Llama 13b quantized github sh). I will eventually use L40s for w4a8_awq inference. 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. This code is based on GPTQ. Links to other models can be found in the index at the bottom. 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. 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. Jul 31, 2023 · from transformers import AutoTokenizer, TextGenerationPipeline: from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig: import logging: logging. I've tested it on an RTX 4090, and it reportedly works on the 3090 . ipynb) and Llama demo (examples/smoothquant_llama_demo. 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. 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. raw Result Quantized inference code for LLaMA models. 34: 13B => ~8 GB; 30B => ~16 GB; 65B => ~32 GB; 3. 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. This is a fork of the LLaMA code that runs LLaMA-13B comfortably within 24 GiB of RAM. 0G free RAM, respectively. When I tried using v0. Hence, the ownership of bind-mounted directories (/data/model and /data/exllama_sessions in the default docker-compose. GPTQ is SOTA one-shot weight quantization method. - johnh00/llama2-13b-qlora This is a fork of the LLaMA code that runs LLaMA-13B comfortably within 24 GiB of RAM. 13B, url : only needed if connecting to a remote dalai server if unspecified, it uses the node. yml file) is changed to this non-root user in the container entrypoint (entrypoint. [July 15] We release the code especially for fine-tuning LLaMA-65B within a single A100 GPU. bin with your respective models cd minigpt4 python minigpt4_library. When serving the large language models Llama-3-8B and Qwen1. Contribute to a-leut/llama-int8 development by creating an account on GitHub. json (or what changes to the config. We also provide the script to get the activation channel scales for your models. 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. 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. LLaMA Server combines the power of LLaMA C++ (via PyLLaMACpp) with the beauty of Chatbot UI. promptFormat is set to Llama. You signed out in another tab or window. I'm just so exited about Bitnets that I wanted to give heads up here. Currently 7B and 13B models are available via alpaca. This also holds for an 8-bit 13B model compared with a 16-bit 7B model. 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. 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. The present study uses FinancialPhraseBank dataset curated by Malo et. 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. For example, quantizing a LLaMa-13b model requires 32gb, and LLaMa-33b requires more memory than 64gb. [24/04/21] We supported Mixture-of-Depths according to AstraMindAI's implementation. test. . Sign in Product May 23, 2023 · Use a 5_1 quantized model. 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. CO 2 emissions during pretraining. [NeurIPS'23 Oral] Visual Instruction Tuning (LLaVA) built towards GPT-4V level capabilities and beyond. Jul 18, 2023 · We release the resources associated with QLoRA finetuning in this repository under MIT license. Sign in Product Quantized inference code for LLaMA models. 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. 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. 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. Figure 1 Perplexity as a function of context size for the LLaMA-1 (black) and LLaMA-2 (red) 7B models. 13B => ~8 GB; 30B => ~16 GB; 65B => ~32 GB; 3. This app includes three models, LLaMa-2-7B-Chat-Omniquant-W3A16g128asym, LLaMa-2-13B-Chat-Omniquant-W3A16g128asym, and LLaMa-2-13B-Chat-Omniquant-W2A16g128asym. 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. Quantizing the model requires a large amount of CPU memory. 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. I hope you are having a great day. 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. model size = 13B llama_model Navigation Menu Toggle navigation. int8() work of Tim Dettmers. I used the same dataset with axolotl training. The code as follow: shown as follow: from vllm import LLM, SamplingParams from huggingface_hub import login. This repository contains the necessary This is the 13B fine-tuned GPTQ quantized model, optimized for dialogue use cases. Reload to refresh your session. bin Quantized inference code for LLaMA models. 