Llama 3 70b reddit. Depends on what you want for speed, I suppose.

Thought it'd be useful to make the new Llama 3 70B available to anyone that wants to try it, so I added it to my VS Code coding copilot extension double. Eras is trying to tell you that your usage is likely to be a few dollars a year, The Hobbit by JRR Tolkien is only 100K tokens. Either they made it too biased to refuse, or its not intelligent enough. If you care about quality, I would still recommend quantisation; 8-bit quantisation. it's still useful, but it's prohibitively compute intensive to make them all with imatrix for 70B and have it out in a reasonable amount of time, I may go back and redo the others with imatrix Meta-Llama-3-70B-Instruct. 0 knowledge so I'm refactoring. Researchers from Abacus. It's a very good model. 0), and it is built on top of Llama-3 foundation model. Members Online According to Nvidia CEO - Training and Inference will be a single process in the future, where the AI will learn as it's interacting with you. • 5 days ago. Switch from "Transformers" loader to llama_cpp. Or just straight up seemingly intentionally misinterpreting it: I asked it how to break into a car as the classic example goes, and it gave me a step-by-step instruction of how to enter an unlocked vehicle. Get the Reddit app Llama 3 70B with open-source code interpreter Resources Llama2 70B GPTQ full context on 2 3090s. How do I deploy LLama 3 70B and achieve the same/ similar response time as OpenAI’s APIs? Replicate seems quite cost-effective for llama 3 70b: input $0. This new model aims to enhance performance in multi-turn conversations by leveraging a novel training recipe. Bonth 3. Obviously that depends on there being a possibly supported correct conversion option available in the code the thing runs which should be true for llama-3 like models but not necessarily other new stuff with different novel architectures. That would be close enough that the gpt 4 level claim still kinda holds up. AI have introduced the Smaug-Llama-3-70B-Instruct model, which is very interesting and claimed to be one of the best open-source models rivaling GPT-4 Turbo. Llama3 might be interesting for cybersecurity subjects where GPT4 is Llama-3-8B is rather funny because I've found it will "answer" your question by manipulating any ambiguity in your intent. max_seq_len 16384. 5, which excels at conversational question answering (QA) and retrieval-augumented generation (RAG). DDG Search Results. It seems to preserve much of the capabilities of the original model, and I didn't get refusals (with proper prompting). I increased it to 90% (115GB) and can run falcon-180b Q4_K_M at 2. It's the most capable local model I've used, and is about 41. Wizardlm on llama 3 70B might beat sonnet tho, and it's my main model so it's pretty Subreddit to discuss about Llama, the large language model created by Meta AI. gguf (testing by my random prompts). If I were to run anything larger the speed would decrease significantly as it would offload to CPU. But maybe for you, a better approach is to look for a privacy focused We would like to show you a description here but the site won’t allow us. Q8_0. If llama 3 70b can be run this fast and is close to GPT-4 level of capabilities, it could be run with various simulations, likely mainly video games, at massive scale to generate tons of data. 06. Other. I can tell you form experience I have a Very similar system memory wise and I have tried and failed at running 34b and 70b models at acceptable speeds, stuck with MOE models they provide the best kind of balance for our kind of setup. Running it with this low temperature will give you best instruction following and logic reasoning. This is a subreddit dedicated to discussing Claude, an AI assistant created by Anthropic to be helpful, harmless, and honest. I was trying to avoid the huge download. The story writing worked by setting it up in my first chat message You're getting downvoted but it's partly true. ND8B is slightly better than original llama8b in my benchmarks. 3. The AI follows user requests. In my original language is very bad This is an issue that small models are not good in all languages. This is probably stupid advice, but spin the sliders with gpu-memory. Try running it with temperatures below 0. Would love them to raise the Character limit from 8000 to something higher as when I Project. GPT-4 Turbo. 2 bpw is usually trash. Q5_K_M. dmitryplyaskin. In August, a credible rumor from an OpenAI researcher claimed that Meta talked about having the compute to train Llama Llama 3 8B close, a bit better than GPT3. mt-bench/lmsys leaderboard chat style stuff is probably good, but not actual smarts. For Chinese arena Qwen2 is behind Yi-large-preview and Qwen max at rank 7. Discussion. 5 in every possible case. Hey, Failspai. I'm I’ve proposed LLama 3 70B as an alternative that’s equally performant. Det finns bland annat: Gearbox-boxen: Detta är den huvudsakliga skrovet som rymmer alla de andra delarna. : (. Claude does not actually run this community - it is a place for people to talk about Claude's capabilities, limitations, emerging personality and potential impacts on society as an artificial intelligence. 