Llama 3 v reddit

Llama 3 v reddit. Thank you for developing with Llama models. It is good, but I can only run it at IQ2XXS on my 3090. ChatQA-1. Using this unit of measurement, you would have: 3 bucket-fulls (from the 3-gallon bucket) + 2 bucket-fulls (from the 2-gallon bucket) = 5 bucket-fulls Which is equivalent to saying you have 2. When I tried running llama-3 on the webui it gave me responses, but they were all over the place, sometimes good sometimes horrible. 1 405B. 175K subscribers in the LocalLLaMA community. The lower the texture resolution, the less VRAM or RAM you need to run it. 0000800, thus leaving no difference in the quantized model. Looking at the GitHub page and how quants affect the 70b, the MMLU ends up being around 72 as well. The 70B scored particularly well in HumanEval (81. 2M times, we've seen 600+ derivative models and the repo has been starred over 17K times. 1. Hmm, does it run a quant of 70b? I am getting underwelming responses compared to locally running Meta-Llama-3-70B-Instruct-Q5_K_M. Just seems puzzling all around. 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 A: The foundational Llama models are not fine-tuned for dialogue or question answering like ChatGPT. 5 I found in the LLaMA paper was not in favor of LLaMA: Despite the simplicity of the instruction finetuning approach used here, we reach 68. The Llama 3 instruction tuned models are optimized for dialogue use cases and outperform many of the available open source chat models on common industry benchmarks. 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?… AFAIK then I guess the only difference between Mistral-7B and Llama-3-8B is the tokenizer size (128K vs. 4 for GPT code Made a NEW Llama 3 Model: Meta-Llama-3-8B-Instruct-Dolfin-v0. 5/4 performance, they'll have to make architecture changes so it can still run on consumer hardware. 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. Large Dataset: Llama 2 is trained on a massive dataset of text and code. Super exciting news from Meta this morning with two new Llama 3 models. 1-70B-Instruct, which, at 140GB of VRAM & meta-llama/Meta-Llama-3. 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. Get the Reddit app Scan this QR code to download the app now. Mixture of Experts - Why? This literally is useless to us. And you trashed Mistral for it. 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. 152K subscribers in the LocalLLaMA community. Apr 18, 2024 · Compared to Llama 2, we made several key improvements. 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. This accounts for most of it. Members Online Llama 3 Post-Release Megathread: Discussion and Questions 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. I'm running it at Q8 and apparently the MMLU is about 71. They should be prompted so that the expected answer is the natural continuation of the prompt. 0000805 and 0. GPT 4 got it's edge from multiple experts while Llama 3 has it's from a ridiculous amount of training data. " It didn't really seem like they added support in the 4/21 snapshot, but idk if support would just be telling it when to stop generating. However, on executing my CUDA allocation inevitably fails (Out of VRAM). A warning will be displayed if the model was created before this fix. I'm having a similar experience on an RTX-3090 on Windows 11 / WSL. 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" Get the Reddit app Scan this QR code to download the app now. 9 vs 76. 6)so I immediately decided to add it to double. There's some amount of certainty that it has the second best score. the 32k in llama 2. 0000803 might both become 0. In August, a credible rumor from an OpenAI researcher claimed that Meta talked about having the compute to train Llama 3 and Llama 4, with Llama 3 being as good as GPT-4. Open Source: Llama 2 embodies open source, granting unrestricted access and modification privileges. Llama-3 is sort of a game-changer in the AI space, in my anecdotal opinion. g. was the gguf for this created after the llamacpp update? "Added fixes for Llama 3 tokenization: Support updated Llama 3 GGUFs with pre-tokenizations. gguf (testing by my random prompts). More on the exciting impact we're seeing with Llama 3 today ️ go. Reply reply We introduce ChatQA-1. It seems to perform quite well, although not quite as good as GPT's vision albeit very close. 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. Jul 23, 2024 · As our largest model yet, training Llama 3. With an embedding size of 4096, this means almost 400m increase in input layer parameter. As usual, making the first 50 messages a month free, so everyone gets a chance to try it. What are the VRAM requirements for Llama 3 - 8B? My Ryzen 5 3600: LLaMA 13b: 1 token per second My RTX 3060: LLaMA 13b 4bit: 18 tokens per second So far with the 3060's 12GB I can train a LoRA for the 7b 4-bit only. Members Online Chatbot Arena results are in: Llama 3 dominates the upper and mid cost-performance front (full analysis) Llama 1 training was from around July 2022 to January 2023, Llama 2 from January 2023 to July 2023, so Llama 3 could plausibly be from July 2023 to January 2024. Well not this time To this end, we developed a new high-quality human evaluation set. 