P40 gptq. Using a Tesla P40 I noticed that when using llama.
P40 gptq 0-16k-GPTQ:gptq-4bit-32g-actorder_True. (GPTQ 4 bit), around 7-9 on 30B models and about 2-3 on 65B models split across the two cards in textgen-ui. The End for QwenLM/vllm-gptq. We also outperform a recent Triton implementation for GPTQ by 2. gguf only the rtx3090 (GPU 0) and the CPU. Also, Tesla P40’s lack FP16 for some dang reason, so they tend to suck for training, but there may be hope of doing int8 or maybe int4 inference on them. No SD: Prompt processed in 0. _base:GPT2GPTQForCausalLM hasn't fused attention The benefits of GPTQ for 4-bit quantization is negligible vs RTN, so GPTQ really only has a place in 2/3-bit quant. Transformer recognize all GPUs. A GPTQ model should even inference faster than an equivalent-bitrate EXL2 model. This repository has fulfilled its role. modeling import BaseGPTQForCausalLM class OPTGPTQForCausalLM (BaseGPTQForCausalLM): # chained attribute name of transformer layer block layers_block_name = "model. 57 seconds, 250 tokens, 33. The full manuscript of the paper is available at GPTQ: Accurate Post-Training Quantization for Generative Pre-Trained Transformers. Here's a recent writeup on the LLM performance you can expect I've seen people use a Tesla p40 with varying success, but most setups are focused on using them in a standard case. Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them. 30. Don't use the load-in-8bit command! The fast 8bit inferencing is not supported by bitsandbytes for cards below cuda 7. Defaulting to 'pt' metadata. 3060 is 2 generations of compute newer so supports all inference engines and flash attention but won't fit any larger models. . Finally, let's look at the time to load the model: load_in_4bit takes a lot longer because it has to read and convert the 16-bit model on the fly. Would it be easier to pack two weights into one int8? Still a lot of work though. Use this flag CUDA_VISIBLE_DEVICES=x to choose devices Is there an existing issue for t. There is a flag for gptq/torch called use_cuda_fp16 = False that gives a massive speed boost -- is it possible to do The Tesla P40 is much faster at GGUF than the P100 at GGUF. cpp and some old forks of GPTQ that do intermediate calcs at FP32. 7× over GPTQ, and 1. 24 seconds. I've also made it clear that while GGML is now rivalling or beating AutoGPTQ and GPTQ-for-LLaMa, it's not yet close to exllama, the turbo-charged GPTQ implementation by turboderp. Your work is greatly appreciated. WARNING:auto_gptq. The "HF" version is slow as molasses. 74 tokens/second Response generated in 7. the inference code shows something like error: invalid element type in packLLEElements. Currently the max speed of one CPU core will be a bottleneck - just like it often is with GPTQ. But . i'm beginner with text-generation webui. 04. modeling:The safetensors archive passed at gpt2-GPTQ/gptq_model-4bit-128g. 85× speed up over cuBLAS FP16 implementation. GPTQ is a neural network compression technique that enables the efficient deployment of Generative Pretrained Transformers (GPT). q6_K version of the model (llama. Auto GPTQ is slower, Old Nvidia P40 (Pascal 24GB) cards are easily available for $200 or less and would be easy/cheap to play. For training: would the P40 slow down the 3090 to its speed if WARNING:accelerate. 53 seconds. 1B-1T-OpenOrca-GPTQ. It is useful to look at the plot without it: Wow, it got it right! localmodels. 04 tokens/second. 58 seconds. I find it very slow with 1,6 token/s for the model "THE BLOKE - vicuna-13B-1. My P40 still P40 has the big VRAM but also basically unusable FP16 performance, it will run only llama. The 8bit models are higher quality than 4 bit, but again more memory etc. safetensors does not contain metadata. decoder. To download from another branch, add :branchname to the end of the download name, eg TheBloke/Yi-34B-GPTQ:gptq-4bit-128g-actorder_True. 1. Expected 'f32' but got 'f16' err Using a Tesla P40 I noticed that when using llama. Another test I like is to try a group chat and really test character positions. Eventually it would be nice to have this, but given the lack of a robust 3-bit CUDA kernel this is a non-starter for How to download, including from branches In text-generation-webui To download from the main branch, enter TheBloke/Nous-Capybara-34B-GPTQ in the "Download model" box. Also: Thanks for taking the time to do this. 45×, a maximum speedup of 1. the old gptq was incidentally similar enough to , i think q4_0, that adding a little padding was enough to make it work. quantization is a lossy thing. < llama-30b-4bit 2nd load Running a 3090 and 2700x, I tried the GPTQ-4bit-32g-actorder_True version of a model (Exllama) and the ggmlv3. Loading time. INFO:Loaded the model in 104. However, it has been surpassed by AWQ, which is approximately twice as fast. I wonder if it was old instances of gptq_llama being installed. which is efficient enough to execute on models with hundreds of billions of parameters in at most a few hours, and precise enough to compress such models to 3 or 4 bits per For multi-gpu models llama. 