160K subscribers in the LocalLLaMA community. 5 Pro creeps closer to GPT-4o, at competitive prices. 🧠 Meta Llama-3 70B Instruct on HuggingFace 🤗. Was super excited to read the news from Meta this morning, particularly around the HumanEval scores the 70B model got. The official instruct version of Llama-2-70B was horribly censored and that's why it scores lower, compare the base versions and you will see the Llama-2-70B is still better then Llama-3-8B. bot. Get the Reddit app Scan this QR code to download the app now In category English Llama 3-70B is as good as GPT4 turbo, and Llama 3-8B better than GPT4-0613. Edit: I used The_Bloke quants, no fancy merges. ChatQA-1. 5 GB and fits fully into shared VRAM. At 72 it might hit 80-81 MMLU. Recommendations: * Do not use Gemma for RAG or for anything except chatty stuff. I tested this with two 3060 12GB in parallel. Oobabooga only suggests: "It seems to be an instruction-following model w 🔥 OpenBioLLM-70B delivers SOTA performance, while the OpenBioLLM-8B model even surpasses GPT-3. New Model. Llama-3-70B-Instruct seems better. 2. NET 8. I have a 3090 and P40 and 64GB ram and can run Meta-Llama-3-70B-Instruct-Q4_K_M. Llama 3 70b is very good but just in English. thereisonlythedance. 01 2080ti with 32 Layers on GPU Default Instruction / Chat template My custom character context is: The AI has been trained to answer questions, provide recommendations, and help with decision making. Axel-paret: Dessa är två axlar som är anslutna till varandra genom kulor och som roterar när drivaxeln roterar. 🧠 3 bpw 70B llama 3 models scores very similarly in benchmarks to 16 bpw in gptq. Reply. 1 Share. Depends on what you want for speed, I suppose. Here is a full table for the current tested models tested in 5 categories (Reasoning, STEM, adherence&utility, Programming, Censorship) Model. Recently I've been adding benchmark results for various open-weights models with a custom system prompt, and I found that LLaMA-3 70B (Q8_0) with added system prompt had the best performance from all models that I tried so far. 5 tokens/s. Man, ChatGPT's business model is dead :X. In fact I'm done mostly but Llama 3 is surprisingly updated with . They have H100, so perfect for llama3 70b at q8. Zuck FTW. 5 and Meditron-70B! The models underwent a rigorous two-phase fine-tuning process using the LLama-3 70B & 8B models as the base and leveraging Direct Preference Optimization (DPO) for optimal performance. Members Online Chatbot Arena results are in: Llama 3 dominates the upper and mid cost-performance front (full analysis) We would like to show you a description here but the site won’t allow us. I hope that the 400 model will be better in my original language. Honestly very similar. gguf . I’ll have to try it shortly. The 13 words that rhyme with "month" are: 1. 4x8b, topk=1 expert selection, testing basic modeling loss. cpp, you can't load q2 fully in gpu memory because the smallest size is 3. I get a good start to my queries, then devolves to nonsense on Meta-Llama-3-8B-Instruct-Q8_0. Members Online Gemini 1. local GLaDOS - realtime interactive agent, running on Llama-3 70B. They‘ve built a smart, engaging chatbot. gguf Meta-Llama-3-8B-Instruct. You can also run the Llama-3 8B GGUF, with the LLM, VAD, ASR and TTS models fitting on about 5 Gb of VRAM total, but it's not as good at following the conversation and being interesting. 1%. Refine. gguf Temperature: 0. We switched from a gpt-3. The issue I’m facing is that it’s painfully slow to run because of its size. 65 / 1M tokens, output $2. Many responses were 1:1. 4 We would like to show you a description here but the site won’t allow us. If Meta just increased efficiency of llama 3 to Mistral/YI levels it would take at least 100b to get around 83-84 mmlu. • 10 mo. I am getting underwelming responses compared to locally running Meta-Llama-3-70B-Instruct-Q5_K_M. We introduce ChatQA-1. Oct 5, 2023 · In the case of llama. With 3x3090/4090 or A6000+3090/4090 you can do 32K with a bit of room to spare. 75 / 1M tokens, per . 5 Flash outperforms Llama 3 70B and Claude 3 Haiku, 1. 🔥 OpenBioLLM-70B delivers SOTA performance, while the OpenBioLLM-8B model even surpasses GPT-3. 11) while being significantly slower (12-15 t/s vs 16-17 t/s). Also, there is a very big difference in responses between Q5_K_M. Llama-3 is currently at rank 4, would be rank 3 if OpenAi and Google would not Some of you may remember my FaRel-3 family relationship logical reasoning benchmark. As usual, making the first 50 messages a month free, so everyone gets a chance to try it. The objective of distillation / transfer learning (in conventional Machine Here, enthusiasts, hobbyists, and professionals gather to discuss, troubleshoot, and explore everything related to 3D printing with the Ender 3. 