Exllama hf reddit github. 8K subscribers in the Oobabooga community.
- Exllama hf reddit github 5 times faster than ExllamaV2. Exllama: 9+ t/s, ExllamaV2 1. Deploy them across mobile, desktop, VR/AR, consoles or the Web and connect with people globally. However lora works with transformers but slow af we really need exllama for this. In order to bootstrap the process for this example while still building a useful model, we make use of the StackExchange dataset. dev. I tried various temperatures. - gabyang/textgen-webui I was using git exllama but downgraded to . Subreddit to discuss about Llama, the large language model created by Meta AI. Sign up for GitHub By clicking “Sign up I get ~2-3 tokens/sec with the 6-bit EXL2 model @ 48K of context (same as the 4-bit GPTQ from huggingface). A more memory-efficient rewrite of the HF transformers implementation of Llama for use with quantized weights. (I'm still in GPTQ-land w/ TGWUI & exllama/exllama_hf from about a month or two ago. There is no need to run any of those scripts (start_, update_, or cmd_) as admin/root. log of perplexity) and I can confirm that it works in exllama_hf (as described in this You signed in with another tab or window. 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. 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). Beta Was this translation helpful? NOTE: by default, the service inside the docker container is run by a non-root user. ExLlama gets around the problem by reordering rows at load-time and discarding the group index. Your gist is great, thank-you. Logs 11 votes, 28 comments. It's also happening on/off in the textgen UI again. 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. true. Minor thing, but worth noting. 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. i'm pretty sure thats just a hardcoded message. 2. upvotes /r/StableDiffusion is back open after the protest of Reddit killing open API access, which will ExLLaMA is a loader specifically for the GPTQ format, which operates on GPU. Use Unity to build high-quality 3D and 2D games and experiences. ai), otherwise it always generate the same char as example chats. Here's the deterministic preset I'm using for test: Here's the ExLlama, ExLlama_HF, ExLlamaV2, ExLlamaV2_HF are the more recent loaders for GPTQ models. Ok, maybe it's the fact I'm trying llama 1 30b. Write better code with AI Security Sign up for a free GitHub account to open an issue and contact its maintainers and the community. 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. You may have to reduce max_seq_len if you run out of memory while trying to generate text. ExLlama v1 vs ExLlama v2 GPTQ speed (update) I had originally measured the GPTQ speeds through ExLlama v1 only, but turboderp pointed out that GPTQ is faster on ExLlama v2, so I collected the following additional data for the model Load exllama_hf on webui. Other loaders are for different types of models. See the model card in each repository for details on instruction formats. Try to load a model which can't be used on same GPU, but in more than 1 GPUs. md at master · turboderp/exllama. 25 t/s (ran more than once to make sure it's not a fluke) Ok, maybe it's the max_seq_len or alpha_value, so here's a test with the default llama 1 context of 2k. 2) Create a llama. exlla Classifier-Free Guidance (CFG) has been merged into the transformers library I was having a similar issue before with it taking a lot of RAM, are you using exllama or exllama_hf as the loader? If so, it's not supposed to use over a few gigabytes ever, make sure your Oobabooga installation is updated. Am also using torch 2. Enterprise-grade security features because there's very little of the original HF structure left. 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. Official subreddit for oobabooga/text-generation-webui, a Gradio web UI for Large Language Models. cpp, ExLlama, ExLlamaV2, AutoGPTQ, GPTQ-for-LLaMa, CTransformers Dropdown menu for quickly switching between different models Then I tried to load TheBloke_guanaco-13B-GPTQ and unfortunately got CUDA out of memory. Evaluation setup. Is there an existing issue for this? I have It seems to work on my setup (also Cuda 12. For me, these were the parameters that worked with 24GB VRAM: This is done with the llamacpp_HF wrapper, which I have finally managed to optimize (spoiler: it was a one line change). 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. Enterprise-grade security features ExLlama nodes for ComfyUI. AI-powered developer platform Available add-ons. run in terminal in your 'text-generation-webui' directory (but don't forget to activate your venv first): My 4090 with WizardCoder-Python-34B-V1. Screenshot. cpp directly, but with the following benefits: More samplers. 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 The main idea is better VRAM management in terms of paging and page reusing (for handling requests with the same prompt prefix in parallel. It does not solve all the issues but I think it go forward because now I have : A more memory-efficient rewrite of the HF transformers implementation of Llama for use with quantized weights. neither does it help to do chat completions. Try to do inference. env file if using docker compose, or the You signed in with another tab or window. It's definitely powerful for a production system (especially those designed to handle many similar Contribute to DylPorter/LLaMA-2 development by creating an account on GitHub. 