Awq quantization vllm github serve. ValueError: The input size is not aligned with the quantized weight shape. In general, AWQ is faster and more accurate than Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community. Pre-computed AWQ model zoo You signed in with another tab or window. 这是vllm不支持gptq量化的模型吗 Dependencies ‘’‘ peft 0. ai Themes. Hi vLLM team! I was testing vLLM 0. However, I was under the impression that the --tensor-parallel-size would partition the model between the two gpus however both gpu is utilizing the same amount of memory I saw @WoosukKwon's msg here on how to setup AWQ. bfloat16 to torch. INFO 03-14 01:20:43 llm_engine. 4-bit AWQ (A4W16) quantization has already been implemented in vLLM 0. py` here 🐛 Describe the bug When N=64, we don't have 4*8=32 c_warp result; In this case, we only have 2(N/32) * 8=16 c_warp results. 👍 4 cody-moveworks, JuLian1130, zx12671, and renwuli reacted with thumbs up emoji 👀 2 cody-moveworks and leocnj reacted with eyes emoji Below is an example for the simplest use of auto_awq with QUICK to quantize a model and inference after quantization: Quantization & Inference Expect this to take 10-15 minutes on smaller 7B models, and around 1 hour for 70B Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community. @WoosukKwon If you need to create a new format for the INT4 packed weights to optimize throughput, let me know and we can work this into AutoAWQ as a new format to optimize throughput. Use quantization=awq_marlin for faster inference WARNING 10-18 10:01 Efficient and accurate low-bit weight quantization (INT3/4) for LLMs, supporting instruction-tuned models and multi-modal LMs. SoTA understanding of images of various resolution & ratio: Qwen2-VL achieves state-of-the-art performance on visual understanding benchmarks, including MathVista, DocVQA, RealWorldQA, MTVQA, etc. , local PC WARNING 03-14 01:20:43 config. ) on Intel XPU (e. 6x-2. More information on AWQ here. However, the astronomical model size and the limited hardware resource pose significant deployment challenges. 🚀 vLLM and SGLang inference integration for quantized model where format = FORMAT. 0 ‘’’ 2023-12-08 17 Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community. 0. Quantizing reduces the model's precision from FP16 to INT4 which effectively reduces the file size by AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. vllm_worker). For other GPUs, you may use nvidia-smi --query-gpu=compute_cap --format=csv to get the compute capability and merely change '87' to that. For some reason I get wierd response when I talk with the AI, or at least not as good as when I w llmcompressor now supports quantizing weights, activations, and KV cache to fp8 for memory savings and inference acceleration with vllm. You signed out in another tab or window. Currently, we support "awq", "gptq" and "squeezellm". Currently we have a very hacky version of vLLM integration - mostly because of the pre-fused layers such as qkv and up_proj. This may cause the following quantization check failures when performing model inference on ROCm GPU using GPTQ or AWQ quantization Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community. Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community. AutoAWQ implements the vLLM supports AWQ, GPTQ and SqueezeLLM quantized models: To use AWQ model you need to install the autoawq library pip install autoawq. The end conclusion is as following, you are seeing undesirable performance because vLLM's under-optimized support for AWQ models at the moment. The calibration data is alreay 🚀 | This serverless worker utilizes vLLM (very Large Language Model) behind the scenes and is integrated into RunPod's serverless environment. g. If None, we first check the quantization_config attribute in the model config file. TLDR: Deploying LLMs is difficult due to their large memory size. Currently, only Hopper and Ada Lovelace GPUs are officially I've managed to deploy vllm using vllm openai compatible entrypoint with success between all the gpus available in my kubernetes node. post1 Model Input Dumps ValueError: Weight input_size_per_partition = 10944 is not divisible by min_thread_k = 128. 5 are 🚀 The feature, motivation and pitch As the title suggests Currently, VLLM supports MOE, but does not support quantitative versions. 0 has not been not released yet, so please clone the main and build it from source. Please help me understand why? @TheBloke WARNING: WatchFiles detected changes in 'fastapi_vllm_codellama. Quantization reduces the bit-width of model Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community. 