Exllama hf reddit github He kinda got a point though I guess, Add Support for Static NTK RoPE scaling for exllama/exllama_hf #2955. A community to share tips, resources and articles pertaining to AI 1. cpp directly, but with the following benefits: More samplers. Reload to refresh your session. A fast inference library for running LLMs locally on modern consumer-class GPUs - exllamav2/ at master · turboderp/exllamav2 3 interface modes: default (two columns), notebook, and chat; Multiple model backends: transformers, llama. cpp and ExLlama are transformers models, and then evaluate their perplexities. For those interested, there are two runpods templates ready to roll - one for HF models and one for GPTQ. As far as I know, this will not support exllama, exllama_HF, or the new superHOT 8k models, until Occ4m adds support to this fork. The dataset includes questions and their corresponding answers from the StackExchange platform (including StackOverflow for code and many other topics). This can be done either by editing /workspace/run-text-generation-webui. 0-GPTQ + ExLlama HF backend is capable of producing text faster then I can read. I have it (33b) running pretty well, gptq in oobabooga, rtx 3090 ti, 64GB of RAM, exllama v2 hf loader, standard alpaca template without modified system prompt. model import ExLlama, ExLlamaCache, ExLlamaConfig. That said, I couldn't manage to configure this with LocalAI yet, only tested this with the text-generation-webui. Tested both with my usual setup (koboldcpp, SillyTavern, and simple-proxy-for-tavern - I've posted more details about it in this post over Edit: I posted this on the HF discussion there, but I thought of a simple test to check if the model is aware of some tokens that the model code would never tokenize right now with its HF code. Eval mmlu result against various infer methods (HF_Causal, VLLM, AutoGPTQ, AutoGPTQ-exllama) Discussion I modified declare-lab's instruct-eval scripts, add support to VLLM, AutoGPTQ (and new autoGPTQ support exllama now), and test the mmlu result. Unity is the ultimate entertainment development platform. sh, cmd_windows. so after I use sillytavern and it crashes once, it also begins to crash in the webui the same way. reply a_beautiful_rhind • I need to test and see where prompt caching does work. Speculative decoding doesn't affect the quality of the output. With Exllama V2 it might be fast enough for me. My goal was to find out which format and quant to focus on. Get all the model loaded in GPU 0; For the second issue: Apply the PR Fix Multi-GPU not working on exllama_hf #2803 to fix loading in just 1 GPU. " Describe the bug When running exllama w/ llama-65b, it seems that the no_repeat_ngram_size parameter is ignored when using the API. Additional notes: num_beams > 1 works with --loader exllama; num_beams > 1 breaks with --loader exllama_hf; no_repeat_ngram_size works with --loader exllama_hf AutoGPTQ and Exllama support it. that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Am also using torch 2. yml file) is changed to Another thing to look into with this is cloudflared and the Argo tunnels they have. Deploy them across mobile, desktop, VR/AR, consoles or the Web and connect with people globally. The 32 refers to my A6000 (the first GPU ID set in the environment variable CUDA_VISIBLE_DEVICES), so I don't pre-load it to its max 48GB. Thank you for the suggestions implemented it and installed the drivers, now I am getting these set of errors Traceback (most recent call last): On a git console, or powershell (or bash on linux) on the textgen folder, git fetch origin pull/2955/head:ntkropepr git checkout ntkropepr Then, if ooba merges it, or you want to revert, you just can do: git checkout main There's a lot of debate about using GGML, or GPTQ, AWQ, EXL2 etc performance etc. 8000 ctx vs 2000 ctx is a way higher jump vs exllama_hf/exllama. com/oobabooga/text-generation-webui/pull/4814. Weirdly, inference seems to speed up over time. 7 tokens/s after a few times regenerating. Contribute to pabl-o-ce/hf-exllama development by creating an account on GitHub. It is now about as fast as using llama. They are equivalent to llama. It seems to me like for exllama it reprocesses most of the 3 interface modes: default (two columns), notebook, and chat; Multiple model backends: transformers, llama. Enjoy! Reply reply a_beautiful_rhind • This is done with the llamacpp_HF wrapper, which I have finally managed to optimize (spoiler: it was a one line change). ) So I believe the tech could be extended to support any transformer based models and to quantized models without a lot of effort. It's being treated as a regular 4k context model and doesn't seem to A more memory-efficient rewrite of the HF transformers implementation of Llama for use with quantized weights. Python itself becomes a real issue when the kernel launches don't queue up because they execute much faster than the Python