50: 46. You switched accounts on another tab or window. 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. raw Result Sep 30, 2023 · Hello guys, I was able to load my fine-tuned version of mistral-7b-v0. py minigpt4-13B-f16. 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. Run the quantized model: Llama 2 13B. Aug 23, 2023 · FWIW, connected to above, new export. 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. 5-72B. This is huge, because using transformers with autoawq uses 7Gb of my GPU, does someone knows how to reduce it? Navigation Menu Toggle navigation. This would have several advantages: Llama 3 8B model performs significantly better on Quantized inference code for LLaMA models. In addition, we release the Guanaco model family for base LLaMA model sizes of 7B, 13B, 33B, and 65B. This allows you to load the largest model on your GPU with the smallest amount of quality loss. With the code below, for prompts w/ a token length ~1300 or less, after running the generate 3 times, it produces a random response. 9 a month ago, I could successfully quantize my model using AMMO and get int4_awq and w4a8_awq engines (group_size = 64) finally. 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. - 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. Deploying the quantized LLAMA 2–13b language model as an API using FastAPI - peterbull/fastapi-hermes-2. Contribute to mlc-ai/llm-perf-bench development by creating an account on GitHub. n1-highmem-4 1 x NVIDIA T4 Virtual Workstation. First Steps. 5G, 7. al which is a polar sentiment dataset consisting of 4,840 sentences from English language financial news. Pre-computed AWQ model zoo for LLMs (Llama-1/2/3, OPT, CodeLlama, StarCoder, Vicuna, VILA, LLaVA; load to generate quantized weights). Test if minigpt4 works by calling the following, replacing minigpt4-13B-f16. 15: 28. 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. 34 ms per token 30b (6 threads): main: predict time = 165125. Contribute to jihoyeo/llama-int8 development by creating an account on GitHub. Mar 11, 2023 · 13b (6 threads): main: predict time = 67519. This model is under a non-commercial license (see the LICENSE file). 5-streaming-api Aug 13, 2023 · I was able to replicate this issue. Efficient CUDA kernel implementation for fast inference (support context and decoding stage). cpp. 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. Jul 19, 2023 · Similar to #79, but for Llama 2. Q4_K_M. When I run the 13B model it is very slow I have tried to set mlock as true as well. Contribute to Ak4ft7/llama-int8 development by creating an account on GitHub. ; Thanks for your rely. llama. This is a fork of the LLaMA code that runs LLaMA-13B comfortably within 24 GiB of RAM. 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. Automate any workflow Packages The main goal of llama. Quantization Bits per Weight (BPW) Q2_K: 3. 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. 30: 31. 481; quantized accuracy (w quantized MLP): 0. py --auto-devices - Example: alpaca. 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. 23: 28. [24/04/22] We provided a Colab notebook for fine-tuning the Llama-3 model on a free T4 GPU. 4x-3. LLaMA-13B: 28. 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. Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. 0! UPDATE: Now supports better streaming through PyLLaMACpp! Apr 6, 2023 · I think I'm missing a conversion step here. 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 Quantized inference code for LLaMA models. 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. Contribute to munifico/llama-int8 development by creating an account on GitHub. 🚀 LoftQ finds good enough quantized LoRA initialization: quantized backbone Q and LoRA adapters A and B, given a pre-trained weight W. 4 GB, while a 2-BIT QuIP model on You signed in with another tab or window. Nov 8, 2023 · Interesting I just played around a bit with Bakllava and compared it to llava 1. 026; smooth quant accuracy (w quantized MLP): 0. 1-awq quantized with autoawq on my 24Gb TITAN RTX, and it’s using almost 21Gb of the 24Gb. 5x higher throughput for Qwen1. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. bin and ggml-vicuna-13B-v0-q5_k. This contains the weights for the LLaMA-13b model. May 17, 2023 · Running 4bit quantized models on M1 with 8gb RAM. Presently this is Linux only, but you might be able to make it work with other OSs. This is the 13B fine-tuned GPTQ quantized model, optimized for dialogue use cases. cpp is not just for Llama models, for lot more, I'm not sure but hoping would work for Bitnets too. 0. > cargo run --release --features 13B,group_128,quantized -- -c l13orca. Memory-efficient 4-bit Linear in PyTorch. 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. Jun 7, 2023 · quantized accuracy (w/o quantized MLP): 0. Before you do any of this, you will need a bot token. 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. Apr 8, 2023 · Seems to happen with different models (Tested with llama-30b-4bit-128g, llama-13b-4bit-128g and Alpaca-30b-4bit-128g). 7B, llama. 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. 2x-1. 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. . It might also theoretically allow us to run LLaMA-65B on an 80GB A100, but I haven't tried this. Sign in Product Aug 3, 2023 · You signed in with another tab or window. AND. I've tested it on an RTX 4090, and it reportedly works on the 3090. Mostly Default . A set of out-of-the-box arbitrary bit quantization operators that support arbitrary bit model inference in Turing and above architectures. May 18, 2023 · Not Compatible with Models quantized with updated llama. Navigation Menu Toggle navigation. 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. Alpaca comes fully quantized (compressed), and the only space you need for the 7B model is 4. bin Jul 13, 2023 · You signed in with another tab or window. 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. This is the repository for the 13B pretrained model, converted for the Hugging Face Transformers format. /gpt4all-lora-quantized-linux-x86 -m ggml-vicuna-13b-4bit-rev1. py quantized llama model with reference to opt. You can use the OPT demo (examples/smoothquant_opt_demo. These are the models published on HuggingFace by decapoda-research. This repo implements the paper 🔗: LoftQ: LoRA-Fine-Tuning-Aware Quantization for Large Language Models . 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 . NOTE: by default, the service inside the docker container is run by a non-root user. An 8-8-8 30B quantized model outperforms a 13B model of similar size, and should have lower latency and higher throughput in practice. These models are intended for purposes in line with the LLaMA license and require access to the LLaMA models. 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. py llama-13b c4 --wbits 8 --true-sequential --groupsize 128 --save_safetensors llama-13b-8bit-128g. 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. 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. The sub-modules that contain the ONNX files in this repository are access controlled. A collection of quantization recipes for various large models including Llama-2-70B, QWen-14B, Baichuan-2-13B, and more. Disk Space Requirements Alpaca. To get access permissions to the Llama 2 model, please fill out the Llama 2 ONNX sign up page. Build your greatest ideas and seamlessly deploy in minutes with Llama API and Llama Stack. 4x higher throughput compared to the leading industry solution, TensorRT-LLM, for Llama-3-8B, and a 2. Set n_ctx as you want. This repository contains the necessary GitHub Advanced Security. They require at least 4. py script OOMs with llama-2-70B model on 197G machine. 14GB: LLaMA Original model card: Meta's Llama 2 13B Llama 2. We see LLaMA-2 Q4_K_S perplexity is lower than the fp16 perplexity of LLaMA-1. Contribute to Gary3410/llama-int8 development by creating an account on GitHub. login("") prompts = Jun 11, 2024 · w4a8_awq only support group_size = 128 at the moment. 0 licensed weights are being released as part of the Open LLaMA project. I've tried finetuning a quantized model (q6_K) and full precision model. 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. Meta AI has since released LLaMA 2. Contribute to mengjiexu/llama-int8 development by creating an account on GitHub. Possible values are 7B, 13B, 30B, 7B_8bit, 13B_8bit, 30B, 30B_8bit, 65B, 65B_8bt. It relies almost entirely on the bitsandbytes and LLM. Note that increasing this parameter increases quality at the cost of performance (tokens per second) and VRAM. 21GB: 13B. - ranchlai/quantizations Pre-trained ABQ-LLM model weights for LLM (LLaMA and LLaMA-2 loaded to run quantized models). Jul 23, 2023 · A comparison between k-quants perplexities for the 13B LLaMA-1 and LLaMA-2 models is shown in Figure 5. 