Exllama slow 👍 1 vuiseng9 reacted with thumbs up emoji All reactions Sep 27, 2023 · The T4 is quite slow. Qlora did this too when it came out, but HF picked it up and now it’s kinda eclipsed GPTQ-lora. 5 tokens per second. 39). 279 votes, 147 comments. The triton version gets 11. Update 1: I added tests with 128g + desc_act using ExLlama. no_flash_attn = True to tell ExLlama to ignore it, before model. 4 These are just options for 7b because 100+ tokens per second is a crazy high metric by larger model standards Also you would want 4 bit gptq with exllama loader selected Jul 29, 2023 · The same on a 4090 when interfering with a 33b model an 8k context size with over 4K chat history. Jun 29, 2023 · I have an older laptop without a dedicated video card and 16 GB RAM. cpp generation is reaching such negative peaks that it's a joke. 其中gen_begin函数中首先将输入预处理（推理）一遍. Or set config. Two weeks ago, only the first generation was slow, but now the llama. Bing GPT 4's response on using two RTX 3090s vs two RTX 4090s: Yes, you can still make two RTX 3090s work as a single unit using the NVLink and run the LLaMa v-2 70B model using Exllama, but you will not get the same performance as with two RTX 4090s. 13B 6Bit quantized is acceptable. For PC questions/assistance. It will pin the process to the listed cores, just in case Windows tries to schedule ExLlama on efficiency cores for some reason. Jun 2, 2023 · Unless you've got extremely slow cores or extremely fast VRAM, the operation ends up being entirely bandwidth-limited, and with even a naively written kernel the multiplication will be done in however long you can read in both matrices from RAM. Subreddit to discuss about Llama, the large language model created by Meta AI. Nov 7, 2023 · This may because you installed auto_gptq using a pre-build wheel on Windows, in which exllama_kernels are not compiled. This is not an Ooba specific issue but an issue for all WSL Aphrodite supports gguf, exl2, smooth quant+, awq, gptq and even more. 13b and both 4bit-32g. Is there something I am missing that is causing my EXL2 inference to hit a speed wall? I have been struggling with llama. 支持4位GPTQ量化模型; 动态批处理与智能提示缓存; K/V缓存去重优化; 简化的API设计 WARNING:Exllama kernel is not installed, reset disable_exllama to True WARNING:The safetensors archive passed at model does not contain metadata WARNING:skip module injection for FusedLlamaMLPForQuantizedModel not support integrate without triton WARNING - _base. 0)是不支持的CPU推理的，新版AutoGPTQ有实验性的支持。 2. cpp and ggml before they had gpu offloading, models worked but very slow. Lora models are not supported yet. With a 13b ggml model, I get about 4 tok/second with 0 layers offloaded (cpu is ryzen 3600). With regular exllama you can't change as many generation settings, this is why the quality was worse. It is activated by default. Note: It’s unclear to me how much the GPU is used during quantization. 11 seconds (25. EXLLAMA_NOCOMPILE= python setup. Minor thing, but worth noting. Example: from auto_gptq import exllama_set_max_input_length model = exllama_set_max_input_length(model, 4096) Falcon is uniquely slow. ExLlama supports 4bpw GPTQ models, exllamav2 adds support for exl2 which can be quantised to fractional bits per weight. It's the Exllama loaders that run poorly on P40s. true. It goes without saying if you use an Ada A6000 or two 4090s it could go even faster =] Does that only have 6GB VRAM? If so, you're going to struggle. They are marked with (new) Update 2: also added a test for 30b with 128g + desc_act using ExLlama. (pip uninstall exllama and modified q4_matmul. Is this just not possible? If it is, can someone pinpoint me to some examplary code in which ExLlama is used in python. llms. 42 ms per token, 40. The Pascal is usable and works very well, but you do have to fiddle around with drivers versions, cuda versions and bits and bytes versions (0. 165K subscribers in the LocalLLaMA community. Update 3: the takeaway messages have been updated in light of the latest data. 2 ; anything after that gets slow, x10 slower. py. Among these techniques, GPTQ delivers amazing performance on GPUs. Other than that basically 7b for speed. I don't believe they can really use CPU since that will be horribly slow for any sort of production. 