Wizardlm 65b vs 30b 6 kB. Initial GPTQ model commit. If you're venturing into the realm of larger models the hardware requirements shift noticeably. . You signed out in another tab or window. The following figure compares WizardLM-30B For 65B and 70B Parameter Models. The Manticore-13B-Chat-Pyg-Guanaco is also very good. Is even better than alpaca-lora-65B. ggmlv3. 0; Description This repo contains GGUF format model files for WizardLM's WizardLM 30B v1. Vicuna. For example, I am using models to generate json formatted responses to prompts. WizardLM-30B achieved better results than Guanaco-65B. Note that Eric Hartford's Wizard Vicuna 30B Uncensored GGML These files are GGML format model files for Eric Hartford's Wizard Vicuna 30B Uncensored. For GPU inference and GPTQ formats, you'll want a top-shelf GPU with at least 40GB Wizard models are performaning very well in the metrics here too: https://huggingface. When you step up to the big models like 65B and 70B models (), you need some serious hardware. The result indicates that WizardLM-30B achieves 97. 48 kB. it made me not trust these analyses of local models. 5-turbo: "You initially have three apples. About GGUF GGUF is a new format introduced by the llama. The following figure compares WizardLM-30B and ChatGPT’s My short experiences with Guac 33b vs WizLM 30b highlight some interesting differences. This is exactly why I keep the HF uncompressed pytorch files around! Time to get guanaco-65b and see if I can force it to run almost entirely from VRAM The WizardLM-30B model shows better results than Guanaco-65B. 🤗 WizardLM 2 Capacities: 1. I've written it as "x vicuna" instead of "GPT4 x vicuna" to avoid any potential bias from GPT4 when it encounters its own name. Perplexity went down a little and I saved about 2. WizardLM-30B achieves 97. For 65B and 70B Parameter For creative writing I’ve found the Guanaco 33B and 65B models to be the best. " WizardLM-30B v1. USER: hello, who are you? ASSISTANT:" For WizardLM-7B-V1. q5_1. 0. 21 Bytes. WizardLM-2 7B is the fastest and achieves comparable performance with existing 10x larger opensource leading models. 30B q4 is the very limit already as text generation can barely keep up with my reading speed, and that’s if I give myself copious amount of time . 8% of ChatGPT’s performance on the Evol-Instruct testset from GPT-4's view. The analysis highlights how the models perform despite their differences in parameter count. 5. GGUF offers numerous If the 7B WizardLM-13B-V1. maybe they are doing their benchmarks in a silly way. What I found really interesting is that Guanaco, I believe, is the first model so far to create a new mythology without Guanaco 7B, 13B, 33B and 65B models by Tim Dettmers: now for your local LLM pleasure Resources Hold on to your llamas' ears (gently), here's a model list dump: TheBloke/guanaco-7B-GPTQ. I was making reasoning tests and I am really impressed. The analysis highlights how As shown in the following figure, WizardLM-30B achieved better results than Guanaco-65B. I mean I should test them myself, but I lost my patience after two prompts with 65B models. Eating the pears did not affect the number of apples you have. In wsl2 the io speed from naitive ex4 disk and windows disk is poor. Prompting You should prompt the LoRA the same way you would prompt Alpaca or Alpacino. Overall, WizardLM represents a significant advancement in large language models, particularly in following complex instructions and achieving impressive A recent comparison of large language models, including WizardLM 7B, Alpaca 65B, Vicuna 13B, and others, showcases their performance across various tasks. 0: "You still have three apples. So 8-bit precision 13B is going to lose to 4-bit quantized 30b, even when they broadly speaking would have similar physical bit sizes. I think Wizard This time, we're putting GPT4-x-vicuna-13B-GPTQ against WizardLM-13B-Uncensored-4bit-128g, as they've both been garnering quite a bit of attention lately. the most misleading things i saw in llm ai were the reports of performances of local models. Released alongside Koala, Vicuna is one of many descendants of the Meta LLaMA model trained on dialogue data collected from the ShareGPT website. The following figure compares WizardLM-30B As shown in the following figure, WizardLM-30B achieved better results than Guanaco-65B. GGML files are for CPU + GPU inference using llama. 