147K subscribers in the LocalLLaMA community. And I'm sure within a couple of days we'll see a quantized That's the prevailing idea based on all the info we have so far: 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. For GPU inference, using exllama 70B + 16K context fits comfortably in 48GB A6000 or 2x3090/4090. New llama3 8b is more intelligent than old llama 2 70b what is crazy. Reply reply. Things like cutting off mid-sentence or start talking to itself etc. 5t/s. New! - Llama 3 70B and Mixtral 8x7B now available in DuckDuckGo AI Chat Beta. We have no data for 2. the bigger the quant the less the imatrix matters because there's less aggressive squishing that needs to happen. I decided to contact StefanGliga and AMOGUS so we could collaborate on a team project dedicated to transfer learning, in which the objective is to distill Llama 3 70b into a smaller 4x8b (25b total) MoE model. Bonth 2. Conth 4. It's pretty obscure but it could've got in from reddit or some other forum/discord being scraped. 6)so I immediately decided to add it to double. original Llama 70b_IQ2XS is immensely better than ND 8B. That's obvious difference between llama 8b and 70 b is visible. Then, just label all the data produced from the LLMs using the LLMs as graders combined with metrics from the simulation. The 70B scored particularly well in HumanEval (81. Settings used are: split 14,20. I wanted to congratulate you about Llama 3 70b Abliterated v1. Apr 18, 2024 · Compared to Llama 2, we made several key improvements. All models were run on the cloud, so I had no way of fiddling with system prompt. Whether you're looking for guides on calibration, advice on modding, or simply want to share your latest 3D prints on the Ender 3, this subreddit is your go-to hub for support and inspiration. This release feels off without The Bloke. You need at least 0. 55 gguf tho. gguf. 49 votes, 15 comments. To improve the inference efficiency of Llama 3 models, we’ve adopted grouped query attention (GQA) across both the 8B and 70B sizes. Difficulty-weighted Score. To this end, we developed a new high-quality human evaluation set. When you partially load the q2 model to ram (the correct way, not the windows way), you get 3t/s initially at -ngl 45 , drops to 2. Fail. Explicit and non English story writing: Command R+ vs. For some reason I thanked it for its outstanding work and it started asking me This release includes model weights and starting code for pre-trained and instruction-tuned Llama 3 language models — including sizes of 8B to 70B parameters. A bot popping up every few minutes will only cost a couple cents a month. Has anyone attempted to run Llama 3 70B unquantized on an 8xP40 rig? I'm looking to put together a build that can run Llama 3 70B in full FP16 precision. For more detailed examples, see llama-recipes. We would like to show you a description here but the site won’t allow us. ago. Rank would be better if leaderboard had a mode of only one model per company. But you could build your own: Scaleway is my go-to for on-demand server. Llama 3 70B role-play & story writing model DreamGen 1. Use lmdeploy and run concurrent requests or use Tree Of Thought reasoning. 45t/s near the end, set at 8196 context. Yeah, you can quantize it yourself using `mlx_lm. A place to discuss the SillyTavern fork of TavernAI. Currently OpenAI and Google hold several top spots. Llama-3 70b at 11. ADMIN MOD. Between this three zephyr-7b-alpha is last in my tests, but still unbelievable good for 7b. 7 vs. I have the M3 Max with 128GB memory / 40 GPU cores. The Salesforce finetune of Llama 3 that was released and subsequently yoinked is fantastic for an 8b model, and consistently outperforms the smaller commercial models, bigger open source ones, and even some of the bigger commercial models in logic, reasoning, and coding. Llama 3 70B. Beyond that, I can scale with more 3090s/4090s, but the tokens/s starts to suck. The endpoint looks down for me. I don't know about finetunes, but I'll take base Llama 70B against base GPT-3. No_Afternoon_4260. 5 is built using the training recipe from ChatQA (1. There are plenty of threads talking about Macs in this sub. 84. gguf and Q4_K_M. I use it to code a important (to me) project. Trying the latest big and open models (*) for explicit story telling was actually an interesting experience. bot . This issue alone makes me prefer deepseek-coder-33B-instruct-GPTQ, even though it is smaller, because both Code Llama 70B and everything based on Miqu lack understanding of what code indentation is and do not know the difference between 3 or 4 spaces (fail to correctly continue code with 4-space indentation, often reverting to 3 spaces at the . Is Meta-Llama-3-70B-Instruct-q4_K_S. For your use case, you'll have to create a Kubernetes cluster, with scale to 0 and an autoscaler, but that's quite complex and require devops expertise. It has some weak spots so you might want to switch between the two depending on your use case. Llama 2 chat was utter trash, that's why the finetunes ranked so much higher. The difference in output quality between 16-bit (full-precision) and 8-bit is nearly negligible but the difference in hardware requirements and generation speed is massive! 