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. It is probably because the author has "turbo" in his name. When asking a question or stating a problem, please add as much detail as possible. There is a technical reason for it (Which you can find detailed here if you are curious) but the TL;DR is that reading a file outside of WSL will always be significantly slower due to the way the filesystem is mounted. This will overwrite the quantization config stored in the config. But other larger context models are appearing every other day now, since Llama 2 dropped. 11 votes, 28 comments. Aug 29, 2023 · 高速推論のためのExllamaカーネル. . Let's try with llama 2 13b. cpp main. 0-GPTQ with text-generation-webui Loading the… Jan 17, 2025 · If you are really serious about using exllama, I recommend trying to use it without the text generation UI and look at the exllama repo, specifically at test_benchmark_inference. 9. Now that I added a second card and am running 70b, the best I can get is 11-12 t/s. If you’re doing inference on a CPU with AutoGPTQ 0. Won't be nearly as fast as exllama but you could offload a decent amount of layers to 3090 with ggml. Ah wait I misunderstood, never mind. You can use text-generation-webui's pre_layer to offload some to RAM but it will be very slow. More on ExLlama here: I see from your own testing testing that you have multi-GPU working. Exllama V2 defaults to a prompt processing batch size of 2048, while llama. 81 tokens/s Testing with Wizard-Vicuna-30BN-Uncensored 4-bit GPTQ, RTX 3090 24GB Another Slow 10gbe Performance Now, as the rows are processed in-order during inference, you have to constantly reload the quantization parameters, which ends up being quite slow. The issue with P40s really is that because of their older CUDA level, newer loaders like Exllama run terribly slow (lack of fp16 on the P40 i think), so the various SuperHOT models can't achieve full context. However, in the case of exllama v2, it is good to support Lora, but when using Lora, the token creation speed slows down by almost 2 times. It uses system RAM as shared memory once the graphics card's video memory is full, but you have to specify a "gpu-split"value or the model won't load. 7 tokens/s after a few times regenerating. I don't own any and while HIPifying the code seems to work for the most part, I can't actually test this myself, let alone optimize for a range of AMD GPUs. recently PRs have been slow to Some quick tests to compare performance with ExLlama V1. Aug 7, 2023 · There could be something keeping the GPU occupied or power limited, or maybe your CPU is very slow? I recently added the --affinity argument which you could try. So, you are probably looking for Aphrodite. I cannot seem to find any guide/tutorial in which it is explained how to use ExLlama in the usual python/huggingface setup. Here are some results with the TheBloke_airoboros-7B-gpt4-1. I have an rtx 4090 so wanted to use that to get the best local model set up I could. Downsides are that it uses more ram and crashes when it runs out of memory. Currently, the two best model backends are llama. This issue caused some people to opportunistically claim that the webui is "bloated", "adds an overhead", and ultimately should not be used if you care about performance. whether or not you're using the unpaged fallback mode. You can change that behavior by passing disable_exllama in GPTQConfig. It supports lots of quantization types, is incredibly fast for single users, and is also incredibly fast for multiple users as well. exl2 processes most things in FP16, which the 1080ti, being from the Pascal era, is veryyy slow at. load_autosplit(). 