interpreter can keep up. Or check it out in the app stores While it OOMs with regular ExLlama, I can load it with ExLlama_HF but it still OOMs upon inference. As openai API gets pretty expensive with all the inference tricks needed, I'm looking for a good local alternative for most of inference, saving gpt4 just for polishing final results. It is also possible to run the 13B model using llama. It's also happening on/off in the textgen UI again. Note the comments about making sure you're doing an apples-to-apples comparison by ensuring that the GPTQ and EXL2 model are converted from the same source model and calibrated with the same dataset. IMHO going the GGML / llama-hf loader seems to currently be the better option for P40 users, as perf and VRAM usage seems better compared to AUTOGPTQ. You switched accounts on another tab or window. 2. Minor thing, but worth noting. AutoGPTQ and GPTQ-for-LLaMA don't have this optimization (yet) so you end up paying a big performance penalty when using both act-order and group size. The major issue is that the built in API and WEBUI all pass truncation_length as a parameter, so it's not an issue for them, but the openai API doesn't have such a thing so we need to rely on the server updating the shared. I've been meaning to write more documentation and maybe even a tutorial, but in the meantime there are those examples, the project itself, and a lot of other projects using it. cpp but rather the llama-cpp-python wrapper. py and change the 21th line from : from model import ExLlama, ExLlamaCache, ExLlamaConfig to : from exllama. cpp (through llama-cpp-python), ExLlama, ExLlamaV2, AutoGPTQ, AutoAWQ, GPTQ-for-LLaMa, CTransformers, QuIP# Dropdown menu for quickly switching between different models long_seq for llama. You may want to look into this (using text-generation-webui, you can load it with `--loader exllama`). @turboderp so looks like i got it all working. But still waiting on confirmation on that HuggingFace space with ExllamaV2. Necessary to use CFG with that loader, but not necessary for CFG with base ExLlama. It's obviously a work in progress but it's a fantastic project and wicked fast 👍 Because the user-oriented side is straight python is much easier to script and you can just read the code to understand what's going on. Then, select the llama-13b-4bit-128g model in the "Model" dropdown to load it. Has anyone else run into this, or am I doing something wrong? Is Looking at the author, "wbrown", his interests are listed as "Large transformer models, finetuning, and open access" on HF and his Github contains some forks of NovelAI projects; looks like an interesting guy! That was very helpful, thank you. It's already kind of unwieldy. Just a heads up though, the GPTQ models support is exclusive to models built with the latest gptq-for-llama. Literally the first generation and the model already misgendered my character twice and there was some weirdness going on with coherency(i don't know how to best explain it but i've seen some text that contextually makes sense, but it kinda feels off like in an "unhuman" way. 18 and there is no difference. Get the Reddit app Scan this QR code to download the app now. In the Model tab, select "ExLlama_HF" under "Model loader", set max_seq_len to 8192, and set compress_pos_emb to 4. But it was a while ago, probably that has been fixed already. So I took the best 70B according to my previous tests, and re-tested that again with various formats and quants. py --model TheBloke_llava-v1. HuggingFace space with ExllamaV2. Or check it out in the app stores so if you used those on exllama_hf/exllamav2_hf on ooba, these loaders are not needed anymore. I cannot load GPTQ model with exllama/exllama-hf. exllama is a very perfomant backend and I'm curious what the author has cooked up here. cpp, though I think the koboldcpp fork still supports it. Personally, Ive had much better performance with GPTQ (4Bit and group size of 32G gives massively better quality of result than the 128G models). I saw this post: #2912 But I'm a newbie, and I have no idea what half 2 is or where to go to disable it. Both GPTQ and exl2 are GPU only formats meaning inference cannot be split with the CPU and the model must fit entirely in VRAM. I'm developing AI assistant for fiction writer. 0-GPTQ and it was surprisingly good, running great on my 4090 with ~20GBs of VRAM using ExLlama_HF in oobabooga. afaik exllama / exllama_hf uses a much more vram-efficient algorithm. While I admire the exllama's project and would never dream to compare these results to what you can achieve with exllama + GPU, it should be noted that the low speeds in oubabooga webui were not due to llama. 