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. For these models make sure the setting locopilot. 56 ms / 555. Peiyu Liu, Zikang Liu, Ze-Feng Gao, Dawei Gao, Wayne Xin Zhao, Yaliang Li, Bolin Ding, Ji-Rong Wen. Use this discussion to Coordinate. Contribute to jorahn/llama-int8 development by creating an account on GitHub. Alpaca comes fully quantized (compressed), and the only space you need for the 13B model is 8. cpp PR 1405. 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. bin -t 0. This is a fork that adds support for ROCm's HIP to use in AMD GPUs, only supported on linux. A Q2_K 13B model needs around 5. DeepSeek-R1-Distill-Qwen-32B outperforms OpenAI-o1-mini across various benchmarks, achieving new state-of-the-art results for dense models. To run LLaMA 2 weights, Open LLaMA weights, or Vicuna weights (among other LLaMA-like checkpoints), check out the Lit-GPT repository. act. If you don't have a bot token, follow this guide to make a bot and then add the bot to your server. bin ggml-vicuna-13B-v0-q5_k. As part of the Llama 3. Also 3-bit 13B GPTQ will perform better than 7B at FP16. It can be quantized similarly. Generate a HuggingFace read-only access token from your user profile settings page. Contribute to jebtang/llama-int8 development by creating an account on GitHub. 37 GB of RAM and accordingly should work on computers with 12GB of RAM or more available. If allowable, you will receive GitHub access in the next 48 hours, but usually much sooner. 7B. py, the accuracy of the model I get is 0? Nov 19, 2023 · Expected Behavior I tried to finetune a model using a dataset. 5-72B on L40S and A100 GPUs, QServe demonstrates superior performance, achieving 1. /perplexity settings with all of wiki. Mar 23, 2023 · We are currently collecting Perplexity scores for all models + quantization + program flags. I am here achieved tok/s: 5. Mar 27, 2023 · Quantized with python llama. 14GB: LLaMA First, 8-bit quantization should be preferred over smaller full precision models, and PTQ methods are sufficient for this case. 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. 🦙LLaMA C++ (via 🐍PyLLaMACpp) 🤖Chatbot UI 🔗LLaMA Server 🟰 😊. This model will require 10. Contribute to jacob1264/llama-int8 development by creating an account on GitHub. Quantized inference code for LLaMA models. Sign in Product. Before you begin, ensure 🇨🇳中文 | 🌐English | 📖文档/Docs | 提问/Issues | 💬讨论/Discussions | ⚔️竞技场/Arena. 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. 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. TARGET_MODEL_NAME correspond to various flavors of Llama models (7B to 30B), with or without quantization. UPDATE: Greatly simplified implementation thanks to the awesome Pythonic APIs of PyLLaMACpp 2. Example: alpaca. llama-2-13B seems to export fine with the same machine. 22: and quant8_saved_dir is the directory where the 8bits quantized model is saved. 5G, and 6. 067; Why is it that when I write the llama. Using 65B versions, however, requires providing the weights yourself. Do Emergent Abilities Exist in Quantized Large Language Models: An Empirical Study Updates: [July 22] We update support for LLaMA-2 fine-tuning. Mar 11, 2023 · However, in other cases it's better (only tested upto 13B models). cpp q4 and q5 quantization released in llama. json) to use when attempting to evaluate 4 bit quantized models. A repo for creating a fine-tuned quantized LORA of the 13B paramater llama2 chat model. Contribute to Jaid/llama-cpp development by creating an account on GitHub. 31 ms / 227. Time: total GPU time required for training each model. 446; smooth quant accuracy (w/o quantized MLP): 0. Apr 23, 2023 · Saved searches Use saved searches to filter your results more quickly You signed in with another tab or window. Additionally, new Apache 2. Post your hardware setup and what model you managed to run on it. ipynb) to test smoothing and quantizing those models. gguf here. See examples for usage. yyfjruj blrhql jcegi gtzryf apthp ger jledti frbr zgtom zmx