55 ms per token, 1. ExLlamaV2 [source] #. You can do that by setting n_batch and u_batch to 2048 (-b 2048 -ub 2048) ExLlama is closer than Llama. This extension allows AI artists to generate high-quality text locally on their machines, leveraging the advanced features of ExLlamaV2. Here's some quick numbers on a 13B llama model with exllama on a 3060 12GB in Linux: Output generated in 10. this is (unfortunately) expected behavior because there is one particular compilation unit, which uses cutlass, is extremely slow, which on my end took 10min to build. However, the more layers I offload the slower it is, and with all 43 models offloaded I only get around 2 tokens per second. Try classification. So I switched the loader to ExLlama_HF and I was able to successfully load the model. json file. For training lora, I am just curious if there is a back propagation module, whether the training speed will be much higher than the traditional I was worried the p40 & 3090ti combo would be too slow (plus I have 4 monitors and needed the video out) but I'm getting 11. ExLlamaV2: The Fastest Library to Run LLMs – Maxime Labonne I tried out llama. Mar 10, 2012 · RuntimeError: The temp_state buffer is too small in the exllama backend. cpp has matched its token generation performance, exllama is still largely my preferred inference engine because it is so memory efficient (shaving gigs off the competition) - this means you can run a 33B model w/ 2K context easily on a single 24GB card. py:733 - Exllama kernel is not installed, reset disable_exllama to True. CUDA kernels for auto_gptq are not installed, this will result in very slow inference speed. 4? No idea otherwise. Probably no point to bother for now. The model is turboderp/Llama2-7B-exl2 with revision 4. While it OOMs with regular ExLlama, I can load it with ExLlama_HF but it still OOMs upon inference. Username checks out; this probably will not help you for your use case. text-generation-webui-text-generation-webui-1 | 2023-08-15 05:47:18 WARNING:CUDA kernels for auto_gptq are not installed, this will result in very slow inference speed. Jul 10, 2024 · This may because you installed auto_gptq using a pre-build wheel on Windows, in which exllama_kernels are not compiled. Jul 10, 2023 · Very good work, but I have a question about the inference speed of different machines, I got 43. At this breakpoint, everything gets slow. The breakdown is Loader, VRAM, Speed of response. WARNING - CUDA kernels for auto_gptq are not installed, this will result in very slow inference speed. Using a GGML might be the better option for you, as that performs much better when partially on GPU and partially in RAM. You need 10GB minimum to load a 13B GPTQ with ExLlama. Very slow network speeds #8171 - microsoft/WSL. 4ビットモデルでは、より高速な推論を行うためにexllamaカーネルを使用することができます。これはデフォルトで有効になっています。GPTQConfigにdisable_exllamaを渡すことでこの動作を変更できます。 Jun 3, 2023 · But then also everything else has to be changed to FP32 from the FP16 it currently is in exllama because all FP16 ops are slow. I was hoping to add a third 3090 (or preferably something cheaper/ with more vram) one day when context lengths get really big locally but if you have to keep context on each card that will really start to limit things. If you want to use GPTQ models, you could try KoboldAI or Oobabooga apps. P40 can't use newer bitsandbyes. Also, yeah, merging a LoRA is a bit of a pain, since afaik you need to merge the weights onto the full-sized fp16 model, then save it, then run the merged model through GPTQ-for-LLaMA/AutoGPTQ so ExLlama can load it, and that all takes a lot of disk space and patience Feb 2, 2024 · On-the-fly Quant-Dequant makes the inference slow. With GPTQ models, I find some older models very slow! Some newer models, run 4x faster for me. Performance is lacking, especially on Ampere, and there may be a significant CPU bottleneck on slower processors until the extension functions are fully built out. 5 t/s with exllama (would be even faster if I had pcie 4) so you'd probably be fine with a p40. exllamv2 works, but the performance is very slow compared to llama-cpp-python. Here, it programs the primitive operation in the Nvidia A post about exllama_hf would be interesting. Please note: ↙. cu according to turboderp/exllama#111 After starting oobabooga Number 1: Don't use GPTQ with exllamav2, IIRC it will actually be slower then if you used GPTQ with exllama (v1) And yes, there is definitely a difference in speed even when fully offloaded, sometimes it's more then twice as slow as exllamav2 for me. But there is one problem. cpp are ahead on the technical level depends what sort of use case you're considering. I get 17. cpp in a while, so it may be different now. use_exllama is True by default and will enable the ExLlama backend for the