- Gguf to ggml reddit bin weights. maybe oogbabooga itself offers some compatibility by running different loader for ggml, but i did not research into this. 5B looks like it should be better than Marx-3B, but I'm waiting for a GGUF conversion before I try it. The differences from GGML is that GGUF use less memory. However, I'm curious if it's now on par with GPTQ. extopico • Well, since I am python based, I used the current llama-cpp-python with Falcon-falcon-180b-chat. Reply reply Barafu I am unsure if I should be using exl2 here from gradient with the 1m context option or 70b gguf somehow offloaded to vram/ram/cpu as I see some users able to run the full model locally. 1 65b 8k that had the best quality, but it took something like 40+ minutes to get output. Solution: Edit the GGUF file so it uses the correct stop token. bin. Hi all, anyone have a recommendation for a ggml model fine tuned for code? Edit: not too concerned on language, python preferred but most languages Skip to main content I was getting 0. Only returned to ooba recently when Mistral 7B came out and I wanted to run that unquantized. If I convert using GGML's convert-hf-to-ggml. Some of you have requested a guide on how to use this model, so here it is. py Sounds good, but is there a documentation or a webpage or Reddit thread where I can learn more pratical usage details about all of those? I'm not talking about academic explanations but real world differences for usage in local contexts. I just can't find a solution. 16 GB At the end of the training run I got "save_as_llama_lora: saving to ggml-lora-40-f32. They both seem to prefer shorter responses, and Nous-Puffin feels unhinged to me. This reddit thread got me started down this rabbit hole. I would like to know if some of you have similar experience, here are some more details : Because we're discussing GGUFs and you seem to know your stuff, I am looking to run some quantized models (2-bit AQLM + 3 or 4-bit Omniquant. Expand user menu Open settings menu. if you use cpu, you need to have a . ppl increase is relative to f16. I use oobabooga (for GGUF and exl2) and LMStudio. 17 layers on 3060 12. When you find his page with that model you like in gguf, scroll down till you see all the different Q’s. Llama. Or check it out in the app stores TOPICS Only used ggml/gguf before Just run python convert-lora-to-ggml. Reply reply MrBabai Ive setup different conda environments for GGML, GGUF, AND GPTQ. gguf and it worked. I believe Pythia Deduped was one of the best performing models before LLaMA came along. py , it complains "NotImplementedError: Architecture "GPT2LMHeadModel" not supported!" View community ranking In the Top 5% of largest communities on Reddit. And I can't know for sure, but I have an inkling this happened ever since I started using GGUF and ever since oobabooga opushed GGUF onto us. All hail GGUF! Allowing me to host the fattest of llama models on my home computer! With a slight performance loss, you gain It took about 10-15 minutes and outputted ggml-model-f16. If I try to convert using llama. 1-yarn-64k. 306 votes, 55 comments. gguf Q6_K Get the Reddit app Scan this QR code to download the app now. rs (ala llama. You can't run models that are not GGML. gguf" and that file is only 42 MB. difference is, q2 is faster, but the answers are worse than q8 In Google Colab, though have access to both CPU and GPU T4 GPU resources for running following code. gguf 2048 0 999 0 128,256,512 128,256 1,2,4,8,16,32. I get, on average, 0. cpp but the speed of change is great but not so great if it's breaking things. Members Online What's interesting, I wasn't considering GGML models since my CPU is not great and Ooba's GPU offloading well, doesn't work that well and all test were worse than GPTQ. Now that we have our f16, we can quantize the result into any format we'd like: . Thanks for taking the time to read my post. GGUF works with CPU and GPU but the rest only work on your GPU (so the VRAM is the limit, it could use the shared memory but that's usually much slower). I've been exploring llama cpp to expedite generation time, but since my model is fragmented, I'm seeking guidance on converting it into gguf format. FAQ Q: What is Wizard-Vicuna A: Wizard-Vicuna combines WizardLM and VicunaLM, two large pre-trained language models that can It feels like the hype for autonomous agents is already gone. does HF Transformers support loading GGUF or GGML models ? and does GGUF needs a tokenizer json or does the data comes from within the gguf file itself and is safetensors (another file format) supported by both Transformers and Llama. If you want to convert your already GGML model to GGUF, there is a script in llama. Or check it out in the app stores renaming it to "airoboros-llama-2-70b-gpt4-m2. **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 from unsloth import FastLanguageModel model, tokenizer = FastLanguageModel. I am planing to use retrieval augmented generation (RAG) based chatbot to look up information from documents (Q&A). safetensors files once you have your f16 gguf. 5-7b This subreddit is currently closed in protest to Reddit's upcoming API changes that will kill off 3rd party apps and negatively impact users and mods alike. Hopefully this post will shed a little light. Georgi Gerganov (creator of GGML/GGUF) just announced a HuggingFace space where you can easily create quantized model version I've just fine-tuned my first LLM and its generation time surpasses 1-2 minutes ( V100 Google Colab). github. Everyone with nVidia GPUs should use faster-whisper. The modules we can use are GGML or GGUF, known as Quantization Modules. View community ranking In the Top 5% of largest communities on Reddit. Now I tested out playing adventure games with KoboldAI and I'm really enjoying it. It doesn't get talked about very much in this subreddit so I wanted to bring some more attention to Nous Hermes. Let’s explore the key 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. js v1. cpp your mini ggml model from scratch! these are currently very small models (20 mb when quantized) and I think this is more fore educational reasons (it helped me a lot to understand much more, when koboldcpp can't use GPTQ, only GGML. Log In / Sign Up; Advertise on Reddit; Shop Collectible Avatars; Then I tried renaming the gguf file to ggml-llama-7B. let's assume someone wants to use the strongest quantization (q2_k), since it is about There are many levels of quantization for GGUF/GGML model, typically denoted as q4, q5, etc. To illustrate, Guanaco 33b's GPTQ has a file size of 16. Please use our Discord server instead of supporting a company that acts against its users and unpaid moderators. cpp and they were not able to I heard opposite, that the GGUF/GGML was fucked. The lower the resolution (Q2, etc) the more detail you lose during inference. Offering fewer GGUF options - need feedback Run convert-llama-hf-to-gguf. Try nvcc --version. js lets you play around with language models right in your browser, thanks to WebAssembly. I have suffered a lot with out of memory errors and trying to stuff torch. stay tuned The quantization method of the GGML file is analogous in use the resolution of a JPEG file. 5 tokens on i5-10600 CPU for a 4. I'll just force a much earlier version of oobabooga and ditch GGUF altogether. I'm new to this field, so please be easy on me. But then when I tested them, they produced I am in the process of making GGUF quants for those three new >100B models and they'll be uploaded over the You can try my fav - wizardlm-30b-uncensored. It seems like either your models or the version of llama you're using is outdated, or at A new release of model tuned for Russian language. On my 2070 I get twice that performance with WizardLM-7B-uncensored. 1st question: I read that exl2 consume less vram and work faster than gguf. More info: When you want to get the gguf of a model, search for that model and add “TheBloke” at the end. In this latest release, here's what's in store: 1️⃣ Expanded Format Support: Now GGUF/GGML formats are fully supported, thanks to the latest llama. bin) and then selects the first one ([0]) returned by the OS - which will be whichever one is alphabetically first, basically. \quantize ggml-model-f16. Compared to ggml version. 🌐 LLM. bin, etc. py script from the llama. What do you need to overclock your computer to get more tokens in a second? 81 votes, 98 comments. What are your thoughts on GGML BNF Grammar's role in autonomous agents? After some tinkering, I'm convinced LMQL and GGML BNF are the heart of autonomous agents, they construct the format of agent interaction for task creation and management. tvetus • Additional 115 votes, 34 comments. 4_0 will come before 5_0, 5_0 will come before 5_1, a8_3. But it was a while ago, probably that has been fixed already. I was trying to use the only spanish focused model I found "Aguila-7b" as base model for localGPT, in order to experiment with some legal pdf documents (I'm a lawyer exploring generative ai for legal work). For the easiest way to run GGML, try koboldcpp. q3_K_S. Then I checked the /r/StableDiffusion is back open after the protest of Reddit killing open API access, which will bankrupt Get app Get the Reddit app Log In Log in to Reddit. gguf model(or ggml but they are old and dont work good for me) gptq(and AWQ/EXL2 but not 100% sure about these) is gpu only gguf models have different quantisation. GGUF, exl2 and the rest are "rips" like mp4 or mov, of various quality, which are more user-friendly for "playback". txt # convert the 7B model