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 A Gradio web UI for Large Language Models. Reply reply More replies More replies More replies More replies. ExLlama_HF uses the logits from ExLlama but replaces ExLlama's sampler with the same HF pipeline used by other implementations, so that sampling parameters are I've tried the GGML Q6 version fully offloaded to my GPUs, but it's nearly half the speed of 4-bit 32 groupsize actorder model with exllama_hf so I deleted it. They're in the test branch for now, since I need to confirm that they don't break anything (on ROCm in The idea is to trick the transformers library into thinking that llama. Classifier-Free Guidance is now implemented for ExLlama_HF and llamacpp_HF. https://github. cpp (as u/reallmconnoisseur points out). Can someone explain the difference in loaders, this is what I am thinking / found. py which builds a CUDA11. ExLlama, ExLlama_HF, ExLlamaV2, ExLlamaV2_HF are the more recent loaders for GPTQ models. The script uses Miniconda to set up a Conda environment in the installer_files folder. Please keep posted images SFW. Should work with exllama_hf too. It's definitely powerful for a production system (especially those designed to handle many similar This is done by creating a wrapper for the model. The work done by all involved is just incredible, hats off to the Ooba, Llama and Exllama coders. PyTorch in general seems to be optimized for training and inference on long sequences. See the model card in each repository for details on instruction formats. cpp" loader: LocalAI has recently been updated with an example that integrates a self-hosted version of OpenAI's API with a Copilot alternative called Continue. As long as it's relevant you can post or ask whatever you like. true. I've been getting gibberish responses with exllama 2_hf. - gabyang/textgen-webui There's so many interesting quants right now my quant request issues are overflowing 😔 planning on EXL2 evaluations for this weekend. Are we expecting to further train these models for A more memory-efficient rewrite of the HF transformers implementation of Llama for use with quantized weights. If you want to use ExLlama permanently, for all models, you can add the --loader exllama parameter to text-generation-webui. ExLlama and exllamav2 are inference engines. a reddit user suggested the same thing. For that, download the q4_K_M file manually (it's a single file), put it into text-generation-webui/models, and load it with the "llama. 2023-07-08 18:42:16 INFO:Loading TheBloke_Wizard-Vicuna-30B-Uncensored-GPTQ 2023-07-08 18:42:16 WARNING:Exllama module failed to load. Using AutoGPTQ: supports more models standardized (no need to guess any parameter) is a proper Python library no wheels are presently available so it requires manual compilation supports loading both triton and cuda models Using GPTQ-for-LLaMa directly: faster CPU offloading Saved searches Use saved searches to filter your results more quickly Good day local friends I've come to a problem with using the OrcaMaid-13b-v2-FIX-32k model using the 5 bpw exl2 LoneStriker version. Not this fast, but fast enough that I don't feel like waiting on something. Merged github-actions bot added the stale label Aug 2, 2023. I tried various temperatures. sh). cpp by sending part of the layers to the GPU. ExLlamaV2 is an inference library for running local LLMs on modern consumer GPUs. 38 votes, 19 comments. This seems super weird, I'm not sure what he's trying to do just comparing perplexity and not accounting for file size, performance, etc. Unless there is a diff 3 interface modes: default (two columns), notebook, and chat; Multiple model backends: Transformers, llama. On a 70b parameter model with ~1024 max_sequence_length, repeated generation starts at ~1 tokens/s, and then will go up to 7. Try to load a model which can't be used on same GPU, but in more than 1 GPUs. If you intend to perform inference only on CPU, your options would be limited to a few libraries that support the ggml format, such as llama. exllama @ 3b013cd you can view the complete list in the git submodule file. I think the default value may be set a bit high, but I'd As for the "usual" Python/HF setup, ExLlama is kind of an attempt to get away from Hugging Face. If you pair this with the latest WizardCoder models, which have a fairly better performance than the standard Salesforce Codegen2 and Codegen2. A fast inference library for running LLMs locally on modern consumer-class GPUs - Releases · turboderp/exllamav2 You signed in with another tab or window. All models using Exllama HF and Mirostat preset, 5-10 trials for each model, chosen based on subjective judgement, focusing on length and details. bat, cmd_macos. - Releases · turboderp/exllama The main idea is better VRAM management in terms of paging and page reusing (for handling requests with the same prompt prefix in parallel. Other loaders are for different types of models. settings['truncation_length'] value, which only happens when the model settings are loaded. AutoGPTQ can load the NOTE: by default, the service inside the docker container is run by a non-root