Awq quantization vllm github We tried AWQ but the generation quality is not good. 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. py:398] Casting torch. 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. Any pointer will be greatly appreciated. Current capability: 70. 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. 4, but missing quantization parameter. 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. 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. Contribute to NetEase-FuXi/EETQ development by creating an account on GitHub. vllm/vllm-openai:latest --model Qwen/Qwen1. 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 . The authors’ research Your current environment The output of `python collect_env. Llama models still work wi Your current environment vllm==0. 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. Compared to GPTQ, it offers faster Transformers-based inference. 1 # optional, defaults to model name servedModelName: " " # optional, choose awq or squeezellm quantization: " " # dtype: " ValueError: Unknown quantization method: gptq. Sorry for the inconvenience. Quantizing reduces the model’s precision from FP16 to INT4 which effectively reduces the file size by ~70%. INFO 10-18 10:01:29 awq_marlin. 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. bfloat16 to torch. This repo currently supports a varieties of other quantization methods including: GGUF Llama (including mistral, yi), mixtral and qwen1. 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. Documentation: - ohso4/AutoAWQ-torch-2. Hi vLLM team! I was testing vLLM 0. WARNING 12-03 17:13:44 config. g. @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. 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. 9 (Ada Lovelace, Hopper). The main benefits are lower latency and memory usage. vllm_worker). It can also be used to export AutoAWQ is an easy-to-use package for 4-bit quantized models. This might require more GPU memory. When I try to run a AsyncEngine with ybelkada/Mixtral-8x7B-Instruct-v0. 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. 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. 这是vllm不支持gptq量化的模型吗 Dependencies ‘’‘ peft 0. The speed can be slower than non-quantized models. 5. 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. 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. 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. py: error: argument --quantization/-q: invalid choice: 'hqq' (choose from 'awq', 'gptq', 'squeezellm', None) E. py:19] Failed to import from vllm. You signed out in another tab or window. 5-instruct-AWQ #2505 I am applying awq quantization to my fine-tuned MiniCPM-V-2_6 model according to MiniCPM-V 2. 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. I am not sure if this is because of the cast from torch. When I use the above method for inference with Codellama, I encounter CUDA kernel errors. I'm currently with these issues: TheBloke/Mixtral-8x7B-Instruct-v0. 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. 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. 0:58248, pid=3691355] Model qwen2-instruct cannot be run on engine vllm. 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. CUDA_VISIBLE_DEVICES=2 python3 -m vllm. INFO 03-14 01:20:43 llm_engine. 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. 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. Currently, we support "awq", "gptq" and "squeezellm". 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). The main To create a new 4-bit quantized model, you can leverage AutoAWQ. 1-GPTQ" on a RTX A6000 ADA. 2. 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. 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. Collecting environment information PyTorch version: 2. 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). The current release supports: AWQ search for accurate quantization. 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. 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. - 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. 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. Please help me understand why? @TheBloke WARNING: WatchFiles detected changes in 'fastapi_vllm_codellama. Latest News 🔥 [2023/09] 1. Pre-computed AWQ model zoo model_is_embedding is introduced in version 0. Pre-computed AWQ model zoo You signed in with another tab or window. 0 +cu118 torchaudio 2. 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). On-device LLM is becoming increasingly important: running LLMs locally on edge devices can reduce the cloud computing cost and protect users' privacy. 4-bit AWQ (A4W16) quantization has already been implemented in vLLM 0. 👍 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. g5. 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. _C with ModuleNotFoundError("No module named 'vllm. 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. ) 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. 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. 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. float16. 0 has not been not released yet, so please clone the main and build it from source. g The quantization method awq is not supported for the current GPU. 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. 5-72B-Chat-AWQ --max-model-len AutoAWQ implements the AWQ algorithm for 4-bit quantization with a 2x speedup during inference. ValueError: The input size is not aligned with the quantized weight shape. Quantizing reduces the model’s precision from FP16 to INT4 which effectively reduces the file size by ~70%. WARNING 04-15 15:50:49 config. During use, the quantitative version will provide better cost-effectiveness. 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. 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. In my understanding, quantization helps with cutting down lattency but not throughput. Reload to refresh your session. 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. 使用vllm运行Yi-34B-Chat-4bit，python -m vllm. ai Themes. Minimum capability: 75. Alternatives As the title su Hi @ryanshrott, If you are using VLLM via LangChain, so, the correct code is as follows. py:93] Detected that the model can run with awq_marlin, however you specified quantization=awq explicitly, so forcing awq. 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. Pre-computed AWQ model zoo I ran without AWQ quantization and it works. 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. Currently we have a very hacky version of vLLM integration - mostly because of the pre-fused layers such as qkv and up_proj. 6. 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. 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. 1. 1+cu113 Is AutoAWQ implements the Activation-aware Weight Quantization (AWQ) algorithm for quantizing LLMs. ) on Intel XPU (e. Documentation: - shifan3/AutoAWQ-llava-fix Get the following error: RuntimeError: Failed to launch model, detail: [address=0. 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. py'. If None, we first check the quantization_config attribute in the model config file. The prefilling speed is roughly the same as the current GEMM kernels (including the dequantize + torch. Find and fix vulnerabilities Find and fix vulnerabilities For quantized model, i only tried with AWQ on 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. ’‘’ 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. 