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). The dataset includes questions and their corresponding answers from the StackExchange platform (including StackOverflow for code and many other topics). Hello, I noticed the quality of the output decreased with exllama2 so I took a look at the logits, it's the same model, same quant, same samplers, same prompt, same seed Maybe it's a bug on ooba's Should work with exllama_hf too. 2-GPTQ when using exllama: when using autoGPTQ by default: and Custom stopping strings in webui is fine: Is there an existing issue for t I'm developing AI assistant for fiction writer. After that, load them using the "ExLlama_HF" loader. 5tk/s forced me to try some Pyg-6B-q4-128g / mayaerie / Shygmalion and band in 6 and 7B quantized when they were realeased founding them quite lame though sometimes they proposed extremely interesting chats (maybe by accident Kobold's exllama = random seizures/outbursts, as mentioned; native exllama samplers = weird repetitiveness (even with sustain == -1), issues parsing special tokens in prompt; ooba's exllama HF adapter = perfect; The forward pass might be perfectly fine after all. yml file) is changed to Saved searches Use saved searches to filter your results more quickly Upvote for exllama. 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. It is also possible to run the 13B model using llama. yml file) is changed to this non-root user in the container entrypoint (entrypoint. sh, or cmd_wsl. Transformers samplers added to exllama on oobabooga text-gen-webui, so all the samplers of GPTQ-for-LLaMA now works in exllama! From a quick glance at the github, tau reperesent the average surprise value (i. I don't know if manually splitting the GPUs is needed. That's all done in webui with its dedicated configs per model now though. Contribute to Zuellni/ComfyUI-ExLlama-Nodes development by creating an account on GitHub. The model card on HF is clear on this: meta-llama/Llama-2-7b: "Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. as in just inputting Hi or Hello Last time I've tried it, using their convert-lora-to-ggml. It's already kind of unwieldy. so after I use sillytavern and it crashes once, it also begins to crash in the webui the same way. cpp by sending part of the layers to the GPU. Both GPTQ and exl2 are GPU only formats meaning inference cannot be split with the CPU and the model must fit entirely in VRAM. It reads HF models but doesn't rely on the framework. with exllama_hf, I don't need that occ order. They're in the test branch for now, since I need to confirm that they don't break anything (on ROCm in 1) Make ExLlama_HF functional for evaluation. Also the memory use isn't good. PyTorch in general seems to be optimized for training and inference on long sequences. More than 100 million people use GitHub to discover, fork, and contribute to over 420 million projects. ) Can you describe how you experience the difference? 3- Open exllama_hf. cpp , koboldcpp , and C Transformers I guess. Was wondering the same, I started with a Pygmalion 6B when it came out, results were nice for the character card I had developed but the max 0. Classifier-Free Guidance is now implemented for ExLlama_HF and llamacpp_HF. But upon sending a message it gets CUDA out of memory again. Saved searches Use saved searches to filter your results more quickly @turboderp so looks like i got it all working. This issue caused some people to opportunistically claim that the webui is You can find them for many (most?) datasets on HF, with a little "auto-converted to Parquet" link in the upper right corner of the dataset viewer. Not this fast, but fast enough that I don't feel like waiting on something. 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. 96 seconds (40. 169K subscribers in the LocalLLaMA community. 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. Skip to content. Output generated in 2. env file if using docker compose, or the A more memory-efficient rewrite of the HF transformers implementation of Llama for use with quantized weights. I am not sure if it's an issue, but I assume that it loads Unity is the ultimate entertainment development platform. 18 and there is no difference. - Pull requests · turboderp/exllama. bat, cmd_macos. 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 178K subscribers in the LocalLLaMA community. 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. And if that's something exllama2 could support or One click notebook. Gathering human feedback is a complex and expensive endeavor. Internet Culture (Viral) Amazing so if you used those on exllama_hf/exllamav2_hf on ooba, these loaders are not LocalAI has recently been updated with an example that integrates a self-hosted version of OpenAI's API with a Copilot alternative called Continue. 