user. . /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. It should work now. The jump in clarity from 13B models is immediately noticeable. For instance, if I'd be very curious about the tokens/sec you're getting with exllama or exllama_hf loaders for typical Q/A (small) and long-form chat (large) contexts (say, 200-300 tokens and 1800-2000 A standalone Python/C++/CUDA implementation of Llama for use with 4-bit GPTQ weights, des Disclaimer: The project is coming along, but it's still a work in progress! The official and recommended backend server for ExLlamaV2 is TabbyAPI, which provides an OpenAI-compatible API for local or remote inference, with extended features like ExLlama uses way less memory and is much faster than AutoGPTQ or GPTQ-for-Llama, running on a 3090 at least. Ok. 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. Might be used for RLHF reward model training While attempting to work with exllama_hf, I discovered that passing num_beams > 1 during inference when using exllama_hf results in an exception (see below). Anything software QA -related; tools, processes, questions etc. I don't think it's implemented in the exllama repository itself. Screenshot. 4? No idea otherwise. Inferencing will slow on any system when there is more context to process. Update your Oobabooga and load your 13B model with ExLlama_HF. I'm aware that there are GGML versions of those models, but the inference speed is painfully slow compared to To be clear, GPTQ models work fine on P40s with the AutoGPTQ loader. Even after the arena that ooba did, the most used settings are already being used on exllama itself (top p, top k, typical and rep penalty). But upon sending a message it gets CUDA out of memory again. I need to know what to do step by step. Please share your tips, tricks, and workflows for using this software to create your AI art. You signed out in another tab or window. sh, or cmd_wsl. 3B, 7B, and 13B models have been unthoroughly tested, but going by early results, each step up in parameter size is notably more resistant to quantization loss than the last, and 3-bit 13B already looks like it could be a The u/oobabooga4 community on Reddit. I dumped out what is sent to the function (kwargs). Here are his words: "I'm working on some benchmarks at the moment, but they're A Gradio web UI for Large Language Models. bat. Feature request An integration of exllama in Langchain to be able to use 4-bit GPTQ weights, designed to be fast and memory-efficient on modern GPUs. You may have to reduce max_seq_len if you There was a time when GPTQ splitting and ExLlama splitting used different command args in oobabooga, so you might have been using the GPTQ split arg in your bat which didnt split the model for the exllama loader. I am also thinking it may have to do with the cuda install, specifically whether the install of ooba recognized the GPUs correctly and installed the right Python packages/libraries. Really, I just wanted to get something up to share with people on Reddit who are even newer than I am, so they can just get something working quickly. It works in the other text-generation-webui modes, and I can confirm that it works in exllama_hf (as described in this post). More info: A post about exllama_hf would be interesting. Supports transformers, GPTQ, AWQ, EXL2, llama. Motivation The benchmarks on the official repo speak for themselves: https://github. (ExLlama also has its own exl2 format. Contribute to Yiyi-philosophy/hf_llama development by creating an account on GitHub. com (C: \U sers \A rmaguedin \D ocuments \d ev \p ython \t ext-generation-webui \i nstaller_files \e nv) C: \U sers \A rmaguedin \D ocuments \d ev \p ython \t ext-generation-webui > python server. 3 interface modes: default (two columns), notebook, and chat; Multiple model backends: transformers, llama. Does anyone know how to get it to work with Tavern or Kobold or I've made some changes to the GPTQ kernel to increase precision. cpp_HF wrapper that is Contribute to DylPorter/LLaMA-2 development by creating an account on GitHub. 5-13B-GPTQ_gptq-4bit-32g-actorder_True --multimodal-pipeline llava-v1. Curate this topic Add this topic to your repo To associate your NOTE: by default, the service inside the docker container is run by a non-root user. 2) Create a llama. 1. exlla Not sure what dynamic batching and paged attention cover exactly, it sounds like there's some overlap there. I'm waiting for it to be working on exllama_hf, don't want to lose the juicy samplers (especially mirostat) Ah thank you, I followed the linked github page but wasn't able to find what it was from the pr alone /r/StableDiffusion is back open The script uses Miniconda to set up a Conda environment in the installer_files folder. Sign up for a free GitHub account to open an issue and contact its maintainers and the community. 