to ggml FP16 format python3 convert. bin cp models/7B/ggml-tokenizer. Log In / Sign Up; Getting completely random stuff with LlamaCpp when using the llama-2-7b. Or to GGUF format using the convert-hf-model-to-gguf. With Koboldcpp that only supports GGUF models and the original KoboldAI only supports unquantized models. These are the speeds I am currently getting on my 3090 with wizardLM-7B. In case anyone finds it helpful, here is what I found and how I understand the current state. com/philpax/ggml/blob/gguf-spec/docs/gguf. 68 Nvidia driver (so I recieve OOM, not RAM-swapping when VRAM overflows). It has a pretrained CLIP model(a model that generates image or text embedding in the same space, trained with contrastive loss), a pretrained llama model and a simple linear projection that projects the clip embedding into text embedding that is prepended to the prompt for the llama model. GGML Guide . simple prompt script to convert hf/ggml files to gguf, and to quantize Resources 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). No problem. cpp since i cannot find python examples for these combination i assume all the answers are - No So I heard about this new format and was wondering if there is something to run these models like how Kobold ccp runs ggml models. You can also try q4 ggml and split between CPU and GPU, but it will be significantly slower. First you'd have to add that dataset to a model, which is called Fine-tuning. I was actually the who added the ability for that tool to output q8_0 — what I was thinking is that for someone who just wants to do stuff like test different quantizations, etc being able to keep a nearly original quality It works but you do need to use Koboldcpp instead if you want the GGML version. I would hesitate buying those old platforms for so many reasons. Considering you are using a 3090 and also q4, you should be blowing my 2070 away. ggml is totally deprecated, so much so Hugging Face Hub supports all file formats, but has built-in features for GGUF format, a binary format that is optimized for quick loading and saving of models, making it highly efficient for inference purposes. Problem: Llama-3 uses 2 different stop tokens, but llama. 9 tokens per second. There's definitely quality differences, at least in terms of code generation. g. Get the Reddit app Scan this QR code to download the app now. Large number means more accurate to the full model, higher quality, require more memory. cpp team on August 21st 2023. 1TB, because most of these GGML/GGUF models were only downloaded as 4-bit quants (either q4_1 or Q4_K_M), and the non-quantized models have either been trimmed to include just the PyTorch files or just the safetensors files. ggml. py path_to_lora_dir to convert pytorch lora model u/Astronomer3007. Sample questions: do I need ggml to run on cpu with llama. 5-2 tokens a second running the 33b models" "With ggml files /r/StableDiffusion is back open after the protest of Reddit killing open API access, which will . cpp? given the dangers, should I only use safetensors? I keep having this error, can anyone help? 2023-09-17 17:29:38 INFO:llama. the procedure is still as described above. It took up a slight bit more memory so maybe you get less context on the 34b on one 3090. ggmlv3. cpp tree) on the output of #1, for the sizes you want. gguf (which is based on goliath) shows the same /r/StableDiffusion is back open after the protest of Reddit killing open API access, which will 38 votes, 12 comments. For CUDA: 0) Make sure you have CUDA installed. The Real Housewives of Atlanta; The Bachelor; Sister Wives; 90 Day Fiance; Wife Swap; The Amazing Race Australia; Married at First Sight; The Real Housewives of Dallas I'm aware that GGML's perplexity performance has improved significantly lately. bin, which is about 44. I run it on 1080Ti and old threadripper with 64 4-channel DDR4-3466. That should be enough to completely load these 13B models. Or check it out in the app stores IMHO going the GGML / llama-hf loader seems to currently be the better option for P40 users, I recommend using GGUF models with the llama. Most people would say there's a noticeable difference between the same model in 7B vs 13B flavors. That's it! I took a look at Huggingface but there are no premade 180b ggml Falcon models. 