5 and the p40 does only support cuda 6. Additionally, vllm now includes Marlin and MoE support. P40 makes i'm beginner with text-generation webui. latest. you can easily fit a 13B and 15B GPTQ should be significantly faster in ExLlamaV2 than in V1. 84 seconds. GGUF edging everyone out with it's P40 support, good performance at the high end, and also CPU inference for the low end. So GPTQ through ExLlamav2 is actually the model with the fastest evaluation speed of all, 13% faster than the same model on ExLlama v1. < llama-30b FP16 2nd load INFO:Loaded the model in 39. EDIT - Just to add, you can also change from 4bit models to 8 bit models. cpp beats exllama on my machine and can use the P40 on Q6 models. But is there something like GPTQ that runs well on older pascal cards like the P40? GGUF runs well on P40s, but I'd imagine something GPU _ CUDA specific would work even better on a P40, but it Loading: Much slower than GPTQ, not much speed up on 2nd load. Old Nvidia P40 (Pascal 24GB) cards are easily available for $200 or less and would be easy/cheap to play. Since both have OK speeds (exllama was much faster but both were fast enough) I would recommend the GGML. Since December 2023, vllm has supported 4-bit GPTQ, followed by 8-bit GPTQ support since March 2024. i have a ryzen 5 I'd really like to try a 1024 group version to see if it would run full but you only have that for triton. It sort of get's slow at high PCI-e x16 or x8 for the p40? I have the same problem with p40. 1-GPTQ-4bit-128g. modeling. I have a P40 in a R720XD and for cooling I used attached some fans I pulled from a switch with some teflon tape on the intake side of the P40 housing and use an external 12v power supply to drive the fans. What’s New in Qwen2-VL? Key Enhancements: SoTA understanding of images of various resolution & ratio: Qwen2-VL achieves state-of-the-art performance on visual understanding benchmarks, including Explore and code with more than 12 million developers，Free private repositories ！：） I'm using old p40 , which seems not supporting fp16 I tried to latest triton branch, and compile triton from master. 02 Driver Version: 530. So, using GGML models and the I'm seeing 20+ tok/s on a 13B model with gptq-for-llama/autogptq and 3-4 toks/s with exllama on my P40. Downsides are that it uses more ram and crashes when it runs out of memory. How to download, including from branches In text-generation-webui To download from the main branch, enter TheBloke/Nous-Hermes-2-Yi-34B-GPTQ in the "Download model" box. For all models that are larger then the RAM do not work even cud fit in VRAMs + RAM. Strange some times works faster depending of the model. Autogptq loads but can only do very small But you can run Llama 2 70B 4-bit GPTQ on 2 x 24GB and many people are doing this. At a rate of 25-30t/s vs 15-20t/s running Q8 GGUF models. I’ve found that ideally packing 8 weights x 4 bit into one fp32 would increase performance by 8 time minus overhead and allow up to 40B GPTQ models. I got it running now. 02 CUDA Version: 12. It's also shit for samplers and when it doesn't re-process the prompt you can get identical re-rolls. auto_gptq and gptq_for_llama can be specified to use fp32 vs fp16 calculations, but this also means you'll be hurting performance drastically on the 3090 cards (given there's no way to indicate using one or the Qwen2-VL-7B-Instruct-GPTQ-Int8 Introduction We're excited to unveil Qwen2-VL, the latest iteration of our Qwen-VL model, representing nearly a year of innovation. 4× since it relies on a high-level language and forgoes opportunities for low-level optimizations. 