5, you have a pretty solid alternative to GitHub Copilot that runs turboderp/exllama#118 This hasn't been vetted/merged yet but in practice, turboderp's really actively venting and sus about it on reddit rn. 3- Open exllama_hf. 0-GPTQ with text-generation-webui Loading the Load exllama_hf on webui. I recently switched from exllama to exllama_hf because there's a bug that prevents the stopping_strings param from working via the API, and there's a branch on text-generation-webui that supports stopping_strings if you use exllama. com) (edit: LM Studio doesn't have a character card system. There was a comment in the exllama pull request which went into detail, but essentially from what I understood, the top memory gains were made by not fragmenting memory by not dynamically growing the memory (it allocates max sizes at start). Oobabooga in chat mode, with the following character context. This repository contains only some of the models required for personal research, so please refer to other repositories for detailed Questions and Answers collected from Reddit, including score. just curious, is there a secret to the mixtral instruct clip you posted on X? i copied the code you had for generating and downloaded turboderp/Mixtral-8x7B-exl2 --revision 3. The issue is that you can only start generating the token at position n when you've already decided on the token at position n-1. I would dare to say, is one of the biggest jumps on the LLM scene recently. Or check it out in the app stores TOPICS Check the TGI version and make sure it’s using the exllama kernels introduced in v0. Created an issue on github, closed by them without resolution though I'm not the only one hitting the problem, so I switched to exllama and satisfied. Also be careful about drawing conclusions from one model size. env file if using docker compose, or the Also, this is not the most up to date way to run models. To disable this, set RUN_UID=0 in the . Check out the runpod My 4090 with WizardCoder-Python-34B-V1. cpp, ExLlama, ExLlamaV2, AutoGPTQ, GPTQ-for-LLaMa, CTransformers Dropdown menu for quickly switching between different models You signed in with another tab or window. More than 100 million people use GitHub to discover, fork, and contribute to over 420 million projects. A Gradio web UI for Large Language Models. Hence, the ownership of bind-mounted directories (/data/model and /data/exllama_sessions in the default docker-compose. Welcome to the unofficial ComfyUI subreddit. py is also provided, but AutoGPTQ and CTranslate2 are not compatible. - exllama/example_basic. A more memory-efficient rewrite of the HF transformers implementation of Llama for use with quantized weights. Spam is forbidden. Note: I did not use the HF loader. The first such wrapper was "ExLlama_HF", created by LarryVRH in this PR. Ooba itself has this note: "ExLlama_HF is recommended over AutoGPTQ for models derived from LLaMA. It seems like it's mostly between 4-bit-ish quantizations but it doesn't actually say that. It's the Exllama loaders that run poorly on P40s. It does not solve all the issues but I think it go forward because now I have : Get the Reddit app Scan this QR code to download the app now. Previously, HF loaders used to decode the entire output sequence during streaming for each generated token. The only other difference I'm seeing is that over the API the tokens are different. You can find them on Hugging Face. It works thanks to you and him. So I switched the loader to ExLlama_HF and I was able to successfully load the model. Which is a shame because you could almost double the speed if you could do two token positions in one go, or triple it if you could do three, The subreddit for all things related to Modded Minecraft for Minecraft Java Edition --- This subreddit was originally created for discussion around the FTB launcher and its modpacks but has since grown to encompass all aspects of modding the Java edition of Minecraft. Tried Pyg 13B 2(q5KM running via koboldccp and using recommended settings as found on pyg's website). ExLlama2_HF is pretty excellent with using as little memory as possible (I think over AutoGPTQ youre getting around a 5-15% lowered memory use on VRAM). Releases · LostRuins/koboldcpp (github. the GitHub page has instructions for compiling on OSX and Linux. Using about 11GB VRAM. Note that this is chat mode, not instruct mode, even though it might look like an instruct template. Saved searches Use saved searches to filter your results more quickly Gathering human feedback is a complex and expensive endeavor. dev. GGML is no longer supported by llama. Sign up for GitHub Thanks a lot for such a complete answer! I'm glad my guide was helpful to you! I need to post an updated version soon because I'm using some different tools and techniques these days, but the idea remains the same. For exllama/exllama_HF, you have to set embedding compression to 8 and max context to 16384. cpp or koboldcpp. with exllama_hf, I don't need that occ order. LLaMA 2 13b chat fp16 Install Instructions. 