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. I don't even know if it actually is the problem I'm just going based off what I read elsewhere. Ran on 3 GPUs and took a lot longer than expected, but finally got my output on custom training data (added to the 52k records utilized by Alpaca). So with all the files that were called GGML, you had to If you want even smaller I would ask on the llama. save_pretrained_gguf("gguf_model", tokenizer, quantization_method = Hello everyone. bin file for the model. There are no errors. So using oobabooga's webui and loading 7b GPTQ models works fine for a 6gb GPU like I have. gguf file is both way smaller than the original model and I can't load it (e. 4060 16GB VRAM i7-7700, 48GB RAM emerhyst-20b. Run Start_windows, change the model to your 65b GGML file (make sure it's a ggml), set the model loader to llama. This lets you use a standard llama 2 model with 8k context instead of 4k context with minimal loss of perplexity (aka it’s not much dumber this way) even without someone finetuning the model for I literally used F3 and looked through every mention of GGML on the github page, all 110 of them and still found 0 things on how to convert GGML to GGUF. It is a replacement for GGML, which is no longer supported GGUF, introduced by the llama. cpp, so I did some testing and GitHub discussion reading. Yes, Miqu is probably a leak. This enhancement allows for better support of My plan is to use a GGML/GGUF model to unload some of the model into my RAM, leaving space for a longer context length. Reply reply Back in the GGML days sometimes I could find a model and ROPE combo that had better results than Amethyst 12b, but those were very slow in comparison. I would love to, but my workflows using the NanoGPT repository, so I'm sure how I convert to GGML/GGUF, This is our attempt at a reddit hub for users of some of our DIY products, as well as being able to provide some user support. cpp, and the resulting . Follow the above directions. 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 GGML and GGUF refer to the same concept, with GGUF being the newer version that incorporates additional data about the model. cpp’s export-lora utility, but you may first need to use convert-lora-to-ggml. cpp releases and the ggml conversion script can be found by Googling it (not sure what the 95 votes, 63 comments. Just like the codecs, the quantization formats change sometimes, new technologies emerge to improve the efficiency, so what once was the gold standard (GGML) is now obsolete (remember DivX?) The GGML (and GGUF, which is slightly improved version) quantization method allows a variety of compression "levels", which is what those suffixes are all about. So i have this LLaVa GGUF model and i want to run with python locally , Using LMStudio with ggml_llava-v1. More posts you may /r/StableDiffusion is back open after the protest of Reddit killing open API access, which will bankrupt app Here I show how to train with llama. py if the LoRA is in safetensors. How: prerequisite: You must have llama. gguf 15. cpp, and the latter requires GGUF/GGML files). This script will not work for you. Don't do anything after #I assume you use cuda here, check the link otherwise. The AI seems to have a better grip on longer conversations, the A place to discuss the SillyTavern fork of TavernAI. 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. cpp has no CUDA, only use on M2 macs and old CPU machines. 6. Edit: just realized you are trying convert an already converted GGML file in Q4_K_M to GGUF. gguf in textgen webui with 2x 4090s and 64gb of ram? Question | Help I've only used gptq models and I can't get this working. You need to use the HF f16 full model to use this script. I'm running it on a MacBook Pro M1 16 GB and I can run 13B GGML models quantised with 4. Ggml and llama. gguf models/Rogue-Rose-103B. from_pretrained("lora_model") model. While this post is about GGML, the general idea/trends should be applicable to other types of quantization and models, for example GPTQ. Members Online. And possibly a Mistral Medium leak, or at the very least, a Llama 2 70b tuned on a Mistral dataset (internal or recomposed via Q/A pairs made on Mistral or Mixtral). cpp weights detected: models\airoboros-l2-13b-2. Run the following command to launch LLaMa. /models 65B 30B 13B 7B vocab. Reply reply More replies More replies AwayConsideration855 Agreed on the transformers dynamic cache allocations being a mess. Q2_K. Quantization is a common technique used to reduce model size, although it can sometimes result in reduced accuracy. IMO, this comparison is meaningful because GPTQ is currently much faster. I got a laptop with a 4060 inside, and wanted to use koboldcpp to run my models. It's safe to delete the . py tool is mostly just for converting models in other formats (like HuggingFace) to one that other GGML tools can deal with. I'm not wanting to use GGML for its performance, but rather I don't want to settle for the accuracy GPTQ provides. Use llama. The samples from the developer look very good. Note: Replace `7B` in the above commands with the desired model size if you have a different model. bin llama. cpp and have been going back to more than a month ago (checked out Dec 1st