Gptq vs ggml vs gguf reddit

Gptq vs ggml vs gguf reddit. Before complaining that GPTQ is bad please try the gptq-4bit-32g-actorder_True branch instead of the default main. jonathanbesomi. com/in/f Oct 22, 2023 · A Qantum computer — the author and Leonardo. Mixtral 8X7B v0. I have tried running mistral 7B with MLC on my m1 metal. cpp performance: 29. GGML offers a wide range of features, including support for animations, physics simulations, and user input handling. In other words for 7B q5_ks increase perplexity about 1/18th of the Subreddit to discuss about Llama, the large language model created by Meta AI. Only my new bindings, server and ui are under AGPL v3, open to public (other commerical licenses are possibly on a case by case request basis) Feb 29, 2024 · GGUF in a Nutshell. 2 toks. GGML to GGUF is the transition from prototype technology demonstrator to a mature and user-friendy solution. 5 Update 0106. Most people would say there's a noticeable difference between the same model in 7B vs 13B flavors. gguf gpt4-x-vicuna-13B. 3 GB. you will have a limitations with smaller models, give it some time to get used to. We will explore the three common methods for Add a Comment. Sep 8, 2023 · Sep 8, 2023. My graphics card probably can't handle it even at 4bit quantization so I usually prefer the ggml versions. Sabin_Stargem. Runner Up Models: chatayt-lora-assamble-marcoroni. In terms of models, there's nothing making waves at the moment, but there are some very solid 13b options. GGUF is an advanced binary file format for efficient storage and inference with GGML, a tensor library for machine learning written in C. BigCode's StarCoder Plus. 20B models also technically work, but just like the TPU side it barely fits. It's a 15. AWQ, on the other hand, is an activation-aware weight quantization approach that protects salient weights by Feb 21, 2024 · Comparison of GPTQ, NF4, and GGML Quantization Techniques GPTQ. Its upgraded tokenization code now fully accommodates special tokens, promising improved performance, especially for models utilizing new special tokens and custom 🔥 The following figure shows that our WizardCoder attains the third position in the HumanEval benchmark, surpassing Claude-Plus (59. Both should be considered poor. Jan 20, 2024 · Benefits of using GGUF. cpp tree) on the output of #1, for the sizes you want. gguf 15. Make sure your GPU can handle. q8_0 = same as q4_0, except 8 bits per weight, 1 scale value at 32 bits, making total of 9 bits per weight. E. cpp team on August 21st 2023. 1. I tried to convert it myself using ggerganov's script on the fp16 version but the script gets killed before completion. cpp with much more complex and more heavier model: Bakllava-1 and it was immediate success . Wizard-Vicuna-30B-Uncensored. If you plan to run this on a GPU, you would want to use a standard GPTQ 4-bit quantized model. Regarding the supported models, they are GPTQ, AWQ, and GGUF are all methods for weight quantization in large language models (LLMs). 4bit and 5bit GGML models for CPU inference . 34 which isnt released yet and I'm too lazy to build head. You can dig deep into the answers and test results of each question for each quant by clicking the expanders. GGUF doesn't claim [no breaking changes] at all. Sep 4, 2023 · To answer this question, we need to introduce the different backends that run these quantized LLMs. This model scored the highest - of all the gguf models I've tested. New PR llama. Q8_0 marcoroni-13b. I mean do you confirm breaking changes will be needed in the future, at least for 1-bit? Do you see the format could be (easily) extended to that? This repo contains GGUF format model files for Mistral AI's Mistral 7B Instruct v0. You are responsible for anything you do with the model, just as you are responsible for I used GGUF (or its predecessor GGML) when I ran KoboldCpp for CPU-based inference on my VRAM-starved laptop, now I have an AI workstation and prefer ExLlama (EXL2 format) for speed. While on the TPU side this can cause some crashes, on the GPU side it results in very limited context so its probably not worth using a 20B model over its 