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. And if you have a 2-gallon bucket, you have 2 bucket-fulls. If there were 8 experts then it would have had a similar amount of activated parameters. Llama 3 8b is still far better I would say overall, and has that unique style of writing but phi 3 is 100% the model to use if you cant run Llama 3 8b or are looking for something far faster. GroqCloud's LLaMa 3. 516 votes, 148 comments. Also, there is a very big difference in responses between Q5_K_M. With the money large companies are investing in AI since the release of GPT 4, I'd be surprised if upcoming open source models don't see massive We would like to show you a description here but the site won’t allow us. 1 (Modified Dolphin dataset and Llama 3 chat format) upvotes · comments r/LocalLLaMA Yes and no, GPT4 was MOE where as Llama 3 is 400b dense. Jul 23, 2024 · How well does LLaMa 3. On LMSYS Chatbot Arena Leaderboard, Llama-3 is ranked #5 while current GPT-4 models and Claude Opus are still tied at #1. 5, which excels at conversational question answering (QA) and retrieval-augumented generation (RAG). 1 not even the most up to date one, mistral 7B 0. So, if you have a 3-gallon bucket, you have 3 bucket-fulls. It's extremely coherent (even obnoxiously so at times). Then there's 400m more in lm head (output layer). Rocking the Llama-8B derivative model, Phi-3, SDXL, and now Piper, all on a laptop with RTX 3070 8GB. Here, enthusiasts, hobbyists, and professionals gather to discuss, troubleshoot, and explore everything related to 3D printing with the Ender 3. 75 alpha_value for RoPE scaling, but I'm wondering if that's optimal with Llama-3. I fiddle diddle with the settings all the time lol. 5-turbo outputs collected from the API? They're unusually short, and asking the same questions through chatgpt gives completely different answers, typically all a full page long with lots of bullet points, all of which were vastly better than the presented llama 2 replies. 5-turbo, which was far more vapid and dull. OpenAI makes it work, it isn't naturally superior or better by default. Fine-tuned Llama models have scored high on benchmarks and can resemble GPT-3. 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. Opus beats Reka's large model (which granted is still training) on HumanEval 84. Just look at how much better GPT 4 is than GPT 3, then compare GPT 3 to an instruction-tuned Llama. In theory Llama-3 should thus be even better off. 9% on MMLU. Llama models are not yet GPT-4 quality. Our use case doesn’t require a lot of intelligence (just playing the role of a character), so YMMV. 8, and on chat Elo (1185 vs 1091) per their evaluation. Hi, I'm still learning the ropes. 5 project, and I strongly suggest that the minicpm-llama 3-v 2. Llama 3 70b only has an interval up to 1215 as its maximum score, that is not within the lower interval range of the higher scored models above it. 2 q4_0. We're really just around 1 GPT sized generation away from exceedingly capable open source models. Hi. Most people here don't need RTX 4090s. 5-Turbo. 7 tokens/s after a few times regenerating. Not sure if the results are any good, but I don't even wanna think about trying it with CPU. . 5: Boosting SFT models' performance with DPO 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. ⏤⏤⏤⏤⏤⏤⏤⏤ 🔥 ⏤⏤⏤⏤⏤⏤⏤ 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 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. 5 and Opus/GPT-4 for quality. 10 vs 4. 5-turbo tune to a Llama 3 8B Instruct tune. 5 native context) and 16K (x2 native context)? I'm getting things to work at 12K with a 1. 4% Apr 18, 2024 · Meta developed and released the Meta Llama 3 family of large language models (LLMs), a collection of pretrained and instruction tuned generative text models in 8 and 70B sizes. Subreddit to discuss about Llama, the large language model created by Meta AI. If you prioritize accuracy and efficiency in coding tasks, Llama 3 might be the better choice. Plans to release multimodal versions of llama 3 later Plans to release larger context windows later. 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. Subreddit to discuss about Llama, the large language model created by Meta AI. Mistral 7B was very active again, not as much as llama-1, but there were some great finetunes coming out using combinations of previous datasets /techniques. Apr 19, 2024 · Llama 3 has 128k vocab vs. Llama 3 8b writes better sounding responses than even GPT-4 Turbo and Claude 3 Opus. Llama-2 The perplexity also is barely better than the corresponding quantization of LLaMA 65B (4. One thing I enjoy about Llama 3 is how stable it is. The only comparison against GPT 3. Llama 3 knocked it out of the fucking park compared to gpt-3. Q: How to get started? You're getting downvoted but it's partly true. Note: In order to benefit from the tokenizer fix, the GGUF models need to be reconverted after this commit. 