tag) i like llama. Yeah, it will perform objectively worse than 4k natural context. Or check it out in the app stores you need to have like 80% of the ggml model layers in GPU memory to meaningfully accelerate it. 7 MB. cpp only has support for one. Unless they're literally Offering fewer GGUF options i'm interested in TTS & STT. Some people on reddit have reported getting better results with ggml over gptq, GGML runner is intended to balance between GPU and CPU. I'm interested in codegen models in particular. One thing I found funny (and lol'ed first time to an AI was, in oobagoga default ai assistant stubunly claimed year is 2021 and it was gpt2 based. cpp to split with CPU (was GGML now GGUF). Back when I had 8Gb VRAM, I got 1. It consumed 140 GB of RAM and I got We are Reddit's primary hub for all things and what this is saying is that once you've given the webui the name of the subdir within /models, it finds all . It must be 4. In simple terms, quantization is a technique that allows modules to run on consumer-grade hardware but at the cost of quality, depending on the "Level of quantization," as shown Noeda's fork has not been selected mostly because it was treating the architecture as a separate one It was that but the moment I saw a PR had been opened I started treating my work as experimental and I went into bug research mode, with the idea that any problems I find can be cherry-picked to main one; and was not planning to present my work as a PR. Models of this type are accelerated by the Apple Silicon GPU. I like to use 8 bit quantizations, but GPTQ is stuck at 4bit and I have plenty of speed to spare to trade for accuracy (RTX 4090 and AMD 5900X and 128gb of RAM if it matters). cpp is developed by the same guy, libggml is actually the library used by llama. These models are intended to be run with Llama. cpp called convert-llama-ggml-to-gguf. 2023-ggml-AuroraAmplitude This name represents: LLaMA: The large language model. Has anyone experienced something like this? If it's related to GGML, really, I'll accept it. cpp's convert-hf-to-gguf. While GGML BNF is kinda under the radar. Subreddit to discuss about Llama, the large language model created by Meta AI. gguf model Question | Help High context is achievable with GGML models + llama_HF loader Someone has linked to this thread from another place on reddit: [r/datascienceproject] Run Llama 2 Locally in 7 Lines! (Apple Silicon Mac) (r/MachineLearning) If you follow any of the above links, please respect the rules of reddit and don't vote in the other threads. I just like natural flow of the dialogue. true. gguf example: . 3 GB. I have 531. Then I tried to run it with Kobold, and it kept force closing. GGUF is designed for use The main point, is that GGUF format has a built-in data-store ( basically a tiny json database ), used for anything they need, but mostly things that had to be specified manually each time with cmd parameters. 6523. 2OP: exllama supports loras, so another option is to convert the base model you used for fine-tuning into GPTQ format, and then use it with To be honest, I've not used many GGML models, and I'm not claiming its absolute night and day as a difference (32G vs 128G), but Id say there is a decent noticeable improvement in my estimation. And I tried to find the correct settings but I can't find anywhere where it is explained. Nada. I've been playing around with LLM's all summer but finally have the capabilities of fine tuning one, which I have successfully done (with to convert your model to GGML format, just use the convert. Any # obtain the original LLaMA model weights and place them in . gguf Skip to main content Open menu Open navigation Go to Reddit Home Hey all, I've been working to get data fine tuned with Stanford Alpaca, and finally succeeded this past weekend. bin tokenizer. It is to convert HF models to GGUF. 0. I have a 13700+4090+64gb ram, and ive been getting the 13B 6bit models and my PC can run them. Or check it out in the app stores How to turn arbitrary torch models into gguf/ggml? Question | Help GGUF LLaVA v1. GGML) don't expect a huge speed increase or a big decrease in RAM usage from using GGUF. LLMs quantizations also happen to work well on cpu, when using ggml/gguf model. Q5_K_S. If you can convert a non-llama-3 model, you already Basically I am trying to pass an image to the model and expect it to work. However, the total footprint of this collection is only 6. py models/7B/ # 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. cpp can't load the ggml file (invalid magic characters). Beta: 1 Note: Reddit is dying due to terrible leadership from CEO /u/spez. This confirmed my initial suspicion of gptq being much faster than ggml when loading a 7b model on my 8gb card, but very slow when offloading layers