5x speed boost on For those of us that have downloaded a large archive of gguf models, it would be a great benefit to use the vLLM project with the artifacts we have already downloaded and available, rather than downloading fp16 or awq and Add AWQ quantization inference support Fixes #781 This PR (partially) adds support for AWQ quantization for inference. Latest News 🔥 [2023/12] Mixtral, LLaVa, QWen Did some additional tests, seems that running models through vllm somehow messes up my GPU. 1+cu124 Is debug build Proposal to improve performance Hi~ I find the inference time of Qwen2-VL-7B AWQ is not improved too much compared to Qwen2-VL-7B. 1+cu113 Is AutoAWQ implements the Activation-aware Weight Quantization (AWQ) algorithm for quantizing LLMs. from transformers import AutoTokenizer from vllm import LLM, SamplingParams MODEL AWQ quantization Continuous batching Streaming output Efficient implementation of decoding strategies (parallel decoding, beam search, etc. 9 Love the LLAMA2-AWQ support, really handy! Are there any plans to support Falcon-180B-AWQ in the near future? Have a question about this project? Sign up for a free GitHub account to open an issue and contact its Large language models (LLMs) have transformed numerous AI applications. Reload to refresh your session. config. After a year's relentless efforts, today we are thrilled to release Qwen2-VL!Qwen2-VL is the latest version of the vision language models in the Qwen model families. However, how i have a question, can i leverage ray between multiple nodes? With Find and fix vulnerabilities [MLSys 2024 Best Paper Award] AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration - mit-han-lab/llm-awq AutoAWQ implements the Activation-aware Weight Quantization (AWQ) algorithm for quantizing LLMs. 7 for serving Mixtral-8x7B-Instruct-v0. Collecting environment information PyTorch version: 2. 🐛 Describe the bug Hello, I am running llama3-70b and mixtral with VLLM on a bunch of different kinds of machines. AutoAWQ implements the AWQ algorithm for 4-bit quantization with a 2x speedup during inference. Skip to content Navigation Menu Toggle navigation Sign in Product GitHub Copilot Actions To effectively run AWQ models in vLLM, it is essential to understand the quantization process and how to utilize the models efficiently. I downloaded the weights from the bloke here but I'm having issues with Mistral as it's bfloat16 and currently for quantization it seems you have some assumptions To create a new 4-bit quantized model, you can leverage AutoAWQ. ’‘’ from vllm import LLM, SamplingParams prompts = [ "Tell me about AI", "Write a story about llamas", "What is Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community. Try launching with awq instead xinference launch --model-engine To create a new 4-bit quantized model, you can leverage AutoAWQ. The following sections provide a comprehensive guide on quantizing models and Find and fix vulnerabilities To create a new 4-bit quantized model, you can leverage AutoAWQ. The speed can be slower than non-quantized mode Skip to content Navigation Menu Toggle navigation Sign in Product Security Find and fix Hi there, I was struggling on how to implement quantization on autoawq as you mentioned in home page. 5. AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration - fkatada/hf-llm-awq Skip to content Navigation Menu Toggle navigation Sign in Product GitHub Copilot Write better code with AI Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community. To use GPTQ models you need to install the In this blog, we explore AWQ, a novel weight-only quantization technique integrated with vLLM. You can also specify other bit rates like 3-bit, but some of these options may lack kernels for running inference. 在启动后推理了，推理是1的并发不停的请求，一段时间内显存占用固定为差不多14G； 然而在1个多小时之后，vllm What's the difference netween so many options. Alternatives As the title su Hi @ryanshrott, If you are using VLLM via LangChain, so, the correct code is as follows. and I have some local code that is a thin wrapper around LLM class If i run this with tensor-parallel == 2 I get the following: Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community. 用vllm部署32b-chat没问题，就是慢，部署32bAWQ-chat后 Qwen2. from langchain. 