5 in the LLaMA paper was not in favor of LLaMA: Despite the simplicity of the instruction finetuning approach used here, we reach 68. LLaMA-I (65B) outperforms on MMLU existing instruction finetuned models of moderate sizes, but are still far from the state-of-the-art, that is 77. Comparisons with current versions of Sonnet, GPT-4, and Llama 3. 5 buckets. 0 and v1. 7 vs. To improve the inference efficiency of Llama 3 models, we’ve adopted grouped query attention (GQA) across both the 8B and 70B sizes. Think about Q values as texture resolution in games. 5 This still kinda continued with llama-2, but this is also where we saw the big merges starting and many bad actors just finetuning on the benchmark. My current rule of thumb on base models is, sub-70b, mistral 7b is the winner from here on out until llama-3 or other new models, 70b llama-2 is better than mistral 7b, stablelm 3b is probably the best <7B model, and 34b is the best coder model (llama-2 coder) We switched from a gpt-3. Reka Edge (the 7b one) does poorly relative to the large models. In CodeQwen that happened to 0. Or check it out in the app stores New Phi-3-mini-128k and Phi-3-vision-128k, re-abliterated Llama-3 We would like to show you a description here but the site won’t allow us. Generally, bigger, better. Additionally, we incorporate more conversational QA data to enhance its tabular and arithmatic calculation Jul 23, 2024 · The same snippet works for meta-llama/Meta-Llama-3. 1 405B on over 15 trillion tokens was a major challenge. Kept sending EOS after first patient, prematurely ending the conversation! Amy, Roleplay: Assistant personality bleed-through, speaks of alignment. 2x TESLA P40s would cost $375, and if you want faster inference, then get 2x RTX 3090s for around $1199. To enable training runs at this scale and achieve the results we have in a reasonable amount of time, we significantly optimized our full training stack and pushed our model training to over 16 thousand H100 GPUs, making the 405B the first Llama model trained at this scale. 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 I looked at the report: the Reka models only outperform for multimodal data. The improvement llama 2 brought over llama 1 wasn't crazy, and if they want to match or exceed GPT3. So I have 2-3 old GPUs (V100) that I can use to serve a Llama-3 8B model. We followed the normal naming scheme of community. Putting garbage in you can expect garbage out. Update 2023-03-28: Added answers using a ChatGPT-like persona and some new questions! Removed generation stats to make room for that. Llama 3 will probably released soon and they already teased multimodality with the rayban glasses and Llama 2. 06%. Or check it out in the app stores &nbsp; &nbsp; 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. Llama 3 uses a tokenizer with a vocabulary of 128K tokens that encodes language much more efficiently, which leads to substantially improved model performance. After spending a whole day comparing different versions of the LLaMA and Alpaca models, I thought that maybe that's of use to someone else as well, even if incomplete - so I'm sharing my results here. We would like to show you a description here but the site won’t allow us. 5 project's team go to the complaint to expose the llama3-v project authors' stealing and lying about academic We would like to show you a description here but the site won’t allow us. 5 is built using the training recipe from ChatQA (1. 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. With quantization the 0. GPT-4's 87. 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? 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. 0), and it is built on top of Llama-3 foundation model. Jul 27, 2024 · This is a trick modified version of the classic Monty Hall problem, and both GPT-4o-mini and Claude 3. I've recently tried playing with Llama 3 -8B, I only have an RTX 3080 (10 GB Vram). 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. The even more powerful Llama-3 400B+ model is still in training and is likely to surpass GPT-4 and Opus once released. I used to struggle to go past 3-4k context with Mistral and now I wish I had like 20k context with Llama 3 8B as I reach 8k consistently. This doesn't that matter that much for quantization anyway. This evaluation set contains 1,800 prompts that cover 12 key use cases: asking for advice, brainstorming, classification, closed question answering, coding, creative writing, extraction, inhabiting a character/persona, open question answering, reasoning, rewriting, and summarization. 5 70b llama 3. 4 for GPT With GPT4-V coming out soon and now available on ChatGPT's site, I figured I'd try out the local open source versions out there and I found Llava which is basically like GPT-4V with llama as the LLM component. I have been extremely impressed with Neuraldaredevil Llama 3 8b Abliterated. gguf and Q4_K_M. 2. Yesterday I did a quick test of Ollama performance Mac vs Windows for people curious of Apple Silicon vs Nvidia 3090 performance using Mistral Instruct 0. While the GPU is (a lot) slower, a Mac Studio with an M2 Ultra is quite usable as long as it has enough memory. Yeah, plenty. 