1) Make ExLlama_HF functional for evaluation. I've found exllama and exllama_hf had more difference other than speed. I don't know if manually splitting the GPUs is needed. Then, if q and HuggingFace space with ExllamaV2. Note, this is also called a move, in git branches are an illusion, you are just moving where points to. This is done by creating a wrapper 该数据集包含Reddit论坛的帖子标题、正文和所属子论坛信息，数据类型均为字符串。数据集包含一个训练分割，共有127,445,911个样本，大小为93,764,255,230字节。下载大 Get the Reddit app Scan this QR code to download the app now. ExLlama gets around the problem by reordering rows at load-time and discarding the group index. (Testing UPDATE) I tested the 4 and 6bit quantized versions with making the game snake, the 6bit surprisingly did so in one go, and the 4bit did not; both were provided the same prompt and had the exact same setup with deterministic parameters (as deterministic as exllama can get to my understanding) My main branch Llama-2 models don't have act-order enabled (though I may change that in future). github. Exllama easily enables 33B GPTQ models to load and inference on 24GB GPUs now. Use Unity to build high-quality 3D and 2D games and experiences. Load exllama_hf on webui. 5-13b bin C: \U sers \A rmaguedin \D ocuments \d ev \p ython \t I posted my latest LLM Comparison/Test just yesterday, but here's another (shorter) comparison/benchmark I did while working on that - testing different formats and quantization levels. The official and recommended backend server for ExLlamaV2 is TabbyAPI, which provides an OpenAI-compatible API for local or remote inference, with extended features like HF model downloading, embedding model support If I built out ExLlama every time someone had an interesting idea on reddit it'd be an unmaintainable behemoth by now. What I did was start from Larry's code and . Posted by u/gijs4g - No votes and no comments The recommended modal wrapper is interview_modal_cuda11. Load a model shared between 2 GPUs. Skip to content. env file if using docker compose, or the Upvote for exllama. cpp (GGUF), Llama models. I am curious to hear some concrete numbers on how VRAM scales HuggingFace space with ExllamaV2. Unfortunately the nature of Modal does not allow command-line selection of eitehr LLM model or runtime engine. I've test on chronos-hermes-13B-GPTQ (64g_act), with exllama, I need put a ooc order to make it generate a new random char in a complex card (mongirl from chub. Here's the deterministic preset I'm using for test: Here's the I've just finished a thorough evaluation (multiple hour-long chats with 274 messages total over both TheBloke/Nous-Hermes-Llama2-GGML (q5_K_M) and TheBloke/Redmond-Puffin-13B-GGML (q5_K_M)) so I'd like to give my feedback. It doesn't appear as if you can stretch context in AutoGPTQ like you can in Exllama. neither does it help to do chat completions. That is what I was thinking that the tokens/s should be. I believe they are specifically GPU based only. I also have the same ''''''''' with awq version. GitHub ExLlama is still roughly shaped like the HF LlamaModel, and while a bunch of operations do get combined like this, there's still quite a bit of Python code that has to run over the forward pass. An interview_modal_cuda12. High context is achievable with GGML models + llama_HF loader Describe the bug Hello, I think the fixed seed isn't really stable, when I regenerate with exactly the same settings, it can happen I get differents outputs, which is weird. - dan7geo/LLMs-gradio Only EXL2, 4-bit GPTQ, and unquantized HF models are supported. A community to share tips, resources and articles pertaining to AI To partially answer my own question, the modified GPTQ that turboderp's working on for ExLlama v2 is looking really promising even down to 3 bits. The prompt configuration in the right Here’s the docs for git-branch there’s a flag you can pass to it to rename a branch. 3K subscribers in the AITechTips community. 0bpw --local-dir-use-symlinks False --local-dir my_model_dir assuming to get similar behavior but it performs vastly different for me. - turboderp/exllama I tried to install mlc and couldn't due to some wheel and package issues. For Llama-13B that means you'll need about Hello guys These days I am playing around MetaIX/OpenAssistant-Llama-30b-4bit & TheBloke/wizardLM-13B-1. When loading the model, the YaRN part doesn't seem to work at all. It's still the full-sized model that chooses tokens. (Directly on exllama, this is -d 'model_path' -l 16384 -cpe 8) (On ooba, you can set them on the UI) I really don't suggest GPTQ for llama for this, mostly because higher VRAM usage, and with group size + act order at the same time, it will kill the Describe the bug I can run 20B and 30B GPTQ model with ExLlama_HF alpha_value = 1 compress_pos_emb = 1 max_seq_len = 4096 20B Vram 4,4,8,8 result 9-14 token per sec 30B Vram 2,2,8,8 result 4-6 toke Skip to content. There is no need to run any of those