🚀 The feature, motivation and pitch While running the vLLM server with quantized models specifying the quantization type, the below mentioned Warning is shown: WARNING 04-25 12:26:07 config. After quantizing a llama3-70B model, I'm using lora weights with the --lora-plugin parameter set. QServe: W4A8KV4 Quantization and System Co-design for Efficient LLM Serving - mit-han-lab/qserve Skip to content Navigation Menu Toggle navigation Sign in Product GitHub Copilot Write better code with AI Actions Contribute to powderluv/vllm-docs development by creating an account on GitHub. py:87] Initializing an LLM engine with config Please note that this compatibility chart may be subject to change as vLLM continues to evolve and expand its support for different hardware platforms and quantization methods. Documentation: - ohso4/AutoAWQ-torch-2. Activation-aware weight quantization (AWQ) [2] has recently been one of the most popular quantization algorithms. py:19] Failed to import from vllm. Based on the information available in the LangChain repository, there was a similar issue related to VLLM which was resolved replicaCount: 1 # Change this if you want to serve another model model: mistralai/Mistral-7B-Instruct-v0. Additionally, please note that these commands are just for Jetson Orin GPUs, whose CUDA compute capability is 87. you can find -awq model on my huggingface 👍 1 nigue3025 reacted with thumbs up emoji ️ 1 nigue3025 reacted with heart emoji All reactions Find and fix vulnerabilities Find and fix vulnerabilities This page is accessible via roadmap. ) Multi-GPU support Integration with HuggingFace Deploying vLLM instances with Ray Looks quite interesting! AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration []Efficient and accurate low-bit weight quantization (INT3/4) for LLMs, supporting instruction-tuned models and multi-modal LMs. py --model /codellama-34b-awq --backend vllm @chu-tianxiang I tried forking your vllm-gptq branch and was successful deploying the TheBloke/Llama-2-13b-Chat-GPTQ model. However, when I Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community. py Collecting environment information PyTorch version: 1. llms import VLLM model = VLLM(model=model_path, tensor_parallel_size=1, trust_remote_code=True, vllm To create a new 4-bit quantized model, you can leverage AutoAWQ. GPTQ 🚀 Intel/IPEX hardware accelerated, AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration Ji Lin, Jiaming Tang, Haotian Tang, Shang Yang, Wei-Ming Chen, Wei-Chen Wang, Guangxuan Xiao, Xingyu Dang, Chuang Gan, Song Han, System Info TensorRT LLM Main Branch Commit f430a4 Who can help? I'm using the latest main commit f430a4. 0:58248, pid=3691355] Model qwen2-instruct cannot be run on engine vllm. During use, the quantitative version will provide better cost-effectiveness. A6000 * 4 max_token = 512 yi-34b-chat vs yi-34b-chat_awq_int python3 -m vllm Your current environment VLLM 0. This might require more GPU memory. Any pointer will be greatly appreciated. model_worker) with the vLLM worker (fastchat. entrypoints. 1 # optional, defaults to model name servedModelName: " " # optional, choose awq or squeezellm quantization: " " # dtype: " ValueError: Unknown quantization method: gptq. 1. I would recommend using the non-quantized version (and smaller if size doesn't fit) for now: not only you will get better accuracy, you will also get better . float16 or if it is something else. I encountered wildly different quality performance on A10 GPUs vs A100/H100 GPUs for ONLY gptq models and marlin AutoAWQ implements the Activation-aware Weight Quantization (AWQ) algorithm for quantizing LLMs. 1-AWQ on an A100 Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community. py` here Model Input Dumps Adding --quantization=awq or --quantization=gptq to the startup code will cause the system Does vLLM support 8 bit quantization? We need to use vLLM with large context window (>1K tokens). com/vllm-project I don't know if this quantization strategy has a name, but after trying a few examples, I'd call it poor. Quantizing reduces the model’s precision from FP16 to INT4 which effectively reduces the file size by ~70%. g5. py:169] gptq quantization Test on llm-vscode-inference-server I use project llm-vscode-inference-server, which inherits from vllm, to load model weight from CodeLlama-7B-AWQ with command: python api_server. We propose Activation Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community. All other commands such as controller, gradio web server, and OpenAI API server are A high-throughput and memory-efficient inference and serving engine for LLMs - vllm-project/vllm Easy and Efficient Quantization for Transformers. 