13B version. w2 tensors, else GGML_TYPE_Q4_K Anyway. Jun 17, 2023 · For example I've only heard rumours. Nomic. But I have not personally checked accuracy or read anywhere that AutoGPT is better or worse in accuracy VS GPTQ-forLLaMA. As a consequence, the 4 models above all appear in the VRAM vs perplexity Pareto frontier. Thanks to our most esteemed model trainer, Mr TheBloke, we now have versions of Manticore, Nous Hermes (!!), WizardLM and so on, all with SuperHOT 8k context LoRA. No specific conclusions, it's up to you, but the Mistral looks great against the big models. The llama. cpp repo, the difference in perplexity between a 16 bit (essentially full Nov 23, 2023 · In this tutorial, we will explore many different methods for loading in pre-quantized models, such as Zephyr 7B. 5. Recap of what GGUF is: binary file format for storing models for inference. KoboldCpp updated to v1. Finally, NF4 models can directly be run in transformers with the --load-in-4bit flag. Recent advancements in weight quantization allow us to run massive large language models on consumer hardware, like a LLaMA-30B model on an RTX 3090 GPU. Accessibility for CPU Use: One of the main advantages of GGUF is that it allows users to run LLMs on their CPU. gguf bloomq4km. So if you want the absolute maximum inference quality - but don't have Jul 27, 2023 · While comparing TheBloke/Wizard-Vicuna-13B-GPTQ with TheBloke/Wizard-Vicuna-13B-GGML, I get about the same generation times for GPTQ 4bit, 128 group size, no act order; and GGML, q4_K_M. Exllamav2 supports flash attention 2. And Johannes says he believes there's even more optimisations he can make in future. 2821207 last layer = 6 = 4. In both cases I'm pushing everything I can to the GPU; with a 4090 and 24gb of ram, that's between 50 and 100 tokens per second (GPTQ has a much more variable Aug 2, 2023 · What are the core differences between how GGML, GPTQ and bitsandbytes (NF4) do quantisation? Which will perform best on: a) Mac (I'm guessing ggml) b) Windows. 2) and a Wikipedia dataset. last layer = 8 = 4. A 65b model quantized at 4bit will take more or less half RAM in GB as the number parameters. 36. This model has been finetuned from LLama 13B Developed by: Nomic AI That's a good question -- and I've been wondering myself if I could just convert a GPTQ model into other formats like MLC and CoreML. Ah, or are you saying GPTQ is GPU focused unlike GGML in GPT4All, therefore GPTQ is faster in MLC Chat? So my iPhone 13 Mini’s GPU drastically outperforms my desktop’s Ryzen 5 3500? Bingo. On the other HAND, GPTQ models are optimized for GPUs, providing faster inference on these hardware platforms. One way to evaluate whether an increase is noticeable it so took at the perplexity increase between a f16 13B model and a 7B model: 0. The main features of the GPTQ algorithm are: 2. So on 7B models, GGML is now ahead of AutoGPTQ on both systems I've tested. • 15 days ago • Edited 15 days ago. For reference, I'm used to 13B models generating at 2T/s, and 7B models at 4 T/s. py script that light help with model conversion. But I did hear a few people say that GGML 4_0 is generally worse than GPTQ. Xwin, Mythomax (and its variants - Mythalion, Mythomax-Kimiko, etc), Athena, and many of Undi95s merges all seem to perform well. And switching to GPTQ-for-Llama to load the Wizard Vicuna 13B: 4bit GPTQ models for GPU inference . I have tried running llama. r/LargeLanguageModels. Results from Q8 and Q5_K_M were nearly identical - this model quantizes nicely. Q8_0 All Models can be found in TheBloke collection. And many of these are 13B models that should I am in the middle of some comprehensive GPTQ perplexity analysis - using a method that is 100% comparable to the perplexity scores of llama. And GGML 5_0 is generally better Pashax22. GGUF won't change the level of hallucination, but you are right that most newer language models are quantized to GGUF, so it makes sense to use one. This is a post-training quantization technique that helps to fill large language systems to be more efficient without significantly affecting their performance. 