Llama 13b quantized github This is a fork of the LLaMA code that runs LLaMA-13B comfortably within 24 GiB of RAM. Support for multiple LLMs (currently LLAMA, BLOOM, OPT) at various model sizes (up to 170B) Support for a wide range of consumer-grade Nvidia GPUs Tiny and easy-to-use codebase mostly in Python (<500 LOC) Underneath the hood, MiniLLM uses the the GPTQ algorithm for up to 3-bit compression and large Apr 2, 2023 · I can run normal LLaMA 13B 4-bit on 10GB VRAM / 32GB CPU RAM. A set of out-of-the-box arbitrary bit quantization operators that support arbitrary bit model inference in Turing and above architectures. For example, quantizing a LLaMa-13b model requires 32gb, and LLaMa-33b requires more memory than 64gb. Apr 8, 2023 · Seems to happen with different models (Tested with llama-30b-4bit-128g, llama-13b-4bit-128g and Alpaca-30b-4bit-128g). I am here achieved tok/s: 5. 15: 28. cpp is to enable LLM inference with minimal setup and state-of-the-art performance on a wide range of hardware - locally and in the cloud. raw Result Sep 30, 2023 · Hello guys, I was able to load my fine-tuned version of mistral-7b-v0. int8() work of Tim Dettmers. 4x higher throughput compared to the leading industry solution, TensorRT-LLM, for Llama-3-8B, and a 2. These models are intended for purposes in line with the LLaMA license and require access to the LLaMA models. Links to other models can be found in the index at the bottom. In chat mode it gives a couple of normal answers until then starts spewing some random info (sometimes in polish or french, weirdly) Jan 15, 2024 · Hongbosherlock changed the title AWQ-int4-quantization errors on Llama-2 13B with AMMO AWQ-int4-quantization errors on Llama-2 13B based model with AMMO Jan 15, 2024 Copy link Author Apr 9, 2023 · Navigation Menu Toggle navigation. Run the quantized model: Llama 2 13B. - ranchlai/quantizations Pre-trained ABQ-LLM model weights for LLM (LLaMA and LLaMA-2 loaded to run quantized models). model Apr 17, 2023 · I've been able to convert files from HF format to f16 and 4bit, but I've not been able to figure out what config. bin Quantized inference code for LLaMA models. 14GB: LLaMA Original model card: Meta's Llama 2 13B Llama 2. An 8-8-8 30B quantized model outperforms a 13B model of similar size, and should have lower latency and higher throughput in practice. 14GB: LLaMA First, 8-bit quantization should be preferred over smaller full precision models, and PTQ methods are sufficient for this case. js API to directly run dalai locally To support the research community, we have open-sourced DeepSeek-R1-Zero, DeepSeek-R1, and six dense models distilled from DeepSeek-R1 based on Llama and Qwen. model size = 13B llama_model Navigation Menu Toggle navigation. Jul 19, 2023 · Similar to #79, but for Llama 2. 37 GB of RAM and accordingly should work on computers with 12GB of RAM or more available. 🚀 LoftQ finds good enough quantized LoRA initialization: quantized backbone Q and LoRA adapters A and B, given a pre-trained weight W. Plain C/C++ implementation without any dependencies Apr 13, 2025 · Request access to one of the llama2 model repositories from Meta's HuggingFace organization, for example the Llama-2-13b-chat-hf. chokoon123 changed the title GGML to GGUF Quantized tensor bytes per row (5120) is not a multiple of Q2_K type size (84) GGML to GGUF FAIL Quantized tensor bytes per row (5120) is not a multiple of Q2_K type size (84) Feb 21, 2025 Quantized inference code for LLaMA models. cpp is not just for Llama models, for lot more, I'm not sure but hoping would work for Bitnets too. To promote open research of large models in the Chinese NLP community, this project has open-sourced the Chinese LLaMA model and the Alpaca large model with instruction fine-tuning. Before you do any of this, you will need a bot token. Contribute to mengjiexu/llama-int8 development by creating an account on GitHub. AND. I'm just so exited about Bitnets that I wanted to give heads up here. act. py quantized llama model with reference to opt. bin Jul 13, 2023 · You signed in with another tab or window. The code as follow: shown as follow: from vllm import