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. You can play with the settings and it will still give coherent replies in a pretty wide range. Members Online NeuralHermes-2. 11) while being significantly slower (12-15 t/s vs 16-17 t/s). 70B seems to suffer more when doing quantizations than 65B, probably related to the amount of tokens trained. 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. 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. And under each version, there may be different base LLMs. The fine-tuning data includes publicly available instruction datasets, as well as over 10M human-annotated examples. Are these 3. Personally, I still prefer Mixtral, but I think Llama 3 works better in specialized scenarios like character scenarios. I don't wanna cook my CPU for weeks or months on training Welcome to the Ender 3 community, a specialized subreddit for all users of the Ender 3 3D printer. As part of the Llama 3. Jul 27, 2024 · Llama 3 and GPT-4 are both powerful tools for coding and problem-solving, but they cater to different needs. Mixtral has a decent range, but it's not nearly as broad as Llama 3. 6% 36 5 37 3 Gemini Ultra 35. It's as if they are really speaking to an audience instead of the user. 5% of the values, in Llama-3-8B-Instruct to only 0. It's legitimately the first AI model I've interacted with that actually feels like a person at times. The devil's in the details: If you're savvy with how you manage loading different agents and tools, and don't mind the slight delays during loading/switching, you're in for a great time, even on lower-end hardware. Math is not "up for debate", this equation has only one solution, your is wrong, Llama got it wrong, and Mistral got it right. Llama’s instruct tune is just more lively and fun. 8. 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. Memory consumption can be further reduced by loading in 8-bit or 4-bit mode. 1-405B-Instruct (requiring 810GB VRAM), makes it a very interesting model for production use cases. Only 903 Elo on their chat evaluation. 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. (5-6 tokens per second) I wish Nvidia or AMD would sell consumer GPUs with enough memory for large model inference, but they won't since it would cannibalize the $20K+ server GPU market. I would highly doubt that they started multi modal training now instead of months ago Reply reply The reason GPT 4 0125 is at 2:d place even though there are 3 models above it is because its interval overlapses it with second place. Jun 2, 2024 · Based on the above three facts, I think there is sufficient evidence to prove that the llama3-v project has stolen the academic achievements of the minicpm-llama 3-v 2. Benefits of Llama 2. I have a fairly simple python script that mounts it and gives me a local server REST API to prompt. Prompt: Two trains on separate tracks, 30 miles from each other are approaching each other, each at a speed of 10 mph. This renders it an invaluable asset for researchers and developers aiming to leverage extensive language models. Meta vs OpenAI We would like to show you a description here but the site won’t allow us. Weirdly, inference seems to speed up over time. Mistral 7B just isn't great for creative writing, Llama 3 8B has made it irrelevant in that aspect. gguf. Artificial Analysis shows that Llama-3 is in-between Gemini-1. Or check it out in the app stores Mistral vs Mistral finetunes vs 13B vs Llama-70B vs GPT-3. This post also conveniently leaves out the fact that CPU and hybrid CPU/GPU inference exists, which can run Llama-2-70B much cheaper then even the affordable 2x TESLA P40 option above. fb. Generally, Bunny has two versions, v1. me/q08g2… All prompts were in a supported but non English language. On a 70b parameter model with ~1024 max_sequence_length, repeated generation starts at ~1 tokens/s, and then will go up to 7. bot. I also tried running the abliterated 3. Makes you wonder what was even a point in releasing Gemma if it's so underwhelming. Note how it's a comparison between it and mistral 7B 0. My question is as follows. It generally sounds like they’re going for an iterative release. Or check it out in the app stores Llama-3-70b-Instruct 43. Llama 2 chat was utter trash, that's why the finetunes ranked so much higher. 5 Sonnet correctly understand the trick and answer correctly, while Llama 405B and Mistral Large 2 fall for the trick. Wizardlm on llama 3 70B might beat sonnet tho, and it's my main model so it's pretty Get the Reddit app Scan this QR code to download the app now. MoE helps with Flops issues, it takes up more vram than a dense model. (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. 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. padzb etratrl ziaes civqcb cvbesay wajcp jtzlzf sdkne mlilso fskdh