8K subscribers in the Oobabooga community. cpp_HF wrapper that is also functional for evaluation. The official and recommended backend server for ExLlamaV2 is TabbyAPI, which provides an OpenAI 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 interpreted the same and more samplers are I've made some changes to the GPTQ kernel to increase precision. 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. comments sorted by Best Top New Controversial Q&A Add a Comment. ExLlama supports 4bpw GPTQ models, exllamav2 adds support for exl2 which can be quantised to fractional bits per weight. Topics Trending Collections There’s an excellent guide on the Exllamav2 GitHub: With the fused attention it is fast like exllama, but without it is slow AF. - Pull requests · turboderp/exllama GitHub community articles Repositories. So I switched the loader to ExLlama_HF and I was able to successfully load the model. And you can't override quantize_config like that to pick a model. Sign in Product Add a description, image, and links to the exllama topic page so that developers can more easily learn about it. 3 interface modes: default (two columns), notebook, and chat; Multiple model backends: transformers, llama. Advanced Security. 👍 2 Panchovix and alkeryn reacted with thumbs up emoji edit: Oh I see, ExLlama and ExLlama_HF behave differently. for models that i can fit into VRAM all the way (33B models with a 3090) i set the layers to 600. I was just scripting mergekit and making a ton of models to test the same hypotheses, this is very helpful —I can run the same tests, cheaper / more easily. ) So I believe the tech could be extended to support any transformer based models and to quantized models without a lot of effort. turboderp's In the Model tab, select "ExLlama_HF" under "Model loader", set max_seq_len to 8192, and set compress_pos_emb to 4. e. Let's try with llama 2 13b. To compare, 6-bit 70B model @ 48K gives me about 9-10 tokens/sec on two of those GPUs. But in those cases where it decodes to the second version, the model treats the same three tokens differently for some reason. Ran a long prompt first with ExLlama then reloaded the model with ExLlama_HF and then ran same prompt again:. turboderp/exllama#118 This hasn't been vetted/merged yet but in practice, it seems to unlock the context of un-finetuned models based on the scaling alpha value and does it with minimal perplexity loss. You switched accounts on another tab or window. 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. They are equivalent to llama. If you pair this with the latest WizardCoder models, which have a fairly better performance than the standard Salesforce Codegen2 and Codegen2. 0-GPTQ + ExLlama HF backend is capable of producing text faster then I can read. 87 tokens/s, 121 tokens, context 4371, seed 344184350) 2023-06-29 13:34:42 INFO:Loading noushermes-13b-8k-gptq 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. . As for the performance, it seems to be about the same, maybe a bit slower than the Cuda branch of GPTQ, though this is mainly because I'm heavily single-core CPU bound + as you said, probably don't benefit much from improvements aimed at newer GPU architectures either. For A Gradio web UI for Large Language Models. bat. GitHub community articles Repositories. - turboderp/exllama If I built out ExLlama every time someone had an interesting idea on reddit it'd be an unmaintainable behemoth by now. 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. That said, I couldn't manage to configure this with LocalAI yet, only tested this with the text-generation-webui. You signed out in another tab or window. I'm new to exllama, are there any tutorials on how to use this? I'm trying this with the llama-2 70b model. Load a model shared between 2 GPUs. py and change the 21th line from : from model import ExLlama, ExLlamaCache, ExLlamaConfig to : from exllama. Iory1998 • Additional comment actions As for the "usual" Python/HF setup, ExLlama is kind of an attempt to get away from Hugging Face. 7 tokens/s after a few times regenerating. Contribute to H-2-M/exllamav2-mixtral-webui development by creating an account on GitHub. cpp or koboldcpp. I would dare to say, is one of the biggest jumps on the LLM scene recently. Sign in Product GitHub Copilot. However I was able to load it in ExLlama HF, and it runs seamlessly. A post about exllama_hf would be interesting. On a 70b parameter model with ~1024 max_sequence_length, repeated generation starts at ~1 tokens/s, and then will go up to 7. - exllama/example_basic. View community ranking In the Top 10% of largest communities on Reddit. NOTE: by default, the service inside the docker container is run by a non-root user. Not an issue but seeing that exl2 2 bit quants of a 70b model can fit in a single 24gb GPU. Hence, the ownership of bind-mounted directories (/data/model and /data/exllama_sessions in the default docker-compose. All single batch in exllama_hf streaming mode (kind of a worst case). Only EXL2, 4-bit GPTQ, and unquantized HF models are supported. sh). - Releases · turboderp/exllama 8000 ctx vs 2000 ctx is a way higher jump vs exllama_hf/exllama. Each of these took more hours to get working than I am willing to admit, but lo and behold, it worked. 