LLM, SamplingParams from huggingface_hub import login. Alpaca comes fully quantized (compressed), and the only space you need for the 13B model is 8. A repo for creating a fine-tuned quantized LORA of the 13B paramater llama2 chat model. To run LLaMA 2 weights, Open LLaMA weights, or Vicuna weights (among other LLaMA-like checkpoints), check out the Lit-GPT repository. py minigpt4-13B-f16. Automate any workflow Packages The main goal of llama. [24/04/21] We supported Mixture-of-Depths according to AstraMindAI's implementation. Disk Space Requirements Alpaca. Also 3-bit 13B GPTQ will perform better than 7B at FP16. Currently 7B and 13B models are available via alpaca. js API to directly run dalai locally This release includes 7B and 13B versions for both Base and Chat models, along with a 4bits quantized version for the Chat model. Does this model also support using the —pre_layer flag? By only running 12-16 layers on GPU, I can even run the LLaMA 30B 4-bit, just very slowly 4 bits quantization of LLaMA using GPTQ. This is a fork that adds support for ROCm's HIP to use in AMD GPUs, only supported on linux. 5-streaming-api Aug 13, 2023 · I was able to replicate this issue. Deploying the quantized LLAMA 2–13b language model as an API using FastAPI - peterbull/fastapi-hermes-2. Use this discussion to Coordinate. 5G, 7. bin with your respective models cd minigpt4 python minigpt4_library. You should only use this repository if you have been granted access to the model by filling out this form but either lost your copy of the weights or got some trouble converting them to the Transformers format. 2x-1. Quantized inference code for LLaMA models. This app includes three models, LLaMa-2-7B-Chat-Omniquant-W3A16g128asym, LLaMa-2-13B-Chat-Omniquant-W3A16g128asym, and LLaMa-2-13B-Chat-Omniquant-W2A16g128asym. This model is under a non-commercial license (see the LICENSE file). Jul 31, 2023 · from transformers import AutoTokenizer, TextGenerationPipeline: from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig: import logging: logging. One of the main challenges in quantizing LLMs with frameworks such as GPTQ is the different ranges between the channels, which affects the accuracy and compression ratio of the quantized model. When I tried using v0. json (or what changes to the config. 0. Contribute to Gary3410/llama-int8 development by creating an account on GitHub. 4x-3. This contains the weights for the LLaMA-13b model. js API to directly run dalai locally The most intelligent, scalable, and convenient generation of Llama is here: natively multimodal, mixture-of-experts models, advanced reasoning, and industry-leading context windows. 5 7B and 13B I found Bakllava to be very weak in following the actual prompt, especially trying to make it respond long or short is ignored no matter how I tried it. Disclaimer - these were observed on a small subset of WikiText and Penn TreeBank (following Apr 20, 2024 · LoftQ helps you fine-tune LLMs with limited GPUs. Hence, the ownership of bind-mounted directories (/data/model and /data/exllama_sessions in the default docker-compose. Mostly Default . You can use the OPT demo (examples/smoothquant_opt_demo. al which is a polar sentiment dataset consisting of 4,840 sentences from English language financial news. sh). Navigation Menu Toggle navigation. May 18, 2023 · Not Compatible with Models quantized with updated llama. 0 -s 25 -p " Hello to all the cool people out there who " Hello to all the cool people out there who are reading this. gguf here. py llama-13b c4 --wbits 8 --true-sequential --groupsize 128 --save_safetensors llama-13b-8bit-128g. Two Llama-3-derived models fine-tuned using LLaMA Factory are available at Hugging Face, check Llama3-8B-Chinese-Chat and Llama3-Chinese for details. Apr 1, 2023 · model name: gpt4-x-alpaca-13b-ggml-q4_1-from-gptq-4bit-128g the model was described as: LLaMA 13B, finetuned natively with alpaca dataset, then finetuned on GPT4 responses (GPT4-x), then GPTQ 4b-128g quantized, then converted to ggml q4_1 format it loads, but takes about 30 seconds per token This is a fork of the LLaMA code that runs LLaMA-13B comfortably within 24 GiB of RAM. chokoon123 changed the title GGML to GGUF Quantized tensor bytes per row (5120) is not a multiple of Q2_K type size (84) GGML to GGUF FAIL Quantized tensor bytes per row (5120) is not a multiple of Q2_K type size (84) Feb 21, 2025 Jul 23, 2023 · A comparison between k-quants perplexities for the 13B LLaMA-1 and LLaMA-2 models is shown in Figure 5. These are the models published on HuggingFace by decapoda-research. 