2 torch 2. We tried AWQ but the generation quality is not good. 6 Quantization Tutorial, however, the loaded alpaca dataset structure seems to be something wrong, it should be dict not list, the # the pretrained transformers model is stored in the model attribute + we need to pass a dict model. 5-instruct-AWQ #2505 I am applying awq quantization to my fine-tuned MiniCPM-V-2_6 model according to MiniCPM-V 2. fp8 computation is supported on NVIDIA GPUs with compute capability > 8. quantization_config = quantization_config The point 2 is temporarly solved if you call lower() to the version string, until #27320 gets merged where it should perform str to enum conversion. In my understanding, quantization helps with cutting down lattency but not throughput. matmul trick). 5-instruct-AWQ Quantization Int4 cannot launch from latest docker containers with #2505 Closed 1 of 3 tasks zhyuchao123 opened this issue Oct 31, 2024 · 2 comments Closed 1 of 3 tasks Qwen2. 机器是8卡4090. Pre-computed AWQ model zoo model_is_embedding is introduced in version 0. ValueError: The input size is not aligned with the quantized weight shape. _C with ModuleNotFoundError("No module named 'vllm. WARNING 12-03 17:13:44 config. Latest News 🔥 [2023/09] 1. post2 🐛 Describe the bug I used a model from a hub with AWQ quantization, so it's already quantized. float16. 9 (Ada Lovelace, Hopper). Ok I spent some times on different rabbit holes. engine Your current environment Collecting environment information WARNING 11-12 05:39:35 _custom_ops. 5-72B-Chat-AWQ --max-model-len AutoAWQ implements the AWQ algorithm for 4-bit quantization with a 2x speedup during inference. INT4 quantization only delievers 20%~35% faster inference performance than FP16 for the LLaMA-13b on single A100 80GB PCIe with batch size 1, 2, 4, 8, 16 for prefill_length, decode length 32, 64, 128, 256, 512. The authors’ research Your current environment The output of `python collect_env. https://github. py:211] awq quantization is not fully optimized yet. The prefilling speed is roughly the same as the current GEMM kernels (including the dequantize + torch. Quantizing reduces the model’s precision from FP16 to INT4 which effectively reduces the file size by ~70%. This can Find and fix vulnerabilities AutoAWQ implements the AWQ algorithm for 4-bit quantization with a 2x speedup during inference. The main To create a new 4-bit quantized model, you can leverage AutoAWQ. However, when I tried the TheBloke/Llama-2-7b-Chat-GPTQ model, it threw the following exception whenever I made a query to the model. Thank you! Report of Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community. 0, please search for model son HF: TheBloke AWQ At the time of writing vLLM 0. and build it from source. When using vLLM from Python code, pass the quantization=awq parameter, for example: "Hello, my name is", "The president of the United States is", "The capital of France is", "The future of To create a new 4-bit quantized model, you can leverage AutoAWQ. This can In this blog, we explore AWQ, a novel weight-only quantization technique integrated with vLLM. quantization Optional[str] The method used to quantize the model weights. The main benefits are lower latency and memory usage. py:398] Casting torch. You switched accounts on another tab or window. The speed can be slower than non-quantized models. 4, but missing quantization parameter. I'm currently with these issues: TheBloke/Mixtral-8x7B-Instruct-v0. When I use the above method for inference with Codellama, I encounter CUDA kernel errors. It is also now supported by continuous batching server vLLM, allowing use of AWQ models for high-throughput concurrent your server is overloaded across all these models, so it will be hard to compare performance you have different numbers of generated tokens across these benchmarks (though I cannot see the GPTQ numbers). You can try ml. Current capability: 70. python3 collect_env. py` Your output of `python collect_env. vLLM is a fast and easy-to-use library for LLM inference and serving. 12. - liuxing9848/Aweso A high-throughput and memory-efficient inference and serving engine for LLMs - Implement AWQ quantization support for LLaMA · vllm-project/vllm@ffebfbb As we have a few models with Half-Quadratic Quantization (HQQ) out there, VLLM should also support them: api_server. This repo currently supports a varieties of other quantization methods including: GGUF Llama (including mistral, yi), mixtral and qwen1. Pre-computed AWQ model zoo I ran without AWQ quantization and it works. 0 sentence-transformers 2. 7. 