model for faster inference. The text sent to ExLlama V2 is shared here: prompt_llm_proxy_sip_full. Weirdly, inference seems to speed up over time. py install --user This will install the "JIT version" of the package, i. model_name_or_path = "TheBloke/Mistral-7B-Instruct-v0. GGUF on TGI offers the same issue - 10-15 t/s with little variation between 7b and 70b model sizes. On a 70b parameter model with ~1024 max_sequence_length, repeated generation starts at ~1 tokens/s, and then will go up to 7. It would slow things down a lot on newer GPUs. So far I have attempted to use PEFT with the Huggingface . In the past exllama v1, there was a slight slowdown when using Lora, but it was approximately 10%. 96 tokens per second) llama_print_timings: eval time = 445772. 22 tokens/s speed on A10, but only 51. org e-Print archive For multi-gpu models llama. I do hear people talk about GGUF, but Im sceptical it is faster, however, I may be biased on that. cpp 8-bit through llamacpp_HF emerges as a good option for people with those GPUs until 34b gets released. And 2 cheap secondhand 3090s' 65b speed is 15 token/s on Exllama. Aug 30, 2023 · use_exllama. 量化模型GPU推理，但exllama报错： * exllama提供了一种高效的kernel实现，仅支持GPTQ方式量化得到的int4模型和Modern GPU，需要所有模型参数在GPU上。 Feb 5, 2024 · To use exllama_kernels to further speedup inference, you can re-install auto_gptq from source. And whether ExLlama or Llama. 32 tokens/s, 256 tokens, context 15, seed 1844401441) Output generated in 10. Inferencing will slow on any system when there is more context to process. I didn’t do 65b in this test, but I was only getting 2-3 t/s in Ooba and 13 t/s in exllama using only the A6000. exllamav2. I can't even get 2k context fused and barely touch 3k unfused. The EXLlama option was significantly faster at around 2. 1-GPTQ" To use a different branch, change revision Jul 26, 2023 · * exllama - while llama. Jun 19, 2023 · There is a flag for gptq/torch called use_cuda_fp16 = False that gives a massive speed boost -- is it possible to do something similar in exllama? Well, it would give a massive boost on the P40 because of its really poor FP16 support. 12 ms / 747 runs ( 0. We would like to show you a description here but the site won’t allow us. 其中q、k、v和rope是分开计算的。 在vllm中，q、k、v和rope是一起计算的，所以速度更快。 Dec 21, 2023 · 如果是transformers加载的，autogptq是否开启了exllama（如果有，可以开启看看速度），是否有cuda extension not installed的警告（如果有，需要按AutoGPTQ官方说明安装合适的编译版本，或者自行编译） We would like to show you a description here but the site won’t allow us. May 31, 2024 · ExLlama will attempt to use the library if it's present. Note that Windows 11 does not show virtual adapters so I had to apply the workaround using Powershell as Administrator: Dec 6, 2024 · ComfyUI-ExLlama-Nodes is an extension designed to enhance the capabilities of ComfyUI by integrating it with ExLlamaV2, a powerful local text generation library. Many people conveniently ignore the prompt evalution speed of Mac. This makes the models directly comparable to the AWQ and transformers models, for which the cache is not preallocated at load time. ExLlamaV2 is an inference library for running local LLMs on modern consumer GPUs. You should probably start with smaller models first because the P40 is a very slow card compared to modern cards. However, I need the model in python to do some large scale analyses. Jul 21, 2023 · Is that an A100 40GB or 80GB? I think you can probably safely rule out OOMs if it's 80GB. It took some trial & error, but I figured out that an 18, 23 split lets me use 4096 with neither card reaching the full 24 gb usage. For VRAM tests, I loaded ExLlama and llama. It uses the GGML and GGUF formated models, with GGUF being the newest format. One promising alternative to consider is Exllama, an open-source project aimed at improving the inference speed of Llama. This may because: Dec 18, 2023 · llama_print_timings: load time = 7602. I'm not talking about using the ggml lib for matrix calculations, it's literally using the llama. Then I tried to load TheBloke_guanaco-13B-GPTQ and unfortunately got CUDA out of memory. 4. Mar 4, 2024 · These quantized LLMs can also be fast during inference when using a GPU, especially with optimized CUDA kernels and an efficient backend, e. Though, I haven't tried llama. 