4 GB, while a 2-BIT QuIP model on You signed in with another tab or window. This is a fork of the LLaMA code that runs LLaMA-13B comfortably within 24 GiB of RAM. Jul 18, 2023 · We release the resources associated with QLoRA finetuning in this repository under MIT license. . Contribute to Jaid/llama-cpp development by creating an account on GitHub. ; Thanks for your rely. I've tested it on an RTX 4090, and it reportedly works on the 3090 . Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 23: 28. To get access permissions to the Llama 2 model, please fill out the Llama 2 ONNX sign up page. DeepSeek-R1-Distill-Qwen-32B outperforms OpenAI-o1-mini across various benchmarks, achieving new state-of-the-art results for dense models. Mar 11, 2023 · However, in other cases it's better (only tested upto 13B models). [NeurIPS'23 Oral] Visual Instruction Tuning (LLaVA) built towards GPT-4V level capabilities and beyond. 446; smooth quant accuracy (w/o quantized MLP): 0. ai/mlc-chat-Llama-2-13b-chat-hf-q4f16 cpp # run quantized Llama-2-7B models This is a fork of the LLaMA code that runs LLaMA-13B comfortably within 24 GiB of RAM. Set n_ctx as you want. Contribute to Ak4ft7/llama-int8 development by creating an account on GitHub. NOTE: by default, the service inside the docker container is run by a non-root user. This allows you to load the largest model on your GPU with the smallest amount of quality loss. Note that increasing this parameter increases quality at the cost of performance (tokens per second) and VRAM. /perplexity settings with all of wiki. LLaMA Server combines the power of LLaMA C++ (via PyLLaMACpp) with the beauty of Chatbot UI. ipynb) and Llama demo (examples/smoothquant_llama_demo. cpp q4 and q5 quantization released in llama. This is the 13B fine-tuned GPTQ quantized model, optimized for dialogue use cases. Presently this is Linux only, but you might be able to make it work with other OSs. json) to use when attempting to evaluate 4 bit quantized models. 🦙LLaMA C++ (via 🐍PyLLaMACpp) 🤖Chatbot UI 🔗LLaMA Server 🟰 😊. TARGET_MODEL_NAME correspond to various flavors of Llama models (7B to 30B), with or without quantization. 31 ms / 227. Sign in Product May 23, 2023 · Use a 5_1 quantized model. Contribute to jorahn/llama-int8 development by creating an account on GitHub. It relies almost entirely on the bitsandbytes and LLM. 21GB: 13B. 50: 46. Contribute to jacob1264/llama-int8 development by creating an account on GitHub. 13B, url : only needed if connecting to a remote dalai server if unspecified, it uses the node. Alpaca comes fully quantized (compressed), and the only space you need for the 7B model is 4. 7B, llama. This code is based on GPTQ. This is huge, because using transformers with autoawq uses 7Gb of my GPU, does someone knows how to reduce it? Navigation Menu Toggle navigation. Dec 7, 2023 · Oobabooga implemented this into the webui and certainly in terms of memory, it seems a lot better than current Q2K, by a landslide. test. Post your hardware setup and what model you managed to run on it. Figure 1 Perplexity as a function of context size for the LLaMA-1 (black) and LLaMA-2 (red) 7B models. This code is based on the paper Reorder-Based Post-Training Quantization for Large Language Models, where Mar 13, 2023 · Saved searches Use saved searches to filter your results more quickly Saved searches Use saved searches to filter your results more quickly This is a fork of the LLaMA code that runs LLaMA-13B comfortably within 24 GiB of RAM. Possible values are 7B, 13B, 30B, 7B_8bit, 13B_8bit, 30B, 30B_8bit, 65B, 65B_8bt. We see LLaMA-2 Q4_K_S perplexity is lower than the fp16 perplexity of LLaMA-1. When serving the large language models Llama-3-8B and Qwen1. You signed out in another tab or window. See examples for usage. Quantizing the model requires a large amount of CPU memory. 