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