6 tokens/s. This was to be expected. In this paper, we present a new post-training quantization method, called GPTQ, 1 1 1 This merges the name of the OPT model family with the abbreviation for post-training quantization (PTQ). Llama-2-70B-GPTQ and ExLlama. Running xubuntu as the OS because regular Ubuntu is a pain in the ass with from auto_gptq. Using GPTQ 8bit models that I quantize with gptq-for-llama. From the command line Previously, GPTQ served as a GPU-only optimized quantization method. From the command line I am splitting it. Following the latency for 256 input size and 256 output size with Mistral-7B quants. Why If P40 will not work with exllama, could somebody advise if oobabooga/GPTQ-for-LLaMa would work? If not CUDA, maybe there are good options for i9-13900K with 128G DDR5? P40 makes 4 times more int8 operations than fp32 (47 TOPS vs 12 FLOPS). 09 seconds, 4 tokens, 42. Question: Which is correct to say: “the yolk of the egg are white” or “the yolk of the egg is white?” Factual answer: The yolks of eggs are yellow. Possibly because it supports int8 and that is somehow used on it using its higher CUDA 6. But when using models in Transformers or GPTQ format (I tried Transformers, AutoGPTQ, all ExLlama loaders), the performance of 13B models even in quad bit format is But since the P40 is way slower than the 3090, the 3090 will be idle like 90-95% of the time waiting for the P40 to finish processing before it can take its turn on the next token. Or just manually download it. safetensors". and llama. 3~0. in the download section. GPTs are a specific type of Large Language Model (LLM) developed by OpenAI. The server already has 2x E5-2680 v4's, 128gb ecc ddr4 ram, ~28tb of storage. Make sure to save your model with the `save_pretrained` method. layers" # chained attribute names of other nn modules that in the same level as the transformer layer block outside_layer_modules = [ Draft model: TinyLlama-1. To download from another branch, add :branchname Better performance for GPTQ & AWQ; We extend the marlin kernel to desc-act GPTQ model as well as AWQ model with zero points, and repack the model on the fly. so Mac Studio with M2 Ultra 196GB would run Llama 2 70B fp16? Personally I gave up on using GPTQ with the P40 because Exllama - with its superior perf and vram efficiency compared to other GPTQ loaders - doesn't work. CodeLlama 34B v2 - GPTQ Model creator: Phind Original model: CodeLlama 34B v2 Description This repo contains GPTQ model files for Phind's CodeLlama 34B v2. 1 From a practical perspective, this means you won't realistically be able to use exllama if you're trying to split across to a P40 card. Have ‘char a’ perform an action on ‘char b’ and also have ‘char b’ perform and action on ‘user’ and have ‘user perform an action on either ‘char’ and see how well it keeps up with who is doing TheBloke/SynthIA-7B-v2. 3090 can use triton but P40 cannot. but I guess it is a lot of work. < llama-30b-4bit 1st load INFO:Loaded the model in 7. I think I can only do half context and get about 1it/s, slightly over if I just do instruct. I'd really like to try a 1024 group version to see if it would run full but you only have that for triton. Try 4bit 32G and you will more than likely be happy with the result! The more VRAM the better if you'd like to run larger LLMs. < llama-30b FP32 2nd load INFO:Loaded the model in 68. X16 is faster then X8 and x4 douse not work with p40. Remarkably, despite utilizing an additional bit per weight, AWQ achieves an average speedup of 1. With the Q4 GPTQ this is more like 1/3 of the time. i have a ryzen 5 2400g + Tesla P40 + 48 go Ram on ubuntu 22. 1 In the past I've been using GPTQ (Exllama) on my main system with the 3090, but this won't work with the P40 due to its lack of FP16 instruction acceleration. \end{blockquote} Describe the bug Using main branch with commit 4affa08, When I choose 3090, it's about 15 token/s, but when I use p40, it's only has 0. cpp with all layers offloaded to GPU). utils. cpp does not support gptq. If one has a pre-quantized LLM, it How to download, including from branches In text-generation-webui To download from the main branch, enter TheBloke/Yi-34B-GPTQ in the "Download model" box. with nvidia-smi command : NVIDIA-SMI 530. The speed was ok on both (13b) and the quality was much better on the "6 bit" GGML. To download from another branch, add :branchname to the end of the download name, eg TheBloke/Nous-Capybara-34B-GPTQ:gptq-4bit-128g-actorder_True. cpp the video card is only half loaded (judging by power consumption), but the speed of the 13B Q8 models is quite acceptable. But this will hopefully improve in future. tucxft lzaqkbi budknse sxoc ozwxq anr chald fuvix ymdu ntprdsr