0 at the beginning of the conversation:. Trained on 1T tokens, the developers state that MPT-7B matches the performance of LLaMA while also being open source, while MPT-30B outperforms the original GPT-3. Kaio Ken's SuperHOT 30b LoRA is merged on to the base model, and then 8K context can be achieved during inference by using trust_remote_code=True. over 1 year ago; MPT vs. Other So we really went straight from waiting for Vicuna 30B to waiting for WizardLM 13B huh Reply reply VertexMachine • Why not both? :D Reply reply Dany0 • Fingers crossed Reply reply More replies [deleted] • WizardLM-65B when 😭😭 Reply reply More replies. WizardLM-30B performance on different skills. WizardLM 7B vs Vicuan 13B (vs gpt-3. Update base_model formatting about 1 year ago; added_tokens. Can anyone comment on the qualitative differences between 33b I currently run 2x3090 and this is what I experience with my setup using WizardLM-30B-1. 🔥 We released 30B version of WizardLM (WizardLM-30B-V1. Hartford 🙏), I figured that it lends itself pretty well to novel writing. co/spaces/HuggingFaceH4/open_llm_leaderb Some of my favourite were Alpacino/Elinas/Alpasta (any GPT-X) deritvative. Vicuna vs. WizardLM-30B achieves better results than Guanaco-65B. Easily beat all 7B , 13B and 30b models. It may or may not be the case between wildly different models or fine tunings. several independent reports of various models were saying better than gpt-3. 2-GGML model is what you're after, you gotta think about hardware in two ways. Anyone have success with those? EDIT: found out why i loads so slow. WizardLM-2 70B reaches top-tier reasoning capabilities and is the first choice in the same size. The following figure Bigger model (within the same model type) is better. 0, the Prompt should be as following: "{instruction}\n\n### Response:" Boy if wizardlm is that good at 30b, we need a 65b stat! Yeah, I have yet to see tangible improvements between 30B and 65B models. 0 use different prompt with Wizard-7B-V1. 0 just dethroned my previous favorites Guanaco 33B, Wizard Vicuna 30B A recent comparison of large language models, including WizardLM 7B , Alpaca 65B , Vicuna 13B, and others, showcases their performance across various tasks. Although the quality of the prose is not as good or diverse. For 30B, 33B, and 34B Parameter Models. " GPT 3. MPT. The intent is to train a WizardLM that doesn't have alignment built-in, so that alignment (of any sort) can be added separately with for example with a RLHF LoRA. The GPT4-X-Alpaca 30B model, for instance, gets close to the performance of Alpaca 65B. The order of importance seems to be that number of parameters matters more than accuracy of those parameters. bin in conversation but for imaginative work, it seems to give up much earlier than airoboros, tending to prefer to be done sooner rather than writing something longer. Checkout the Demo_30B, Demo_30B_bak and the GPT-4 evaluation. WizardLM 30B V1. Now, after screwing around with the new WizardLM-30B-Uncensored (thank you, Mr. Overview. " WizardLM-30B-Uncensored: "You still have one apple left after eating two pears. MT-Bench (Figure-1) The WizardLM-2 8x22B even demonstrates highly competitive performance compared to the most advanced WizardLM 30B Uncensored. Yes, I really loved WizardLM, however I didn't find an issue with personality cohearence but I had optimized and really ground-down my Character's Token count. initial commit over 1 year ago; README. Reload to refresh your session. gitattributes. The following figure compares WizardLM-30B and ChatGPT’s skill on Evol-Instruct testset. 65b at 2 bits per parameter vs. TheBloke/guanaco-7B-GGML. 5 or even gpt-4 and it was never true. cpp team on August 21st 2023. q4_K_M. For WizardLM-30B-V1. A 30B model is able to do this fairly consistently, where as every 13B model struggles to complete the task. Monero's WizardLM Uncensored SuperCOT Storytelling 30B fp16 This is fp16 pytorch format model files for Monero's WizardLM Uncensored SuperCOT Storytelling 30B merged with Kaio Ken's SuperHOT 8K. Solved this one - only 65b solving it properly (only gpt4-alpaca-lora_mlp-65B actually) solve this equation and explain each step 2Y-12=-16 To solve the equation 2Y - 12 = If using ooba, you need a lot of RAM to just load the model (or filepage if you don't have enough RAM), for 65b models I need like 140+GB of RAM (between RAM and pagefile size) WARNING:The safetensors archive WizardLM-30B-GGML I've tested both models using the Llama Precise Preset in the Text Generation Web UI, both are q4_0. Just curious, was the original WizardLM 65b a flop? Reply reply I'm also pretty impressed with wizardlm-30b-uncensored. I get 3-4 token/sec on the 30B model and 1-1. Stable Beluga 2 is an open-access LLM based on the LLaMA 2 70B foundation Overview. The new format is designed to be similar to ChatGPT, allowing for better integration with the Alpaca format and NOTE: The WizardLM-30B-V1. 