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. 6x-2. The end conclusion is as following, you are seeing undesirable performance because vLLM's under-optimized support for AWQ models at the moment. You switched accounts on another tab or window. 12. 机器是8卡4090. 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. python3 collect_env. A6000 * 4 max_token = 512 yi-34b-chat vs yi-34b-chat_awq_int python3 -m vllm Your current environment VLLM 0. You can try ml. 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. AutoAWQ speeds up models by 3x and reduces memory requirements by 3x compared to FP16. py` Your output of `python collect_env. 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. 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. The current release supports: [Beta] Chunk prefilling for faster prefilling in multi-round Q&A setting. Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community. Quantization reduces the bit-width of model weights, enabling efficient model AWQ performs zero point quantization down to a precision of 4-bit integers. AutoAWQ was created and improved upon from the original work from MIT. 0 sentence-transformers 2. However, the astronomical model size and the limited hardware resource pose significant deployment challenges. 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 Collecting environment information PyTorch version: 1. float16 or if it is something else. Stable GPTQ support has been merged into vLLM, please use the official vLLM build instead. Find and fix vulnerabilities According to my testing, it's possible to get even faster decoding than if you were to use ExLlamaV2 kernels. 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. 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. [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. 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. ValueError: The input size is not aligned with the quantized weight shape. 👍 2 leocnj and lhmin0614 reacted with thumbs up 👍 Besides, we are planning to replace AWQ CUDA kernels with more optimized and general implementation. 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. This can In this blog, we explore AWQ, a novel weight-only quantization technique integrated with vLLM. Latest News 🔥 [2023/12] Mixtral, LLaVa, QWen Did some additional tests, seems that running models through vllm somehow messes up my GPU. _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. 用vllm部署32b-chat没问题，就是慢，部署32bAWQ-chat后 Qwen2. and build it from source. Efficient and accurate low-bit weight quantization (INT3/4) for LLMs, supporting instruction-tuned models and multi-modal LMs. py:193] awq quantization is not fully optimized yet. 5 are 🚀 The feature, motivation and pitch As the title suggests Currently, VLLM supports MOE, but does not support quantitative versions. entrypoints. 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. 7 for serving Mixtral-8x7B-Instruct-v0. Seek help, Qwen-14B-Chat-Int4ValueError: The input size is not aligned with the quantized weight shape. 7. $ python benchmark_throughput. Ok I spent some times on different rabbit holes. quantization Optional[str] The method used to quantize the model weights. It supports dynamic auto-scaling using the built-in RunPod autoscaling feature. 🚀 vLLM and SGLang inference integration for quantized model where format = FORMAT. , local PC WARNING 03-14 01:20:43 config. After quantizing a llama3-70B model, I'm using lora weights with the --lora-plugin parameter set. py:211] awq quantization is not fully optimized yet. This can Find and fix vulnerabilities AutoAWQ implements the AWQ algorithm for 4-bit quantization with a 2x speedup during inference. I guess that after #4012 it's technically possible. AutoAWQ implements the AWQ algorithm for 4-bit quantization with a 2x speedup during inference. 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. 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. post2 🐛 Describe the bug I used a model from a hub with AWQ quantization, so it's already quantized. 0, please search for model son HF: TheBloke AWQ At the time of writing vLLM 0. 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. py:140] awq quantization is not fully optimized yet. 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. To create a new 4-bit quantized model, you can leverage AutoAWQ. 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. A high-throughput and memory-efficient inference and serving engine for LLMs - vllm/docs/source/index. More information on AWQ here. from langchain. 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. 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. you I'm currently working with quantized versions of Mixtral 8x7B provided by TheBloke, and I load them with vLLM. engine Your current environment Collecting environment information WARNING 11-12 05:39:35 _custom_ops. model_worker) with the vLLM worker (fastchat. https://github. 🚀 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. TLDR: Deploying LLMs is difficult due to their large memory size. Do you have any suggestions about improving performance. fp8 computation is supported on NVIDIA GPUs with compute capability > 8. Must be one of ['awq', 'squeezellm']. 24xlarge for AWQ quantization. config. 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. vllm. I loaded it with a half data type, and it performs really fast. 3. Additionally, please note that these commands are just for Jetson Orin GPUs, whose CUDA compute capability is 87. I was trying to quantize 7b qwen2 vl but no matter I use 2 A100 80Gb vram, I still get cuda oom. You can also specify other bit rates like 3-bit, but some of these options may lack kernels for running inference. 2 torch 2. serve. 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. 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. To use GPTQ models you need to install the In this blog, we explore AWQ, a novel weight-only quantization technique integrated with vLLM. 4 Skip to content Navigation Menu Toggle navigation Sign in Product When you launch a model worker, replace the normal worker (fastchat. 0. Try launching with awq instead xinference launch --model-engine To create a new 4-bit quantized model, you can leverage AutoAWQ. matmul trick). This algorithm focuses on the salient weights that are quite important for LLM performance. 🐛 Describe the bug Hello, I am running llama3-70b and mixtral with VLLM on a bunch of different kinds of machines. 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. py --trust-remote-code --model This process may take some time as it involves compiling the code. Activation-aware weight quantization (AWQ) [2] has recently been one of the most popular quantization algorithms. 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. 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. 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. 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. model. 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. post1 Model Input Dumps ValueError: Weight input_size_per_partition = 10944 is not divisible by min_thread_k = 128. 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. 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. vLLM is a fast and easy-to-use library for LLM inference and serving. Your current environment The output of `python collect_env. tad gregp yidzwz miee xoifx xawyud dcj vkq dzfxyh jzgsmd