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