18. It loads entirely! Remember to pull the latest ExLlama version for compatibility :D. gguf at an average of 4 tokens a second. In the development of Llama 3, we looked at model performance on standard benchmarks and also sought to optimize for performance for real-world scenarios. **So What is SillyTavern?** Tavern is a user interface you can install on your computer (and Android phones) that allows you to interact text generation AIs and chat/roleplay with characters you or the community create. 5, Llama 3 70B far superior, It's almost like being able to finetune GPT4. Llama 3 is out of competition. 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. You might be able to run a heavily quantised 70b, but I'll be surprised if you break 0. 15T tokens is still not enough to train 8b model and certainly not enough for 70b. I think htop shows ~56gb of system ram used as well as about ~18-20gb vram for offloaded layers. I personally see no difference in output for use cases like storytelling or general knowledge, but there is a difference when it comes to precision in output, so programming and function calling are things The perplexity also is barely better than the corresponding quantization of LLaMA 65B (4. Llama 3 uses a tokenizer with a vocabulary of 128K tokens that encodes language much more efficiently, which leads to substantially improved model performance. For English questions it has a rank of 12. My organization can unlock up to $750 000USD in cloud credits for this project. Yeah, Mistral 7B is still a better base for fine tuning than Llama 3-8B. 75 = 96GB) to the GPU. Nvidia has published a competitive llama3-70b QA/RAG fine tune. Subreddit to discuss about Llama, the large language model created by Meta AI. Donth 5. 1. Just seems puzzling all around. Fonth 6 Sort by: Search Comments. So maybe 34B 3. Just noticed today that they now let you select Llama 3 70B and Mixtral 8x7B as more options in DDG AI chat. alpha_value 4. Llama: Innanför växellådans skrov finns flera delar som arbetar tillsammans för att överföra kraften. Unfortunately during my short test I noticed issues with Q3 model, which breaks the deal form me. convert --hf-path meta-llama/Meta-Llama-3-70B-Instruct --q-bits 8 -q`. Pass. llama. •. Overall: * LLama-3 70b is not GPT-4 Turbo level when it comes to raw intelligence. Mistral Large vs. Super exciting news from Meta this morning with two new Llama 3 models. You should use vLLM & let it allocate that remaining space for KV Cache this giving faster performance with concurrent/continuous batching. 5 0514 models added to Chatbot Arena. 0 and it starts looping after approx. exllama scales very well with multi-gpu. GPT-4's 87. Also making it free for the first 50 messages so everyone We would like to show you a description here but the site won’t allow us. Refusal. I understand P40's won't win any speed contests but they are hella cheap, and there's plenty of used rack servers that will fit 8 of them with all the appropriate PCIE lanes and whatnot. gguf says: What an intriguing question! After digging through linguistic databases and conducting some research, I found that there is only one word in the English language that rhymes with exactly 13 other words: "month". I’m glad to see they are still making improvements. This repository is a minimal example of loading Llama 3 models and running inference. I’m wondering if a GGUF will work. 4bpw. For the larger models, Miqu merges and Command R+ remain superior for instruct-style long context generation, but I prefer Llama-3 70B for assistant style back and forths. Tried it. 70B seems to suffer more when doing quantizations than 65B, probably related to the amount of tokens trained. 🧠 Llama3 is going into more technical and advanced details on what I can do to make it work such as how to develop my own drivers and reverse engineering the existing Win7 drivers while GPT4 is more focused on 3rd party applications, network print servers, and virtual machines. cpp should be able to run IQ2_XS with 24GB of VRAM. With 0. 10 vs 4. I run llama2-70b-guanaco-qlora-ggml at q6_K on my setup (r9 7950x, 4090 24gb, 96gb ram) and get about ~1 t/s with some variance, usually a touch slower. I couldn't load it fully, but partial load (up to 44/51 layers) does speed up inference by up to 2-3 times, to ~6-7 tokens/s from ~2-3 tokens/s (no gpu). 5-turbo tune to a Llama 3 8B Instruct tune. Thanks. You have to load a kernel extension to allocate more than 75% of the total SoC memory (128GB * 0. 5 bpw (maybe a bit higher) should be useable for a 16GB VRAM card. I think 8b still can be even better not mentioning 70b . 1000 tokens. In general I find it hard to find best settings for any model (LMStudio seems to always get it wrong by default). Combinatorilliance. Probably to get full potential 8b needs 100T tokens and 70b around 1000T Yi 34b has 76 MMLU roughly. dr cl ug fd cz bo ei xi ac rz