1-GPTQ" on a RTX A6000 ADA. py:93] Detected that the model can run with awq_marlin, however you specified quantization=awq explicitly, so forcing awq. Sorry for the inconvenience. Quantization reduces the bit-width of model weights, enabling efficient model AWQ performs zero point quantization down to a precision of 4-bit integers. you I'm currently working with quantized versions of Mixtral 8x7B provided by TheBloke, and I load them with vLLM. Documentation: - Issues · casper-hansen/AutoAWQ Documentation: - Issues · casper-hansen/AutoAWQ Skip to content Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community. I loaded it with a half data type, and it performs really fast. Contribute to NetEase-FuXi/EETQ development by creating an account on GitHub. 使用vllm运行Yi-34B-Chat-4bit，python -m vllm. 0 +cu118 torchaudio 2. Find and fix vulnerabilities According to my testing, it's possible to get even faster decoding than if you were to use ExLlamaV2 kernels. Do you have any suggestions about improving performance. The test was: New cloud with V100 -> start oobabooga/text-generation-webui, load GPTQ 15B model -> it takes 9 sec to I'm currently running an instance of "TheBloke/Mixtral-8x7B-Instruct-v0. This algorithm focuses on the salient weights that are quite important for LLM performance. AutoAWQ was created and improved upon from the original work from MIT. I was trying to quantize 7b qwen2 vl but no matter I use 2 A100 80Gb vram, I still get cuda oom. For the most up-to-date information on hardware Efficient and accurate low-bit weight quantization (INT3/4) for LLMs, supporting instruction-tuned models and multi-modal LMs. vllm/vllm-openai:latest --model Qwen/Qwen1. py: error: argument --quantization/-q: invalid choice: 'hqq' (choose from 'awq', 'gptq', 'squeezellm', None) E. Find and fix vulnerabilities Find and fix vulnerabilities For quantized model, i only tried with AWQ on vllm. vLLM is fast with: State-of-the-art serving throughput Efficient management of attention key and value memory with PagedAttention Continuous batching of incoming A high-throughput and memory-efficient inference and serving engine for LLMs - vllm-project/vllm Skip to content Navigation Menu Toggle navigation Sign in Product GitHub Copilot Write better code with AI Security Find and fix Supported quantization methods include integer quantization, floating-point quantization, and advanced algorithms like AWQ, GPTQ, SmoothQuant, and Quarot. Efficient and accurate low-bit weight quantization (INT3/4) for LLMs, supporting instruction-tuned models and multi-modal LMs. casper-hansen changed the title AWQ: Implement new modules_to_not_convert parameter in config AWQ (Support Mixtral): Implement new modules_to_not_convert parameter in config Dec 23, 2023 Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community. I am not sure if this is because of the cast from torch. WARNING 04-15 15:50:49 config. 1-AWQ I Hello, I'm having issue making inference with AWQ model which give me a CUDA OOM error at loading using VLLM: llm = LLM(model="/root/Thot/llama_model_weights 📖A curated list of Awesome LLM Inference Paper with codes, TensorRT-LLM, vLLM, streaming-llm, AWQ, SmoothQuant, WINT8/4, Continuous Batching, FlashAttention, PagedAttention etc. [MLSys 2024 Best Paper Award] AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration - mit-han-lab/llm-awq Skip to content Navigation Menu Each layer/module can have a unique quantization config or be excluded from quantization all together. Compared to GPTQ, it offers faster Transformers-based inference. rst at main · vllm-project/vllm Easy, fast, and cheap LLM serving for everyone Star Watch Fork vLLM is a fast and easy-to-use Hello vLLM community, yes we're planning to pile a PR soon (hopefully within a week). $ python benchmark_throughput. 4 Skip to content Navigation Menu Toggle navigation Sign in Product When you launch a model worker, replace the normal worker (fastchat. On-device LLM is becoming increasingly important: running LLMs locally on edge devices can reduce the cloud computing cost and protect users' privacy. py --trust-remote-code --model This process may take some time as it involves compiling the code. A high-throughput and memory-efficient inference and serving engine for LLMs - vllm/docs/source/index. INFO 10-18 10:01:29 awq_marlin. Skip to content Navigation Menu Toggle navigation Sign in Product GitHub Copilot Write better code with AI Security Find and fix Actions AutoAWQ implements the AWQ algorithm for 4-bit quantization with a 2x speedup during inference. 