5 13B model as SoTA across 11 benchmarks, outperforming the other top contenders including IDEFICS-80B, InstructBLIP, and Qwen-VL-Chat. VRAM Usage: GPTQ is typically faster and requires less VRAM, but it may exhibit a slight decrease in intelligence. And it kept crushing (git issue with description). linkedin. The major reason I use exl2 is speed, like on 2x4090 I get 15-20 t/s at 70b depending of the size, but GGUF I get like tops 4-5 t/s. They are the same thing. GPTQ. 1. Do you know of any github projects that I could replace GPT4All with that uses CPU-based GPTQ in Python? If the GGML is slow on your Mac M1, I suspect that you have installed Python x86 instead of ARM64, which will cause 10x slower inference on GGML. Regarding HF vs GGML, if you have the resources for running HF models then it is better to use HF, as GGML models are quantized versions with some loss in quality. A good starting point for assessing quality is 7b vs 13b models. #ggml #gptq PLEASE FOLLOW ME: LinkedIn: https://www. There's definitely quality differences, at least in terms of code generation. If you want to use a CPU, you would want to run a GGML optimized version, this will let you leverage a CPU and system RAM. 5 7B and 13B released: Improved Baselines with Visual Instruction Tuning. For CoreML, I understand that the model has to be first converted into torch script, and then the a trace needs to take place prior to starting the conversion. I've tried googling around but I can't find a lot of info, so I wanted to ask about it. It even beat many of the 30b+ Models. You can run 65B models on consumer hardware already. Apparently GGUF K-Quants already approach this a bit similar, but only based on the architectural meaning of the weights. the orca guys mentioned they generated a massive step by step solutions dataset from gpt3-4 and used that to turn llama into orca. cpp now makes ggufs. The ggml file contains a quantized representation of model weights. I had mentioned on here previously that I had a lot of GGMLs that I liked and couldn't find a GGUF for, and someone recommended using the GGML to GGUF conversion tool that came with llama. Here's some more info on the model, from their model card: Model Description. Only the GPTQ models. GGML models are specifically designed to run efficiently on CPUs, offering faster inference speed compared to GPTQ models. I've been trying to try different ones, and the speed of GPTQ models are pretty good since they're loaded on GPU, however I'm not sure which one would be the best option for what purpose. Yeah, 13b is likely the sweet spot for your rig. • 7 mo. If you can fit it in GPU VRAM, even better. Quantized in 8 bit requires 20 GB, 4 bit 10 GB. gguf appears in both Pareto frontiers, so it The ggml/gguf format (which a user chooses to give syntax names like q4_0 for their presets (quantization strategies)) is a different framework with a low level code design that can support various accelerated inferencing, including GPUs. Therefore, lower quality. MythoMax or Stheno L2, both do better at that than Nous-Hermes L2 for me. At least that's what I got from the description in the "older" bloke quants. Introducing LLM-Powered Robots: MachinaScript for Robots. Is this enough to justify continuing to provide quants of multiple group and act order combos? i understand that GGML is a file format for saving model parameters in a single file, that its an old problematic format, and GGUF is the new kid on the block, and GPTQ is the same quanitized file format for models that runs on GPU. However, on 8Gb you can only fit 7B models, and those are just dumb in comparison to 33B. For GPTQ models, we have two options: AutoGPTQ or ExLlama. On windows, run . File formats like GGML and GGUF for local Large Language Models (LLMs) have democratized access to this technology and reduced the costs associated with running language models. 55bpw vs GGUF Q6_K that runs at 2-3 t/s. Get a GPTQ model, DO NOT GET GGML OR GGUF for fully GPU inference, those are for GPU+CPU inference, and are MUCH slower than GPTQ (50 t/s on GPTQ vs 20 t/s in GGML fully GPU loaded). cpp tree) on pytorch FP32 or FP16 versions of the model, if those are originals. 