0G free RAM, respectively. I will eventually use L40s for w4a8_awq inference. The present study uses FinancialPhraseBank dataset curated by Malo et. Apr 17, 2023 · I've been able to convert files from HF format to f16 and 4bit, but I've not been able to figure out what config. 481; quantized accuracy (w quantized MLP): 0. bin -t 0. This repository contains the necessary This is the 13B fine-tuned GPTQ quantized model, optimized for dialogue use cases. May 20, 2023 · Saved searches Use saved searches to filter your results more quickly Apr 3, 2023 · We introduce Vicuna-13B, an open-source chatbot trained by fine-tuning LLaMA on user-shared conversations collected from ShareGPT. Sign in Product. bin and ggml-vicuna-13B-v0-q5_k. 56 ms / 555. 30: 31. I used the same dataset with axolotl training. This repo implements the paper 🔗: LoftQ: LoRA-Fine-Tuning-Aware Quantization for Large Language Models . 7B. At its core, the graph is only measuring how different each quantization is from the base model on average This is a fork of the LLaMA code that runs LLaMA-13B comfortably within 24 GiB of RAM. 34 ms per token 30b (6 threads): main: predict time = 165125. cpp. This repository contains the necessary GitHub Advanced Security. Reload to refresh your session. With the code below, for prompts w/ a token length ~1300 or less, after running the generate 3 times, it produces a random response. Peiyu Liu, Zikang Liu, Ze-Feng Gao, Dawei Gao, Wayne Xin Zhao, Yaliang Li, Bolin Ding, Ji-Rong Wen. cpp PR 1405. It might also theoretically allow us to run LLaMA-65B on an 80GB A100, but I haven't tried this. The sub-modules that contain the ONNX files in this repository are access controlled. This would have several advantages: Llama 3 8B model performs significantly better on Quantized inference code for LLaMA models. Nov 8, 2023 · Interesting I just played around a bit with Bakllava and compared it to llava 1. Llama-2-Chat models outperform open-source chat models on most benchmarks tested, and in human evaluations for helpfulness and safety, are on par with some popular closed-source models like ChatGPT and PaLM. Quantization Bits per Weight (BPW) Q2_K: 3. ipynb) to test smoothing and quantizing those models. Q4_K_M. We also provide the script to get the activation channel scales for your models. This also holds for an 8-bit 13B model compared with a 16-bit 7B model. If allowable, you will receive GitHub access in the next 48 hours, but usually much sooner. llama-2-13B seems to export fine with the same machine. 0 licensed weights are being released as part of the Open LLaMA project. py script OOMs with llama-2-70B model on 197G machine. Apr 23, 2024 · It would be great if the LLaMa 2 13B AWQ 4bit quantized model currently used would be upgraded to the Llama 3 8B model. 067; Why is it that when I write the llama. 5G, and 6. basicConfig LLaMA: Open and Efficient Foundation Language Models - juncongmoo/pyllama This is a fork of the LLaMA code that runs LLaMA-13B comfortably within 24 GiB of RAM. Mar 11, 2023 · 13b (6 threads): main: predict time = 67519. A Q2_K 13B model needs around 5. Sign in Product Aug 3, 2023 · You signed in with another tab or window. Contribute to mlc-ai/llm-perf-bench development by creating an account on GitHub. Build your greatest ideas and seamlessly deploy in minutes with Llama API and Llama Stack. Contribute to jihoyeo/llama-int8 development by creating an account on GitHub. I've tested it on an RTX 4090, and it reportedly works on the 3090. It can be quantized similarly. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others. A collection of quantization recipes for various large models including Llama-2-70B, QWen-14B, Baichuan-2-13B, and more. yml file) is changed to this non-root user in the container entrypoint (entrypoint. n1-highmem-4 1 x NVIDIA T4 Virtual Workstation. They require at least 4. Jul 23, 2023 · A comparison between k-quants perplexities for the 13B LLaMA-1 and LLaMA-2 models is shown in Figure 5. py, the accuracy of the model I get is 0? Nov 19, 2023 · Expected Behavior I tried to finetune a model using a dataset. Before you begin, ensure 🇨🇳中文 | 🌐English | 📖文档/Docs | 提问/Issues | 💬讨论/Discussions | ⚔️竞技场/Arena. Pre-computed AWQ model zoo for LLMs (Llama-1/2/3, OPT, CodeLlama, StarCoder, Vicuna, VILA, LLaVA; load to generate quantized weights). bin main: seed = 1680773293 llama_model_load: loading model from 'ggml-vicuna-13b-4bit-rev1 Apr 1, 2023 · model name: gpt4-x-alpaca-13b-ggml-q4_1-from-gptq-4bit-128g the model was described as: LLaMA 13B, finetuned natively with alpaca dataset, then finetuned on GPT4 responses (GPT4-x), then GPTQ 4b-128g quantized, then converted to ggml q4_1 format it loads, but takes about 30 seconds per token Mar 17, 2023 · Describe the bug After installing the new transformers webui does not load models changing the tokenizer did not help Is there an existing issue for this? I have searched the existing issues Reproduction python server. > cargo run --release --features 13B,group_128,quantized -- -c l13orca. 5-72B. If you don't have a bot token, follow this guide to make a bot and then add the bot to your server. 5x higher throughput for Qwen1. 1-awq quantized with autoawq on my 24Gb TITAN RTX, and it’s using almost 21Gb of the 24Gb. This is the repository for the 13B pretrained model, converted for the Hugging Face Transformers format. js API to directly run dalai locally Oct 29, 2023 · The question here is on "Hardware specs for GGUF 7B/13B/30B parameter models", likely some already existing models, using GGUF. In addition, we release the Guanaco model family for base LLaMA model sizes of 7B, 13B, 33B, and 65B. - johnh00/llama2-13b-qlora This is a fork of the LLaMA code that runs LLaMA-13B comfortably within 24 GiB of RAM. First Steps. Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This model will require 10. [24/04/22] We provided a Colab notebook for fine-tuning the Llama-3 model on a free T4 GPU. GPTQ is SOTA one-shot weight quantization method. 0! UPDATE: Now supports better streaming through PyLLaMACpp! Apr 6, 2023 · I think I'm missing a conversion step here. bin ggml-vicuna-13B-v0-q5_k. All versions are fully open to academic research, and developers can also use them for free in commercial applications after obtaining an official commercial license through email request . Sign in Product Quantized inference code for LLaMA models. Generate a HuggingFace read-only access token from your user profile settings page. Additionally, new Apache 2. 1 release, we’ve consolidated GitHub repos and added some additional repos as we’ve expanded Llama’s functionality into being an e2e Llama Stack. Apr 23, 2023 · Saved searches Use saved searches to filter your results more quickly You signed in with another tab or window. 34: 13B => ~8 GB; 30B => ~16 GB; 65B => ~32 GB; 3. UPDATE: Greatly simplified implementation thanks to the awesome Pythonic APIs of PyLLaMACpp 2. Preliminary evaluation using GPT-4 as a judge shows Vicuna-13B achieves more than 90%* quality of OpenAI ChatGPT and Google Bard while outperforming other models like LLaMA and Stanford Alpaca in more than 90%* of As part of the Llama 3. In Q4_1 and 13B it can not only reduce RAM (by changing bin size QK from 32 to higher - like 128), but also improve performance. /gpt4all-lora-quantized-linux-x86 -m ggml-vicuna-13b-4bit-rev1. 9 a month ago, I could successfully quantize my model using AMMO and get int4_awq and w4a8_awq engines (group_size = 64) finally. [July 15] We release the code especially for fine-tuning LLaMA-65B within a single A100 GPU. 4 GB, while a 2-BIT QuIP model on Aug 23, 2023 · 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. When I run the 13B model it is very slow I have tried to set mlock as true as well. 22: and quant8_saved_dir is the directory where the 8bits quantized model is saved. Using 65B versions, however, requires providing the weights yourself. 