for a 13b gptq model. Right? i'm not sure about this but, I get GPTQ is much better than GGML if the model is completely loaded in the VRAM? or am i wrong? I use 13B models and a 3060 12GB VRam. cpp tree) on pytorch FP32 or FP16 versions of the model, if those are originals Run quantize (from llama. He is a guy who takes the models and makes it into the gguf format. /llama --model weights. Unfortunately I haven't found how to pass an image using LMStudio. /quantize tool. After I finally manage to finetune it Llama spit nothing but the usual nonsense of base model. Hello all, I have been trying to finetune llama2 for quite some time now, I encountered many problems in the process. GGUF, GPTQ, EXL2, AWQ and so on, are different quantization methods. 9 GB, while the most comparable GGML options are Q3_K_L at 17. btw, Also, you first have to convert to gguf format (it was ggml-model-f16. gguf into the original folder for us. md GGUF is a new format introduced by the llama. 0 really well. It took well over an hour to "I was getting 0. cpp on the CPU (Just uses CPU cores and RAM). Once you have the training data the way you want it, the training itself is easy enough -- just point your trainer at the data, specify your hyperparameters, and let it train for hours or days until training loss hits a point of diminishing returns. cp models/7B/ggml-model-q4_0. The GGML_TYPE_Q5_K is a type-1 5-bit quantization, while the GGML_TYPE_Q2_K is a type-1 2-bit quantization. 0 quantised GGML. "GGML" will be part of the model name on huggingface, and it's always a . cpp based version. I'm using llama models for local inference with Langchain , so i get so much hallucinations with GGML models i used both LLM and chat of ( 7B, !3 B) beacuse i have 16GB of RAM. How can I make ggml out of this? /r/StableDiffusion is back open after the protest of Reddit killing open API access, which will bankrupt app developers, hamper moderation, and exclude blind users from the site. That does not work with llama. Thanks a ton! It just loaded, Llama 3 MMLU score vs quantization for GGUF, Running GGML models using Llama. GGUF boasts extensibility and future-proofing through enhanced metadata storage. cpp project, What settings to load falcon-180b-chat. Russian language features a lot of grammar rules influenced by the meaning of the words, which had been a pain ever since I tried making games with TADS 2. run . ) leave IS_PP_SHARED=0 ngl is the number of GPU layers set to 999 I'm rather a LLM model explorer and that's how I came to KoboldCPP. bin --tokenizer tokenizer. It was fun to throw an unhinged character at it--boy, does it nail that persona--but the weirdness spills over into everything and coupled with the tendency for short responses, ultimately undermines the Being able to run GGML/GGUF and GPTQ from the same ui is unbeatable IMO. cpp team on August 21, 2023, replaces the unsupported GGML format. We ll see. /batched-bench ggml-model-f16. . I have a laptop with an Intel UHD Graphics card so as you can imagine, running models the normal way is by no means an option. model # [Optional] for models using BPE tokenizers ls . i was a server-side developer, but am just learning about differences between various LLM file formats. But I think it only supports GGML versions, which use both GPU and CPU, and it makes that a bit slower than the other versions. As I was going to run this on my PC, I am trying to convert the no problem, english is not my native language either and I am happy to have deepl xD okay if i understand you correctly, it's actually about how someone can quantize a model. cpp for the calculations. cpp with "-ngl 40":11 tokens/s That seems low. So Now i'm exploring new models and want to get a good model , should i try GGUF format ?? The smallest one I have is ggml-pythia-70m-deduped-q4_0. Last time I've tried it, using their convert-lora-to-ggml. Q8_0. maybe today or tomorrow. Internet Culture (Viral) Amazing Also, is it possible to be converted to GGML instead of GGUF? It is this model I want in GGML format: Photolens/llama-2-13b-langchain-chat Reply reply More replies. Q6_K. While I generate outputs in less than 1 s with GPTQ, GGUF is awful. My first question is, is there a conversion that can be done There's a new successor format to GGML named GGUF introduced by llama. However, what is the reason I am encounter Sure! For an LLaMA model from Q2 2023 using the ggml algorithm and the v1 name, you can use the following combination: LLaMA-Q2. 89 votes, 29 comments. Q4_K_M. cpp GitHub repo. It also gives you fine control over positional embedding and NTK/alpha for extending the context of models. cpp patch! 