5. Sort by: [deleted] • 9 mo. 7 GB, 12. So I loaded up a 7B model and it was generating at 17 T/s! I switched back to a 13B model (ausboss_WizardLM-13B-Uncensored-4bit-128g this time) and am getting 13-14 T/s. Did not test GGUF yet, but is pretty much GGML V2. Most people would agree there is a significant improvement between a 7b model (LLaMA will be used as the reference) and a 13b model. GPTQ is for cuda inference and GGML works best on CPU. To recap, LLMs are large neural networks with high-precision weight tensors. Reply reply More replies More replies can-ai-code has been updated to add this family, I performed evaluation via GGUF since the fp16 needs transformers 4. Good to know. These are outdated and only older applications run them now GGUF is the newer version of GGML. 3. I'm aware that GGML's perplexity performance has improved significantly lately. When you finish making your gguf quantized model, please upload it to HF. Context is hugely important for my setting - the characters require about 1,000 tokens apiece, then there is stuff like the setting and creatures. That's why it's not on my lists. I'm running the webui on Windows WSL and I used the same conda environment to run the script as well. g. Then the new 5bit methods q5_0 and q5_1 are even better than that. py script in llama. just iterative improvements with better speed and perplexity and renamed and packed with some metadata. 30B it's a little behind, but within touching difference. Run convert-llama-hf-to-gguf. 5B parameter Language Model trained on English and 80+ programming languages. This time, it's Vicuna-13b-GPTQ-4bit-128g vs. • 11 days ago. When using 3 gpus (2x4090+1x3090), it is 11-12 t/s at 6. I noticed that the 3-bit model (type q3_K_S) fits in 16 GB of RAM. I did a comparison of Mistral-7B-0. Ah, I’ve been using oobagooba on GitHub - GPTQ models from the bloke at huggingface work great for me. They report the LLaVA-1. I’ve seen some people saying 1 or 2 tokens per second, I imagine they are NOT running GGML versions. You can try both and see if the HF performance is acceptable. Dead_Internet_Theory. Though I agree with you, for model comparisons and such you need to have deterministic results and also the best Aug 3, 2023 · Learning Resources:TheBloke Quantized Models - https://huggingface. The . Jul 13, 2023 · GPTQ versions, GGML versions, HF/base versions. It’s also designed for rapid model loading. 8 vs. co/docs/optimum/ llama-2-13b-EXL2-4. I've been interested in trying out this exact model to test its translation capability. Discussion. Most people are moving to GGUF over GPTQ, but the reasons remain the same on way exl2 isn't growing. Jun 13, 2023 · Update to include TheBloke_Wizard-Vicuna-13B-Uncensored-GPTQ GPTQ-for-LLaMa VS Auto GPTQ VS ExLlama (This does not change GGML test results. Looking forward to seeing how L2-Dolphin and L2-Airoboros stack up in a couple of weeks. cpp. Update: Works with latest llama. There's a great table here and here with perplexity measurements. Memory inefficiency problems. exl2 supports quant sizes other than 4-bit so are just as flexible as GGUF, if not more so. cpp GGML models, so we can compare to figures people have been doing there for a while. This is possible thanks to novel 4-bit quantization techniques with minimal performance degradation, like GPTQ, GGML, and NF4. According to the chart in the llama. ggml is totally deprecated, so much so that the make-ggml. cpp's GGML) that has awesome performance but supports only GPU acceleration. According to open leaderboard on HF, Vicuna 7B 1. I know this post is a bit older, but I put together a model that I think is a pretty solid NSFW offering. Llama 2 download /r/StableDiffusion is back open after the protest of Reddit killing open API access, which will bankrupt app developers, hamper Nov 14, 2023 · With sharding, quantization, and different saving and compression strategies, it shouldn’t be easy to know which method is suitable for you. co/TheBlokeQuantization from Hugging Face (Optimum) - https://huggingface. AI's original model in float32 HF for GPU inference. q4_1. Apr 27, 2023 · GPTQ scores well and used to be better than q4_0 GGML, but recently the llama. float16 HF format model for GPU inference . GGUF boasts extensibility and future-proofing through enhanced metadata storage. cpp team on August 21, 2023, replaces the unsupported GGML format. Try using 13b @ 4bit. I meant claimed about the format (on Reddit, so maybe I was misinformed). 