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. Then, select the llama-13b-4bit-128g model in the "Model" dropdown to load it. Get the Reddit app Scan this QR code to download the app now. 2 Ok. 5, you have a pretty solid alternative to GitHub Copilot that runs View community ranking In the Top 5% of largest communities on Reddit. 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 ExLlamaV2 is an inference library for running local LLMs on modern consumer GPUs. model import ExLlama, ExLlamaCache, ExLlamaConfig. I'm having a similar experience on an RTX-3090 on Windows 11 / WSL. 122 votes, 79 comments. 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. I'm wondering if it's possible to run a quantized version of mixtral 7b*8 on a single 24gb GPU. github. 1) Make ExLlama_HF functional for evaluation. I have to think there's something else that's different about the Describe the bug I couldn't load it for my AMD 5800X3D + TUF4090 with default setting (Transformer or GPTQ). Chatting on the Oobabooga UI gives me gibberish but using SillyTavern gives me blank responses and I'm using text completion so I don't think it has anything to do with the API for my case. What I did was start from Larry's code and . Weirdly, inference seems to speed up over time. 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. Here's the wikitext-test split as a Parquet file, for instance. I can't even get 2k context fused and barely touch 3k unfused. The only other difference I'm seeing is that over the API the tokens are different. 1) so hopefully it also solves it for @ilikenwf!. My main branch Llama-2 models don't have act-order enabled (though I may change that in future). py at master · turboderp/exllama. But it was a while ago, probably that has been fixed already. - turboderp/exllama I guess you updated text generation webui repository so requirements changed too (for example, exllama got new changes), try to update them. Supports transformers, GPTQ, AWQ, EXL2, llama. - exllama/doc/TODO. You can find them on Hugging Face. after installing exllama, it still says to install it for me, but it works. Saved searches Use saved searches to filter your results more quickly Describe the bug When running exllama w/ llama-65b, it seems that the no_repeat_ngram_size parameter is ignored when using the API. ExLlama and exllamav2 are inference engines. The first such wrapper was "ExLlama_HF", created by LarryVRH in this PR. 0bpw --local-dir-use-symlinks False --local-dir my_model_dir assuming to get similar behavior but it performs vastly different for me. Load exllama_hf on webui. Curate this topic Add this topic to your 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). The script uses Miniconda to set up a Conda environment in the installer_files folder. Enterprise-grade security In other words should I be able to get the same logits whether I use exllama for inference or another quantisation inference library? Im assuming it is loss-less but just wanted to double check. Navigation Menu Toggle navigation. Describe the bug Literally doesnt work i tried both exllama and exllamav2 with _HF ones as well. Its quite weird - Text completion seems fine, the issue only appears when using chat completion - with new or old settings. sh, cmd_windows. nope, old Exllama still ~2. cpp" loader: GitHub is where people build software. So the CPU bottleneck is removed, and all HF loaders are now faster, including ExLlama_HF and ExLlamav2_HF. To disable this, set RUN_UID=0 in the . Topics Trending Collections Enterprise Enterprise platform. Layer upcycling is among the most promising means of increasing the capability water-line for the ‘GPU-poor’. I believe they are specifically GPU based only. Each of these took more hours to get working than I am willing to So now, will this work with a llama cpp? 6. I dumped out what is sent to the function (kwargs). cpp (GGUF), Llama models. 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. 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'm aware that there are GGML versions of those models, but the inference speed is painfully slow compared to I've found exllama and exllama_hf had more difference other than speed. 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. Or check it out in the app stores TOPICS. There's a lot of debate about using GGML, or GPTQ, AWQ, EXL2 etc performance etc. To sum up: The HF tokenizer encodes the sequence Hello, to [1, 15043, 29892], which then decodes to either <s>Hello, or <s> Hello,, apparently at random. Reload to refresh your session. ExLlama relies on controlling the datatype and stride of the hidden state throughout the That's very strange. It is now about as fast as using llama. Has anyone else run into this, or am I doing something wrong? Is Describe the bug using model: TheBloke/airoboros-65B-gpt4-1. to be clear, all i needed to do to install was git clone exllama into repositories and restart the app. wevmx akkmdo pkpx pod eboyzri qai ayyxn bxp nomfmv fvqgj