1588936 GitHub Advanced Security Find and fix vulnerabilities Actions The game was primarily tested on a Mac M2 Max with Llama 2 13B quantized at Q4_K_M. You switched accounts on another tab or window. Mar 27, 2023 · Quantized with python llama. Memory-efficient 4-bit Linear in PyTorch. Contribute to jebtang/llama-int8 development by creating an account on GitHub. Contribute to a-leut/llama-int8 development by creating an account on GitHub. raw Result Quantized inference code for LLaMA models. py --auto-devices - Example: alpaca. I hope you are having a great day. Efficient CUDA kernel implementation for fast inference (support context and decoding stage). Example: alpaca. CO 2 emissions during pretraining. Running llama-2-13B models exported with --version 2 and --version 1 core dumps: Sep 30, 2023 · Hello guys, I was able to load my fine-tuned version of mistral-7b-v0. Nov 23, 2023 · Depends on whether or not you consider the base model of 13b objectively superior in every way, which is hard to quantify I'd assume. Mar 23, 2023 · We are currently collecting Perplexity scores for all models + quantization + program flags. LLaMA-13B: 28. 5-72B on L40S and A100 GPUs, QServe demonstrates superior performance, achieving 1. - haotian-liu/LLaVA If you have a bit more RAM to spare try upgrading to Code Llama 13B quantized to 4 bits available as codellama-13b. llama. Sep 24, 2023 · Saved searches Use saved searches to filter your results more quickly Nov 9, 2023 · I just use the example code with meta-llama/Llama-2-13b-hf model in GCP VM of the following specification: n1-standard-16 1 x NVIDIA Tesla P4 Virtual Workstation. For these models make sure the setting locopilot. login("") prompts = Jun 11, 2024 · w4a8_awq only support group_size = 128 at the moment. Test if minigpt4 works by calling the following, replacing minigpt4-13B-f16. May 17, 2023 · Running 4bit quantized models on M1 with 8gb RAM. 98 ms per token My assumption is memory bandwidth, my per core speed should be slower than yours according to benchmarks, but when I run with 6 threads I get faster performance. Please use the following repos going forward: info 9-3-23 Added 4bit LLaMA install instructions for cards as small as 6GB VRAM! (See "BONUS 4" at the bottom of the guide) warning 9-3-23 Added Torrent for HFv2 Model Weights, required for ooga's webUI, Kobold, Tavern and 4bit (+4bit model)! Example: alpaca. TensorRT-LLM will succesfully build Llama13b int8 on cards with 10GB of VRAM, but even quantizing to float16 caused out-of-memory errors on my 3080. Mar 24, 2023 · Saved searches Use saved searches to filter your results more quickly this repo uses int8 quantized Llama 13b, as it's the largest model that i could build on a 3080 while maintaining high token/s during inference. Oct 25, 2023 · #Code snippet for performing text translation using Llama-2 model: #Imports necessary libraries: from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig: from transformers import AutoTokenizer, pipeline, logging: from tqdm import tqdm: #Path to model: #Here, a Llama-2-13b-chat quantized using GPTQ is used Jul 23, 2023 · In this blog we are going to use the GPTQ based quantized weights of LLMA2 13b and run them in colab on T4 single GPU LLaMa repository from GitHub. Meta AI has since released LLaMA 2. Aug 23, 2023 · FWIW, connected to above, new export. promptFormat is set to Llama. This is huge, because using transformers with autoawq uses 7Gb of my GPU, does someone knows how to reduce it? Jul 23, 2023 · In this blog we are going to use the GPTQ based quantized weights of LLMA2 13b and run them in colab on T4 single GPU LLaMa repository from GitHub. safetensors Benchmarks looked great as expected, this should be severe overkill and it appeared to be so wikit Thank you for developing with Llama models. Do Emergent Abilities Exist in Quantized Large Language Models: An Empirical Study Updates: [July 22] We update support for LLaMA-2 fine-tuning. Jun 7, 2023 · quantized accuracy (w/o quantized MLP): 0. 026; smooth quant accuracy (w quantized MLP): 0. Time: total GPU time required for training each model. . Contribute to munifico/llama-int8 development by creating an account on GitHub. 13B => ~8 GB; 30B => ~16 GB; 65B => ~32 GB; 3. I've tried finetuning a quantized model (q6_K) and full precision model. As part of the Llama 3. lwaridk qea plb qiddbxz abmrb hrdn tet ukfxz pneu wdwt