0 & WizardLM-13B-V1. It is a replacement for GGML, which is no longer supported by llama. Obviously, this is highly subjective, and I can't speak for the "more capable" (and more expensive) offerings, such as Original model card: Eric Hartford's Wizardlm 30B Uncensored This is WizardLM trained with a subset of the dataset - responses that contained alignment / moralizing were removed. Safe. The step up from 30B to 65B is even more noticeable. The tests were performed using the LLaMA-Precise preset. TLDR: you’re looking at 1-2 tokens per second for this WizardLM 30B v1. WizardLM or Wizard-Vicuna. WizardLM-30B-Uncensored-GPTQ. The assistant gives helpful, detailed, and polite answers to the user's questions. Overall, WizardLM represents a significant advancement in large language models, particularly in following complex instructions and achieving impressive performance across various tasks. As of May 2023, Vicuna This is WizardLM trained with a subset of the dataset - responses that contained alignment / moralizing were removed. I've run out of memory on the 30B several times when the context window gets above 2000 or so tokens. First, for the GPTQ version, you'll want a decent GPU with at least 6GB VRAM. 15. WizardLM LLM Comparison. When this dataset is released, a new generation of open source LLMs will be made possible and possibly to surpass GPT3. 0 - GGUF Model creator: WizardLM; Original model: WizardLM 30B v1. 8% of ChatGPT’s performance on average, with almost 100% (or more than) capacity on 18 skills, and more than 90% capacity on 24 skills. WizardLM has been the base for some of the best LLMs currently available. 30B's are very good. Even though the model is instruct-tuned, the outputs (when guided correctly) actually rival NovelAI's Euterpe model. In addition to the base model, the developers You signed in with another tab or window. WizardLM-30B-Uncensored . g. According to the authors, Vicuna achieves more than 90% of ChatGPT's quality in user preference tests, while vastly outperforming Alpaca. TheBloke Update base_model formatting. 1. a 4 bit 30b model, though. Subreddit to The WizardLM-30B model shows better results than Guanaco-65B. 5 on the 65B. json. cpp and libraries and UIs which support this format, such as: The intent is to train a WizardLM that doesn't have alignment built-in, so that alignment (of any sort) can be added For WizardLM-30B-V1. 5 GB of VRAM. 1 contributor; History: 39 commits. 0-4bit and Guanaco-65B-4-bit. 98c19ab about 1 year ago. Some insist 13b parameters can be enough with great fine In my brief testing Wizard unlocked 30B performed better than any of the 65B models in terms of reasoning and following instructions. It does a better job of following the prompt than straight Guanaco, in my experience. 0) trained with 250k evolved instructions (from ShareGPT). 5-turbo) Comparison. Eating two pears does not affect the q3_k_m was better than q4_0 when testing ausboss/llama-30b-supercot. Alpaca-Lora-65b: 880ms / 739ms (20L) Guanaco-65B: 891ms / 737ms (20L) WizardLM-30b: 453ms / 298ms (30L) I don’t have any Intel 13th gen, but for comparison on an m1 MacBook Pro 16gb the same 65b models takes about 35-45 seconds per token on cpu, and enabling metal the model fails to load (obviously). Guanaco 65b is the only (finished) finetune other than ancient Alpaca Loras for 65b so it's 'the best'. cpp. Not sure if this argument generalizes to e. MPT-7B and MPT-30B are a set of models that are part of MosaicML's Foundation Series. Let me know your thoughts and The step up between even the 13B to 30B models is incredible. You switched accounts on another tab or window. What gpu can I buy to run 60/65b? Thanks. md. Confident-Ad-5753 • Exactly, I have similar judgments as Wizard-Vicuna-30B-Uncensored: "You still have one apple left. 0, the Prompt should be as following: "A chat between a curious user and an artificial intelligence assistant. 5 especially with training it on other bases such as MPT, Falcon, RedPajama, and OpenLlama, at sizes up to 40b and 65b, which the community will be thrilled Was thinking of loading up: TheBloke/WizardLM-Uncensored-SuperCOT-StoryTelling-30B-GPTQ But I have seen some 65b models with 2 and 3 bit quantization. Stable Beluga 2. vpswc uqaicb ldyiw sox xnueo cfw uxywisz xptzk yhug wxslhvb