8, GPU Mem: 4. Since you don't have GPU, I'm guessing HF will be much slower than GGML. The original ggml libraries and llama. Reply reply MLC vs llama. This model can run with 16 GB of RAM. 650b has lower perplexity than llama-2-13b-GPTQ-4bit-32g-actorder and is smaller (on disk), but it uses more VRAM. Which version should you use? As a general rule: Use GPTQ if you have a lot of VRAM, use GGML if you have minimal VRAM, and use the base HuggingFace model if you want the original model without any possible negligible intelligence loss from quantization. I am going to get this tattoed on my forehead, main is for "compatibility" with ancient forks of autogptq that dont run codellama anyway: Most compatible option. One thing I noticed in testing many models - the seeds. And in my GGML vs GPTQ tests, GGML did 20 t/s, GPTQ did 50 t/s at 13B. Mixtral GGUF GPTQ is an alternative method to quantize LLM (vs llama. py (from llama. 5). Jan 10, 2024 · 6 min read. llama-2-13b-Q4_K_S. It is a replacement for GGML, which is no longer supported by llama. c) T4 GPU. As for questions - yes ggml is for kobold cpp, it already supports q4_3. 4bit GPTQ models for GPU inference. 44. Performance: GPTQ is capable of quantizing transformer models from the beginning, although it may entail a longer quantization process. GPT-4-x-Alpaca-13b-native-4bit-128g, with GPT-4 as the judge! They're put to the test in creativity, objective knowledge, and programming capabilities, with three prompts each this time and the results are much closer than before. So far, I've run GPTQ and bitsandbytes NF4 on a T4 GPU and found: fLlama-7B (2GB shards) nf4 bitsandbytes quantisation: - PPL: 8. Note that GGML is working on improved GPU FP16 (16bit) model required 40 GB of VRAM. It's my favorite type! These run in Llamacpp. Bigger but 4 bits generally beats smaller but 8 bits. Thus, q4_2 is just a slightly improved q4_0. e. Good inference speed in AutoGPTQ and GPTQ-for-LLaMa. New comments cannot be posted and votes cannot be cast. 2 GB or Q4_K_S at 18. GGUF is a single file, it looks like exl2 is still a mess of files. 1 GPTQ 4bit runs well and fast, but some GGML models with 13B 4bit/5bit quantization are also good. 4bit and 5bit GGML models for GPU inference. You can run perplexity measurements with awq and gguf models in text-gen-webui, for tbh, no idea, you just pick all this up as a general knowledge while reading 4chan. I find them just good for chatting mostly more technical peeps use them to train. py --threads 4 --mlock. For GGML models, llama. Monero's WizardLM-Uncensored-SuperCOT-Storytelling-30B-GGML 2 bit model available. GPTQ is a one-shot weight quantization method based on approximate second-order information, allowing for highly accurate and efficient quantization of GPT models with 175 billion parameters. A quick glance would reveal that a substantial chunk of these models has been quantified by TheBloke, an influential and respected figure in the LLM community. I suppose I might as well give it a try. GPTQ just didn't play a major role for me, and I considered the many options (act order, group size, etc. For ex, `quantize ggml-model-f16. That's it. I haven't tried this yet, but I guess it should be possible to make the multimodal extension work with llamacpp_hf by adding some 5 lines q5_1 = 32 numbers in a chunk, 5 bits per weight, 1 scale value at 16 bit float and 1 bias value at 16 bit, size is 6 bits per weight. llama. As far as I'm aware, GPTQ 4-bit w/ Exllama is still the best option. This is particularly beneficial for users who may not own This video explains difference between GGML and GPTQ in AI models in very easy terms. GPTQ and ggml. StarCoderPlus is a fine-tuned version of StarCoderBase on 600B tokens from the English web dataset RedefinedWeb