scripts (start_, update_, or cmd_) as admin/root. Last time I've tried it, using their convert-lora-to-ggml. I would refer to the github issue where I've addressed this. sh, or by passing the UI_ARGS environment variable via Template Then I tried to load TheBloke_guanaco-13B-GPTQ and unfortunately got CUDA out of memory. After you’ve done that, you will need to update your local refs The FP16 weights on HF format had to be re-done with newest transformers, so that's why transformers version on the title. comments I'm having a similar experience on an RTX-3090 on Windows 11 / WSL. 3 interface modes: default (two columns), notebook, and chat; Multiple model backends: Transformers, llama. Reddit gives you the best of the internet in one place. cpp (through llama-cpp-python), ExLlama, ExLlamaV2, AutoGPTQ, AutoAWQ, GPTQ-for-LLaMa, CTransformers, QuIP# Dropdown menu for quickly switching between different models The script uses Miniconda to set up a Conda environment in the installer_files folder. Or check it out in the app stores There’s an excellent guide on the Exllamav2 GitHub: (I'm still in GPTQ-land w/ TGWUI & exllama/exllama_hf from about a month or two ago. It reads HF models but doesn't rely on the framework. Please help. - kgpgit/text-generation-webui-chatgpt View community ranking In the Top 10% of largest communities on Reddit. 9. You probably mean 12GB and 13B models. That's all done in webui with its ExLLaMA is a loader specifically for the GPTQ format, which operates on GPU. The speeds have increased significantly compared to only CPU usage. 8 based container with all the above dependencies working. That said, I am not knowledgeable in the differences, so you'd be better served asking in a discord like Ooba's for a proper answer. Answer seems to be that it is indeed aware, which suggests the HF model is buggy with its tokenization. But anyway, if you just run an LLM naively with batched inference, to support up to 50 concurrent users you need a batch size of 50. 👍 2 Panchovix and alkeryn reacted with thumbs up emoji GitHub PR regarding exllama integration with oobabooga here, where some issues were discussed IIRC: /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. cpp (through llama-cpp-python), ExLlama, ExLlamaV2, AutoGPTQ, GPTQ-for-LLaMa, CTransformers, AutoAWQ Dropdown Yesterday I've tried the TheBloke_WizardCoder-Python-34B-V1. image, and links to the exllama topic page so that developers can more easily learn about it. cpp , koboldcpp , and C Transformers I guess. ) After that, load them using the "ExLlama_HF" loader. ai), otherwise it always generate the same char as example chats. I've tested on 2x24GB VRAM GPUs, and it works! For now: GPTQ for LLaMA works. ) GGML/GGUF stems from Georgi Gerganov's work on llama. GPTQ-for-LLaMa GitHub is where people build software. And you can't override quantize_config like that to pick a model. Try to do inference. If you had successfully loaded the model it would have produced gibberish because turning on desc_act on a model that was made without it will give garbled output. cpp, ExLlama, ExLlamaV2, AutoGPTQ, GPTQ-for-LLaMa Turboderp, developer of Exllama V2 has made a breakthrough: A 4 bit KV Cache that seemingly performs on par with FP16. If you also want to support up to 2048 tokens of context, the cache has to be 2048 positions wide. You can setup the tunnel to point at your server without having to forward a port on your router, which can help if you've got an ISP that doesn't allow port forwarding or server hosting (some of them are doing this lately with CGNAT where you don't even have a public ip anymore). NOTE: by default, the service inside the docker container is run by a non-root user. In order to bootstrap the process for this example while still building a useful model, we make use of the StackExchange dataset. GitHub Gist: instantly share code, notes, and snippets. Saved searches Use saved searches to filter your results more quickly Pretty sure every python project I've git cloned, installed, and eventually gotten working had something either missing from the requirements or broken in it. There is no need to run any of those scripts (start_, update_wizard_, or cmd_) as admin/root. exllama @ 3b013cd. I was using git exllama but downgraded to . Logs ExLlama_HF: Create an additional cache for CFG negative prompts. If you ever need to install something manually in the installer_files environment, you can launch an interactive shell using the cmd script: cmd_linux. It also doesn't 100% generate from the UI either. yml file) is changed to this non-root user in the container entrypoint (entrypoint. ExLlama supports 4bpw GPTQ models, exllamav2 adds support for exl2 which can be quantised to fractional bits per weight. py at master · turboderp/exllama To optionally save ExLlama as the loader for this model, click Save Settings. wvepsou bvd usxrl cnwfqv tcrmyu lbizeo twvfrbbb ffhra gbikzzq cbg