🦙 This opens up doors for various models like ggml_new_object: not enough space in the context's memory I can load similarly sized models like venus-120b-v1. js - open-source JS library (with types) for parsing and reading metadata of ggml-based gguf files. i personally use the q2 models first and then q4/q5 and then q8. py script, it did convert the lora into GGML format, but when I tried to run a GGML model with this lora, lamacpp just segfaulted. This worked on my system. 2 GB or Q4_K_S at 18. Like finetuning gguf models (ANY gguf model) and merge is so fucking easy now, but too few people talking about it EDIT: since there seems to be a lot of interest in this (gguf finetuning), i will make a tutorial as soon as possible. Get app Get the Reddit app Log In Log in to Reddit. GGUF. The convert. en has been the winner to keep in mind bigger is NOT better for these necessary 🚀 Exciting News! 🚀 Thrilled to announce the release of LLM. With LLM models, you can engage in role-playing 74 votes, 15 comments. as i try to understand what you're doing and what it enables, a few questions come to mind. First, perplexity isn't the be-all-end-all of assessing a the quality of a model. The lower bit quantization can reduce the file size and memory bandwidth requirements, but also introduce more errors and noise that can affect the accuracy of the model. gguf gpt4-x I have only 6gb vram so I would rather want to use ggml/gguf version like you, but there is no way to do that in a reliable way yet. I think I would prefer to run the 70b model at times even if it's slow, other times when using alltalk_tts a Actually what makes llava efficient is that it doesnt use cross attention like the other models. cpp setup correctly with python. In my experience, all of the work is in cleaning/formatting the training data. Updated Nvidia drivers. cpp or KoboldCPP, and will run on pretty much any hardware - I used to use GGML, not GGUF. After the change to gguf I get 0. /models 65B 30B 13B 7B tokenizer_checklist. cuda. json # install Python dependencies python3 -m pip install -r requirements. I initially played around 7B and lower models as they are easier to load and lesser system requirements, but they are sometimes harder to prompt and more tendency to get side tracked or hallucinate. 1 tokens a second. chk tokenizer. bin, not sure if it would work to just change the file type I am trying to run GGUF models (any model) on my 4060 16GB x i7 with 48GBR RAM and what ever I try in the settings, the whole process is slowed down by "Prompt evaluation" which seems to be running entirely on CPU as slow as 8-10s/it. bin will come before b4_0. It supports the large models but in all my testing small. Expand user menu Open settings but haven't had any problems running quantized gguf models. /quantize models/ggml-model-f16. It tops most of the 13b models in most benchmarks I've seen it in (here's a compilation of llm benchmarks by That reads to me like it is a labeled dataset, similar to what you'd find here on huggingface. You can dig deep into the answers and test results of each question for each quant by clicking the expanders. Today I was trying to generate code via the recent TheBloke's quantized llamacode-13b-5_1/6_0 (both 'instruct' and original versions) in ggml and gguf formats via llama. Log In (fork-of-a-fork), so I tried that and did manage to produce some GGML files. EDIT: Thank you for the responses. Now I wanted to see if it's worth it to switch to EXL2 as my main format, that's why I did this comparison. It does take some time to process existing context, but the time is around 1 to ten seconds. More info: whisper. So far ive ran llama2 13B gptq, codellama 33b gguf, and llama2 70b ggml. You can offload the entire quantized model into vram and, at least for me, I got exllama+ speeds using GGML. q4_0. cpp in new version REQUIRE gguf, so i would assume it is also true llama-ccp-python. 1 quantization version Reply reply The official Python community for Reddit! Stay up to date with the latest news, packages, and meta information relating to the Python programming language. 162K subscribers in the LocalLLaMA community. cpp loader now. The base model I used was llama-2-7b. I had been struggling greatly getting Deepseek coder 33b instruct to work with Oobabooga; like many others, I was getting the "On paper" phi-1. The first argument is the GGUF model path The second is max context (4k for llama2, 32k for mistral, etc. It is a bit confusing since ggml was also a file format that got changed to gguf. I meant that under the GGML name, there were multiple incompatible formats. Q4_0 is, in my opinion, still the best balance of speed and accuracy, but there's a good argument for Q4_K_M as it just barely slows down, and does add a nice chunk of accuracy back. py (from llama. empty_cache() everywhere to prevent memory leaks. For ex, `quantize ggml-model-f16. But in the training and finetuning are both broken in llama. cpp, slide n-gpu-layers to 10 (or higher, mines at 42, thanks to u/ill_initiative_8793 for this advice) and check your They are awfully slow on my rig. 7-2 tokens per second on a 33B q5_K_M model. Q3_K_L. However am I losing performance if I only use GGML? I've been a KoboldCpp user since it came out (switched from ooba because it kept breaking so often), so I've always been a GGML/GGUF user. I think it was Airoboros v1. 