35 seconds (24. It's the best of the affordable; terribly slow compared to today's RTX3xxx / 4xxx but big. But it's likely to be very slow. 16 ms per token, 6167. cpp and textwebui Jul 17, 2023 · If you happen to already have any Nvidia GPU that's Turing or newer (so 16, 20, 30, 40 series), you could install that alongside a 4090 and run OpenLLaMA 3B on it no problem; and I guess a Pascal (10 series) would probably run fine too even with ExLlama's partial (read: slow) support of that μarch, given 3B's small size. Also getting slow TGI GPTQ speed on 4bit 128g quants. 5 times faster than ExllamaV2. Jul 16, 2024 · Discusión sobre la lentitud de Llama3 en comparación con Ollama en los foros de Hugging Face. Also, exllama has the advantage that it uses a similar philosophy to llama. Could not manage to get any decent speed with exLlama. 9 t/sec. Use exllama_hf as the loader with a 4-bit GPTQ model and change the generation parameters to the "Divine Intellect" preset in oobabooga's text-generation-webui. WizardLM Wizard vicuna Guanaco Airoboros 1. I'm using text gen web ui with Mythalion-13B-GPTQ from Hugging Face and my response time for sillytavern are extremally slow ranging from 100 seconds to 200 seconds. Only works with bits = 4. Your help is highly appreciated. Check out airoboros 7b maybe for a starter. 0. 44 seco Hello guys These days I am playing around MetaIX/OpenAssistant-Llama-30b-4bit & TheBloke/wizardLM-13B-1. cpp or koboldcpp. working only with GPTQ models for now. with no actual answer as to why, its just slow. Aug 16, 2023 · Of course, with that you should still be getting 20% more tokens per second on the MI100. 28 tokens per second) llama_print_timings: prompt eval time = 63531. Sep 28, 2023 · 1. The best balance at the moment is to use 4Bit models like autogptq with exllama or 4Bit ggml with a group size of 128. If I go and run the same GGUF models in LMStudio though, I will get 25+ t/s on 7b models and much better inference speeds across the board. Aug 6, 2023 · To use exllama_kernels to further speedup inference, you can re-install auto_gptq from source. Llama2 i can run 16b gptq (gptq is purely vram) using exllama Llama2 i can run 70B ggml, but it is so slow. Bases: LLM ExllamaV2 API. so if exllama supports model like Qwen-72b-chat-gptq, it Nov 20, 2023 · Quantizing Large Language Models (LLMs) is the most popular approach to reduce the size of these models and speed up inference. Otherwise they’re trying to solve the wrong problems, or trying to solve what exllama/exl2 already solves. Reply reply MustBeSomethingThere • People have been recommending P40 without knowing/understanding its poor FP16 Jun 10, 2023 · Yeah slow filesystem performance outside of WSL is a known issue. The quantization time could be reduced with Google Colab V100 or an RTX GPU. 00 ms / 746 runs ( 597. cpp because the code is literally a modification of llama. To be clear, GPTQ models work fine on P40s with the AutoGPTQ loader. Exllama kernels for faster inference For 4-bit model, you can use the exllama kernels in order to a faster inference speed. cpp, AutoGPTQ, ExLlama, and transformers perplexities. 2+, disable the ExLlama kernel in GPTQConfig. after installing exllama, it still says to install it for me, but it works. Is there a way I can run it faster? ExLlamaV2是一个专为在现代消费级GPU上本地运行大语言模型(LLM)而设计的高效推理库。它是ExLlama项目的升级版本,旨在提供更快速、更节省内存的LLM推理体验。 主要特点. 7 t/sec with exllama but that isn't compatible with most software. This may because you installed auto_gptq using a pre-build wheel on Windows, in which exllama_kernels are not compiled. It might be that the CPU speed has more impact on the quantization time than the GPU. 67 tokens per second) llama_print_timings: total time Oobabooga WebUI had a HUGE update adding ExLlama and ExLlama_HF model loaders that use LESS VRAM and have HUGE speed increases, and even 8K tokens to play ar The official API server for Exllama. The framework is not yet fully optimized. Cutlass is known as “slow to build” anyways… arXiv. This overwrites the attributes related to the ExLlama kernels in the quantization config of the config. I get about 700 ms/T with 65b on 16gb vram and an i9 Reply reply Also first generation is usually slow, so 2nd and 