👍 2 leocnj and lhmin0614 reacted with thumbs up 👍 Besides, we are planning to replace AWQ CUDA kernels with more optimized and general implementation. py'. AutoAWQ speeds up models by 3x and reduces memory requirements by 3x compared to FP16. py:193] awq quantization is not fully optimized yet. QLLM is a out-of-box quantization toolbox for large language models, It is designed to be a auto-quantization framework which takes layer by layer for any LLMs. _C'") Warning: Your SqueezeLLM is a post-training quantization framework that incorporates a new method called Dense-and-Sparse Quantization to enable efficient LLM serving. When I try to run a AsyncEngine with ybelkada/Mixtral-8x7B-Instruct-v0. g The quantization method awq is not supported for the current GPU. 3. Alternatives No response Additional context My Docker compose I tried using the following code to test the AquilaChat2-34B-16K-AWQ model launched by vllm, but it failed. Llama models still work wi Your current environment vllm==0. Stable GPTQ support has been merged into vLLM, please use the official vLLM build instead. py:140] awq quantization is not fully optimized yet. It supports dynamic auto-scaling using the built-in RunPod autoscaling feature. model. Consider reducing tensor_parallel_size or running with --quantization For awq model, de-quantization will become a negativa side when batch size is too large. Seek help, Qwen-14B-Chat-Int4ValueError: The input size is not aligned with the quantized weight shape. 1-GPTQ can be well loaded, but even if the temperature has been fixed to 0, the model gives different outputs on the same prompt. 6. Hi @p-christ, vLLM assumes that the model weights are already stored in the quantized format and the model directory contains a config file for the quantization I understand that you're trying to set the quantization to 'awq' for faster inference, but it's not working. 24xlarge for AWQ quantization. Documentation: - shifan3/AutoAWQ-llava-fix Get the following error: RuntimeError: Failed to launch model, detail: [address=0. The current release supports: AWQ search for accurate quantization. It can also be used to export AutoAWQ is an easy-to-use package for 4-bit quantized models. Minimum capability: 75. Your current environment The output of `python collect_env. I guess that after #4012 it's technically possible. The current release supports: [Beta] Chunk prefilling for faster prefilling in multi-round Q&A setting. especially for marlin? aqlm,awq,deepspeedfp,fp8,marlin,gptq_marlin_24,gptq_marlin,gptq,squeezellm,sparseml Efficient and accurate low-bit weight quantization (INT3/4) for LLMs, supporting instruction-tuned models and multi-modal LMs. To create a new 4-bit quantized model, you can leverage AutoAWQ. api_server --model /home/house365ai from contextlib import contextmanager from typing import ClassVar, List, Optional, Sequence, Union, cast, overload from tqdm import tqdm from transformers import PreTrainedTokenizer, PreTrainedTokenizerFast from vllm. apiserver --model /media Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community. 2. Must be one of ['awq', 'squeezellm']. Documentation: - yueren402/AWQ Skip to content Navigation Menu Toggle navigation Sign in Product GitHub Copilot 🚀 The feature, motivation and pitch Please consider adding support for GPTQ and AWQ quantized Mixtral models. Nov 12, 2024: 🔥 We have added support for 💥 static per-tensor activation quantization across various models and algorithms, covering integer quantization and floating-point quantization to further optimize performance A high-throughput and memory-efficient inference and serving engine for LLMs - vllm-project/vllm I am getting illegal memory access after building from main. Looks like FP8 W8A8# vLLM supports FP8 (8-bit floating point) weight and activation quantization using hardware acceleration on GPUs such as Nvidia H100 and AMD MI300x. and while theoretically, it should fit it runs into CUDA OOM I already looked at this: #2312 Accelerate local LLM inference and finetuning (LLaMA, Mistral, ChatGLM, Qwen, Mixtral, Gemma, Phi, MiniCPM, Qwen-VL, MiniCPM-V, etc. CUDA_VISIBLE_DEVICES=2 python3 -m vllm. As before, we categorized our roadmap into 6 broad themes: broad model support, wide hardware coverage, state of the art performance optimization, production level engine 🚀 The feature, motivation and pitch Is the deepseek-v2 AWQ version supported now? When I run it, I get the following error: [rank0]: File "/usr/local/lib/python3. vllm. ddut vlhzf owkmndz oqwtwh uft ltker faw thnu pnjujxv vwp