combined with StarCoderData from The Stack (v1. This user has published LLaVA-1. easy to use (with a few lines of code) mmap (memory mapping) compatibility: models can be loaded using mmap for fast loading and saving. Aug 23, 2023 · GGUF, introduced by the llama. wojtek15. designed for fast loading and saving of models. Now I have a task to make the Bakllava-1 work with webGPU in browser. It's what you'd expect, although I found the larger models seem to be more resistant than the smaller ones. GGUF, previously GGML, is a quantization method that allows users to use the CPU to run an LLM but also offload some of its layers to the GPU for a speed up. cpp version. ) confusing. https://huggingface. Also, if you're on a mac, you probably want to run these. 6. 11 tokens/s. This 13B model was generating around 11tokens/s. \quantize ggml-model-f16. ago. bin 3 1` for the Q4_1 size. 0 dataset. Seems like 8 bit models score effectively the same as the full precision 16bit, but the larger 13b models quantized down to 4bit still scored better than any precision 7b model. GGUF has its place for CPU/GPU inference, but so do GPU optimised formats. To interact with these files, you need to use llama. d) A100 GPU. 9 GB, while the most comparable GGML options are Q3_K_L at 17. VRAM Usage: GGML is more efficient in terms of VRAM usage. r/LocalLLaMA. 33B you can only fit on 24GB VRAM, even 16Gb are not enough. Here's what you need to research the popular gguf/ggml models. cpp repository contains a convert. ADMIN MOD. GPTQ stands for “Generative Pre-trained Transformer Quantization”. ) Test 3 TheBloke_Wizard-Vicuna-13B-Uncensored-GPTQ GPTQ-for A helpful commenter on github (xNul) says "you're trying to run a 4bit GPTQ model in CPU mode, but GPTQ only exists in GPU mode. it's possible to do a comparison of GGUF q5_k_m Vs exl2 b5 h6, but there is no such option for GPTQ. ". i. GGUF and GGML are file formats used for storing models for inference, especially in the context of language models like GPT (Generative Pre-trained Transformer). cpp are still available under the MIT license within the parent repository. Only ChatGPT, Claude and Mira (custom russian model) was able to answer the question "Where are Charles Dickens and Charles Darwin GGML runs on a combination of graphics card and cpu. Uses GGML_TYPE_Q6_K for half of the attention. An uncensored model has no guardrails. That's what I understand. Start by googling Local Models General there. But GGML allows to run them on a medium gaming PC at a speed that is good enough for chatting. The benefit is 4x less RAM requirements, 4x less RAM bandwidth requirements, and thus faster inference on the CPU. AutoGPTQ CUDA 30B GPTQ 4bit: 35 tokens/s. Yes these formats take more time/compute to quant than GGUF, but they are worth it in many use cases. co/TheBloke. LostMagic-. (2) And does the mean we'd do well to download new GPTQ quants of our favorite models in light of the new information? (3) I'm also still a bit curious of GGML is competitive with GPTQ/exllama when running on Nvidia GPU. Aug 2, 2023 · GGML (Generic Game Markup Language) is a powerful tool specifically designed for game development. 53. Ask and you shall receive my friend, hit up can-ai-code Compare and select one of the Falcon 40B GGML Quants flavors from the analysis drop-down. GPTQ is better, when you can fit your whole model into memory. wv and feed_forward. Vicuna 13B, my fav. About GGUF GGUF is a new format introduced by the llama. I'll be posting those this weekend. ai The 2 main quantization formats: GGML/GGUF and GPTQ. gguf. I've been going down huggingface's leaderboard grabbing some of Some insist 13b parameters can be enough with great fine tuning like Vicuna, but many other say that under 30b they are utterly bad. It provides a simple and intuitive way to describe game elements, such as characters, levels, and objects, using a markup language. This is different from LLaVA-RLHF that was shared three days ago. Compare one of thebloke's descriptions to the one you linked. ) Prompts Various (I'm not actually posting the question/answers it's irreverent for this test as we are checking speeds. • 6 mo. TheBloke has released "SuperHot" versions of various models, meaning 8K context! Discussion. If llamacpp model is slow in oobabooga, try to run it with following parameters: python server. Sep 19, 2023 · Fortunately it is possible to find many versions of models already quantized using GPTQ (some compatible with ExLLama), NF4 or GGML on the Hugging Face Hub. GGUF principles guarantee that all essential information for model loading is encapsulated within a single file. Any further functional differences are unintentional. 