3rd generation will be more like the results you want to see. Possibly they are EXL2 (ExLlama v2) format, which is much faster anyway. Please call the exllama_set_max_input_length function to increase the buffer size. With the same parameters. As you will see, there are 2x models. Exllama: 9+ t/s, ExllamaV2 1. cpp on the other hand is capable of using an FP32 pathway when required for the older cards, that's why it's quicker on those cards. May 8, 2025 · ExLlama-v2 support# ExLlama is a Python/C++/CUDA implementation of the Llama model that is designed for faster inference with 4-bit GPTQ weights. py they both worked on one of my RTX 3060's f I limited it to 3072 because 4096 filled my vram and caused it to slow down. (I didn’t have time for this, but if I was going to use exllama for anything serious I would go this route). Following the instructions and running test_benchmark_inference. 0bpw. 43 ms llama_print_timings: sample time = 121. Can those be installed along side standard Geforce drivers? A direct comparison between llama. A place to discuss the SillyTavern fork of TavernAI. I have a fork of GPTQ that supports the act-order models and gets 14. nope, old Exllama still ~2. AutoGPTQ works fine but it's still rather slow to inference. But then the second thing is that ExLlama isn't written with AMD devices in mind. cpp to plugging into PyTorch/Transformers the way that AutoGPTQ and GPTQ-for-LLaMa do, but it's still primarily fast because it doesn't do that. 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 and support for HF Jinja2 chat templates. exllama makes 65b reasoning possible, so I feel very excited. GPTQ can be used with different loaders but the fastest are Exllama/Exllamav2, EXL2 works only with Exllamav2. Scan over the pull requests on the exllama repo to see why it is so fast. When I load a 65b in exllama across my two 3090tis, I have to set the first card to 18gb and the second to the full 24gb. cpp is way slower to ExLlama (v1&2), not just a bit slower but 1 digit slower. For those who are not aware of this feature, it allows the LLM loaders to use a smaller "draft" model to help predict tokens for a larger model. Not sure if it's just 70b or all models. it will install the Python components without building the C++ extension in the process. to be clear, all i needed to do to install was git clone exllama into repositories and restart the app. Also I noticed that autoGPTQ works best if frozen at v0. cpp. I am aware you can attach LoRAs to models hosted by textgen-webui and llama. py:766 - CUDA kernels for auto_gptq are not installed, this will result in very slow inference speed. generate() method; however, inference is too slow for regular use. I setup WSL and text-webui, was able to get base llama models working and thought I was already up against the limit for my VRAM as 30b would go out of memory before The recommended software for this used to be auto-gptq, but its generation speed has since then been surpassed by exllama. According to the project's repository, Exllama can achieve around 40 tokens/sec on a 33b model, surpassing the performance of other options like AutoGPTQ with CUDA. Nov 3, 2023 · from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline. cpp defaults to 512. , ExLlama for GPTQ. The ExLlama kernel is activated by default when users create a GPTQConfig object. cpp beats exllama on my machine and can use the P40 on Q6 models. Not even GPTQ works right now. - theroyallab/tabbyAPI Jun 19, 2023 · In fact, I can use 8 cards to train a 65b model based on bnb4bit or gptq, but the inference is too slow, so there is no practical value. The only way to make it practical is with exllama or similar. To quantize Llama 2 70B, you can do the same. py or test_chatbot. To use exllama_kernels to further speedup inference, you can re-install auto_gptq from source. If you have a specific Keyboard/Mouse/AnyPart that is doing something strange, include the model number i. Hi, I am working with a Telsa V100 16GB to run Llama-2 7b and 13b, I have used gptq and ggml version. Both GPTQ and exl2 are GPU only formats meaning inference cannot be split with the CPU and the model must fit entirely in VRAM. exl2 is also good for 6bit and 8bit if you