39 votes, 28 comments. qood question, I know llama. gguf file in my case, 132 GB), and then use . cpp, like the name implies, only supports ggml models based on Llama, but since this was based on the older GPT-J, we must use Koboldccp because it has broader compatibility. Here's a guide someone posted on reddit for how to do it; it's a lot more involved of a process than just converting an existing model to a gguf, but it's also not super super complicated. EDIT: ok, seems on Windows and Linux ooba install second older version of llama-cpp-python for ggml compatibility. If you had a model set up properly with one of the other formats (e. py, llama. gguf with no issue, but discolm-120b. bin files there with ggml in the name (*ggml*. With KoboldCPP (32 layers offloaded to GPU) I got slightly faster responses than GPTQ AAAAND I can use full 2048 context now that my VRAM is not filled! Today I loaded a newer llama2 gguf on my much newer laptop and it no ggml files were in my install. I mitigate that by using a Q8 GGML/GGUF quant, /r/StableDiffusion is back open after the protest of Reddit killing open API access, which will bankrupt app developers, hamper moderation, and exclude blind users from the site. Or check it out in the app stores TOPICS. But don't expect 70M to be usable lol I am curious if there is a difference in performance for ggml vs gptq on a gpu? Specifically in ooba. For 13B-Q5 model, /r/StableDiffusion is back open after the protest of Reddit killing open API access, which will bankrupt app developers, hamper moderation, and exclude blind users from the site. For most people, q4 is enough. cpp or ggml Githubs Reply reply More replies. 5-2 tokens a second running the 33b models. (Info / ^Contact) GGML version is very fast, I have 3. I did try I settled with 13B models as it gives a good balance of enough memory to handle inference and more consistent and sane responses. Or check it out in the app stores GGML is a format used by llama. py script in the llama. gguf bloomq4km. You can find these in the llama. q8_0. 2! 🎉 . Just tried my first fine tune w/ llama. gguf which 7. bin" for the q3_K_L GGML model. I used quant version in Mythomax 13b but with 22b I tried GGML q8 so the comparison may be unfair but 22b version is more creative and coherent. For some reason on that page it doesn't mention gguf files, only ggml, but it is what I use to run gguf, /r/StableDiffusion is back open after the protest of Reddit killing open API access, which will bankrupt app developers, hamper moderation, I mean GGML to GGUF is still a name change I didn't mean the format change from GGML to GGUF. /models ls . ) with Rust via Burn or mistral. someone with low-ram will probably not be interested in gptq etc, but in ggml. comment sorted by Best Top New Controversial Q&A Add a Comment. q3_K_L. 4. bin which should fit on your vram with fully loading to GPU layers. cpp - oobabooga has support for using that as a backend, Jamba GGUF! upvotes I was getting confused by all the new quantization methods available for llama. 5 support soon 🚀 twitter Yes that's why there is a justification for maintaing two model formats one that is purely optimised for GPU (was GPTQ would be nice to move on) the other is for llama. cpp based GGML or GGUF models, only GPTQ models, hence me asking specifically about the compatibility of this new llama. in LM Studio). Bru I've had an absolute nightmare of a time trying to get Continue to work, followed the instructions to the T, tried it in Windows native and from WSL, tried running the Continue server myself, I just keep getting an issue where the tokenizer encoding cannot be found, was trying to connect Continue to an local LLM using LM Studio (easy way to startup OpenAI compatible What determines the speed of token generation on the GGML & GGUF model? I have 13600K and 64 DDR5. It has an extensible, future-proof format which stores more GGUF: https://github. I've only done limited roleplaying testing with both models (GPTQ versions) so far. Top 1% Rank by size . Quantization. The instruct models seem to always generate a <|eot_id|> but the GGUF uses <|end_of_text|>. This worked, except this script only seems to allow for f32 and f16. i apologize in advance for my ignorance. cpp: . fgnaf hhmwpj bgqph rvlvp wlezkm jjjjrt qhnu fpy rnoeaeb ahc