24, supports new GGJT v3 quantizations while still maintaining full backwards compatibility. The 4bit version still requires 12gb vram. • 9 mo. 240 tokens/s achieved by Groq's custom chips on Lama 2 Chat (70B) r/LocalLLaMA. I understand running in CPU mode will be slow, but that's ok. Both models aim to maintain similar inference quality. 6523. Reply reply Accomplished_Bet_127 GPTQ model support is also being considered for Colab, but won't happen before GPTQ is inside United. 1 - GGUF Model creator: Mistral AI_ Original model: Mixtral 8X7B v0. Reply reply. Az-Bats. To illustrate, Guanaco 33b's GPTQ has a file size of 16. Although GPTQ does compression well, its focus on GPU can be a disadvantage if you do not have the hardware to run it. This is where GGML comes in. cpp with Q4_K_M models is the way to go. Hopefully, the L2-70b GGML is an 16k edition, with an Airoboros 2. GGML. cpp team have done a ton of work on 4bit quantisation and their new methods q4_2 and q4_3 now beat 4bit GPTQ in this benchmark. On my old cpu (Xeon E3-1225 v3 4/8), it runs with ~660 ms per token. 0) and Bard (59. It's true that GGML is slower. 1-GPTQ, its finetunes, some 13B models, Llama-70B-chat and the GPT-3. true. Download a model which can be run in CPU model like a ggml model or a model in the Hugging Face format (for example "llama-7b-hf"). 2. The people doing exl2 also are putting a bunch of data no one is reading in their description instead of useful things. :) ppl increase is relative to f16. The model uses Multi Query Attention, a GGML has done a great job supporting 3-4 bit models, with testing done to show quality, which shows itself as a low perplexity score. if the 65b model is good enough to be such a teacher, maybe a similar dataset can be created and used to train an open-orca. BubblyGrade6133. GGUF and GGML are file formats for quantized models developed by Georgi Gerganov. So from the results at 4 bit we see that GPTQ just about holds out to remain respectable. 282587 Jan 17, 2024 · Optimized Models for CPU and GPU. Nov 13, 2023 · GGUF: GPT-Generated Unified Format. An upgrade! They run on a combination of graphics card and cpu. GPTQ runs purely on your video If anyone is interested in what the last layer bit value does (8 vs 6 bit), it ended up changing the 4th decimal place. Notably, our model exhibits a substantially smaller size compared to these models. Jul 31, 2023 · Quantize your own LLMs using AutoGPTQ. I know there is one for miniGPT4 but it just doesn’t seem as reliable as LLaVA but you need at least 24gb of vRAM for LLaVA to run it locally by the looks of it. I can confirm that certain modes or models are faster or slower of course. It also has a use case for fast mixed ram+vram inference. Run quantize (from llama. The older GGML format revisions are unsupported and probably wouldn't work with anything other than KoboldCCP since the Devs put some effort to offer backwards compatibility, and contemporary legacy versions of llamaCPP. However, I'm curious if it's now on par with GPTQ. Here is an incomplate list of clients and libraries that are known to support GGUF: llama. Dunjeon/lostmagic-RP-001_7B · Hugging Face. Throughout the examples, we’ll use Zephyr 7B, a fine-tuned variant of Mistral 7B that was trained with Direct Preference Optimization (DPO). IMO, this comparison is meaningful because GPTQ is currently much New Model: OpenChat 3. 1; Description This repo contains GGUF format model files for Mistral AI_'s Mixtral 8X7B v0. Let’s explore the Parameter size and perplexity. Like for q4_k_m: New k-quant method. •. lm tc xn di bu ni of tr iy ot