need reference tests, and can’t stomach the painfully slow HF transformers running in 8 bit. Also the memory use isn't good. ExLlama gets around the problem by reordering rows at load-time and discarding the group index. Dec 10, 2023 · No, it can run in 2x3090 with 8-bit or 4-bit quantization using bitsandbytes, but it runs extremely slow. Ok, maybe it's the fact I'm trying llama 1 30b. Jul 10, 2023 · exllama is very optimized for consumer GPU architecture so hence enterprise GPUs might not perform or scale as well, im sure @turboderp has the details of why (fp16 math and what not) but thats probably the TLDR Sillytavern Local model response time being extremely slow, please help me understand why and help me possibly fix it. Use Exllama (does anyone know why it speeds things up?) Use 4 bit quantization so that I can run more jobs in parallel Exllama is GPTQ 4-bit only, so you kill two birds with one stone here. 4 tokens/s speed on A100, according to my understanding at least should Twice the difference Is there a Apr 30, 2023 · @lhl the make flag is passed properly. Aug 29, 2021 · Very slow network speed on WSL2 · Issue #4901. 27 seconds (24. the generation very slow it takes 25s and 32s respectively. ExLlamaV2# class langchain_community. 13b ooba: 26 t/s 13b exllama: 50 t/s 33b ooba: 18 t/s 33b exllama: 26 t/s. That being said, has anyone figured out a way to load a 13B GPTQ model onto a 8 GB card? Some quick tests to compare performance with ExLlama V1. Tested: ExllamaV2's max context on 24gb with 70B low-bpw & speculative sampling performance I'm having a similar experience on an RTX-3090 on Windows 11 / WSL. This makes running 13b in 8-bit precision the best option for those with 24GB GPUs. 33 ms / 2602 tokens ( 24. Jun 20, 2023 · It also takes a considerable context length before attention starts to slow things down noticeably, since every other part of the inference is O(1). This may because: Check the TGI version and make sure it’s using the exllama kernels introduced in v0. Apr 5, 2024 · Hi, I tried to use exllamv2 with Mistral 7B Instruct instead of my llama-cpp-python test implementation. cpp and other normal llama. I know that of course I can offload some layers to the CPU or run GGML, but at that point it's incredibly slow. If inference speed is not your concern, you should set desc_act to True. To use, you should have the exllamav2 library installed, and provide the path to the Llama model as a named parameter to the constructor. g. However, saying that, as mentioned, if you can keep the whole model+context in VRAM, Ive experienced little slow down. 3. I'm aware that there are GGML versions of those models, but the inference speed is painfully slow compared to ExLlama w/ GPU Scheduling: Three-run average = 43. Here are my previous results. K80 (Kepler, 2014) and M40 (Maxwell, 2015) are far slower while P100 is a bit better for training but still more expensive and only has 16GB and Volta-Class V100 (RTX2xxx) is far above my price point. cpp option was slow, achieving around 0. In this case they're comparing against llama. 93 tokens/s, 256 tokens, context 15, seed 545675865) Output generated in 10. 74 tokens/s, 256 tokens, context 15, seed 91871968) Feb 20, 2024 · which seems quite slow compared with the benchmark number. So the original problem of that remains. cpp is a C++ refactoring of transformers along with optimizations. txt. 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. With every hardware. cpp's metal or CPU is extremely slow and practically unusable. With exllamv2 I get my sample response in: 35. GPTQ and EXL2 are meant to be used with GPU. The utilization of lower-precision floating-point formats such as FP4 poses a dual challenge regarding memory efficiency and computational speed during inference. Nov 15, 2023 · Qwen is the sota open source llm in China and its 72b-chat model will be released this month. They are way cheaper than Apple Studio with M2 ultra. Speaking from personal experience, the current prompt eval speed on llama. With the fused attention it is fast like exllama, but without it is slow AF. Whether to use exllama backend. GGUF/llama. cpp, but if I am not mistaken you need to reload the entire base model + lora