost all benchmarks tested, retains its function calling capabilities, and in all our testing, achieves the best of both worlds result. Doing some quick napkin maths, that means that assuming a distribution of 8 experts, each 35b in size, 280b is the largest size Llama-3 could get to and still be chatbot Thank you for developing with Llama models. 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. Members Online Built a Fast, Local, Open-Source CLI Alternative to Perplexity AI in Rust Llama 3 was pretrained on over 15 trillion tokens of data from publicly available sources. Think about Q values as texture resolution in games. 0000803 might both become 0. If there were 8 experts then it would have had a similar amount of activated parameters. Made a NEW Llama 3 Model: Meta-Llama-3-8B-Instruct-Dolfin-v0. 1 8B. Weirdly, inference seems to speed up over time. 1 release, we’ve consolidated GitHub repos and added some additional repos as we’ve expanded Llama’s functionality into being an e2e Llama Stack. 5 70b llama 3. (And Unnatural Code Llama crushes 3. I'm running it at Q8 and apparently the MMLU is about 71. I think Meta is optimizing the model to perform well for a very specific prompt, and if you change the prompt slightly, the performance disappears. Wizardlm on llama 3 70B might beat sonnet tho, and it's my main model so it's pretty We switched from a gpt-3. 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. MoE helps with Flops issues, it takes up more vram than a dense model. Exllamav2 uses the existing tokenizer so it shouldn't have any issues for that Any other degradation is difficult to estimate, I was actually surprised when I went and loaded fp16 just how similar the generation was the 8. The 70B scored particularly well in HumanEval (81. Memory consumption can be further reduced by loading in 8-bit or 4-bit mode. Tiefghter 13B - free Llama 3 70B - premium Llama 3 400B/Chat gpt 4 turbo -Ultra AI, maybe with credits at first, but later without. Kept sending EOS after first patient, prematurely ending the conversation! Amy, Roleplay: Assistant personality bleed-through, speaks of alignment. Happy to hear your experience with the two models or discuss some benchmarks. The base Code LLama beats 3. As part of the Llama 3. 6)so I immediately decided to add it to double. 161K subscribers in the LocalLLaMA community. 7 vs. Mixtral has a decent range, but it's not nearly as broad as Llama 3. Or check it out in the app stores Llama-3-70b-Instruct 43. That actually should be possible to make hmm Hi, I'm still learning the ropes. I'm having a similar experience on an RTX-3090 on Windows 11 / WSL. With quantization the 0. Plans to release multimodal versions of llama 3 later Plans to release larger context windows later. On a 70b parameter model with ~1024 max_sequence_length, repeated generation starts at ~1 tokens/s, and then will go up to 7. GPT 4 got it's edge from multiple experts while Llama 3 has it's from a ridiculous amount of training data. Generally, bigger, better. Subreddit to discuss about Llama, the large language model created by Meta AI. I realize the VRAM reqs for larger models is pretty BEEFY, but Llama 3 3_K_S claims, via LM Studio, that a partial GPU offload is possible. I also tried running the abliterated 3. I have a fairly simple python script that mounts it and gives me a local server REST API to prompt. The python one does even better, of course, but the base model wins as-is (possibly within a margin of error, of course). Super exciting news from Meta this morning with two new Llama 3 models. Jul 23, 2024 · Bringing open intelligence to all, our latest models expand context length to 128K, add support across eight languages, and include Llama 3. Llama 3. With an embedding size of 4096, this means almost 400m increase in input layer parameter. 5 and Opus/GPT-4 for quality. 5, GPT-4o and Gemini Pro 1. 5 and allow me to crown a winner. Or check it out in the app stores TOPICS Llama 3 instruct exl2 vs llama 3 exl2 We would like to show you a description here but the site won’t allow us. coding questions go to a code-specific LLM like deepseek code(you can choose any really), general requests go to a chat model - currently my preference for chatting is Llama 3 70B or WizardLM 2 8x22B, search We would like to show you a description here but the site won’t allow us. Since llama 3 chat is very good already, I could see some finetunes doing better but it won't make as big a difference like on llama 2. I'm still learning how to make it run inference faster on batch_size = 1 Currently when loading the model from_pretrained(), I only pass device_map = "auto" I have been extremely impressed with Neuraldaredevil Llama 3 8b Abliterated. Jul 27, 2024 · This is a trick modified version of the classic Monty Hall problem, and both GPT-4o-mini and Claude 3. 4% Llama 3 8b writes better sounding responses than even GPT-4 Turbo and Claude 3 Opus. The improvement llama 2 brought over llama 1 wasn't crazy, and if they want to match or exceed GPT3. I recreated a perplexity-like search with a SERP API from apyhub, as well as a semantic router that chooses a model based on context, e. But what if you ask the model to formulate a step by step plan for solving the question and use in context reasoning, and then run this three times, and then bundle the three responses together and send them as a context with a new prompt where you tell the model to evaluate the three responses and pick the one it thinks is correct and then if needed improve it, before stating the final answer? Jul 23, 2024 · The same snippet works for meta-llama/Meta-Llama-3. Or check it out in the app stores New Phi-3-mini-128k and Phi-3-vision-128k, re-abliterated Llama-3 In CodeQwen that happened to 0. 5-turbo, which was far more vapid and dull. I've recently tried playing with Llama 3 -8B, I only have an RTX 3080 (10 GB Vram). I would love to see open source dataset that can tune any model to behave like llama 3 70b. And under each version, there may be different base LLMs. dirrkhqhzmwbtpmlddovwejpbvidipgqhzmdnbxmwdvmgfsysxgwhe