whenever you wish to swap out the adapter. It also scales almost perfectly for inferencing on 2 GPUs. OAI compatible, lightweight, and fast. 4-GGML model: First of all, exllama v2 is a really great module. So I would just uninstall flash-attn if you can't use it anyway, then the fallback mode should work. Much appreciated! Jan 29, 2024 · ExLlama is a smaller project but contributions are being actively merged (I submitted a PR) and the maintainer is super responsive. It is the moment, your vram is getting full. This may because: 1. Qwen-int4 is supported by autogptq. I wonder if the speed I got is expected or somehow I missed some important steps. 量化模型不支持CPU推理：旧版AutoGPTQ (<5. The "HF" version is slow as molasses. Jul 8, 2023 · Describe the bug I had the issue mentioned here: #2949 Generation with exllama was extremely slow and the fix resolved my issue. WSL 2 — How To Fix Download Speed | by Chris Townsend. low internet speed in WSL 2. 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. cache/torch_extensions for subsequent use. Instead, check out text-generation-webui, it will let you stand up a model on your cards. P40 needs Tesla specific drivers. cpp loader and GGUF (using oobabooga and the same LLM model), no matter how I set the parameters and how many offloaded layers to GPUs, llama. Transformers has the load_in_8bit option, but it's very slow and unoptimized in comparison to load_in_4bit. cpp and exllama, in my opinion. In that thread, someone asked for tests of speculative decoding for both Exllama v2 and llama. There may be more performance optimizations in the future, and speeds will vary across GPUs, with slow CPUs still being a potential bottleneck: The llama. cpp code. Instead, the extension will be built the first time the library is used, then cached in ~/. There may be more performance optimizations in the future, and speeds will vary across GPUs, with slow CPUs still being a potential bottleneck: The ExLlama kernels are only supported when the entire model is on the GPU. cpp models with a context length of 1. The ExLlama kernels are only supported when the entire model is on the GPU. Using both llama. I assume its a bug to be ironed out at some point. P40 can't do FP16, too slow for ExLlama. It's also shit for samplers and when it doesn't re-process the prompt you can get identical re-rolls. So the CPU bottleneck is removed, and all HF loaders are now faster, including ExLlama_HF and ExLlamav2_HF. but it will become very slow run in multiple gpus. cpp in being a barebone reimplementation of just the part needed to run inference. e. 2024-02-05 12:34:08,056 - WARNING - _base. ExLlama A standalone Python/C++/CUDA implementation of Llama for use with 4-bit GPTQ weights, designed to be fast and memory-efficient on modern GPUs. 4 t/sec. They are much closer if both batch sizes are set to 2048. Its really quite simple, exllama's kernels do all calculations on half floats, Pascal gpus other than GP100 (p100) are very slow in fp16 because only a tiny fraction of the devices shaders can do fp16 (1/64th of fp32). This is not a fair comparison for prompt processing. With every model. It is capable of mixed inference with GPU and CPU working together without fuss. But upon sending a message it gets CUDA out of memory again. Restarting seems to fix. ExLlama and exllamav2 are inference engines. Llama. ExLlama doesn't support 8-bit GPTQ models, so llama. Yes the models are smaller but once you hit generate, they use more than GGUF or EXL2 or GPTQ. for models that i can fit into VRAM all the way (33B models with a 3090) i set the layers to 600. This is an early preview release of ExLlamaV3. Sometimes, Ive see the ExLlama loader just be slow. i'm pretty sure thats just a hardcoded message. Etc. Aug 9, 2024 · ExLlamaV2 是目前运行大型语言模型（LLMs）最快的库，通过优化 GPTQ 算法和引入新的量化格式 EXL2，显著提升了推理速度和灵活性。。EXL2 格式支持多种量化精度，并允许在模型内部和层之间混合使用不同的精度，从而在保持模型性能的同时减少资源占 Get up and running with large language models. **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 characters you or the community create. Here's a comparison from Oobabooga himself/herself. Exllama does not run well on it, I get less than 1t/s. They are equivalent to llama. fyjgt zcxmoxvo epqt feswn lihe cnayt acqi qlcaic nbo wxmib