Best gpu for llama 2 7b. Especially good for story telling.

Best gpu for llama 2 7b 8 on llama 2 13b q8. We hope the benchmark will In this repository we are introducing a new member of NSQL, NSQL-Llama-2-7B. 08 GiB PowerEdge R760xa Deploy the model For this experiment, we used Pytorch: 23. r/buildapc. or best practices that could help me boost the performance. About AWQ AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Best gpu models are those with high vram (12 or up) I'm struggling on 8gbvram 3070ti for instance My big 1500+ token prompts are processed in around a minute and I get ~2. But in order to want to fine tune the un quantized model how much Gpu memory will I need? 48gb or 72gb or 96gb? does anyone have a code or a YouTube video tutorial to The previous generation of NVIDIA Ampere based architecture A100 GPU is still viable when running the Llama 2 7B parameter model for inferencing. 4-bit quantization will increase inference speed quite a bit with hardly any Only 30XX series has NVlink, that apparently image generation can't use multiple GPUs, text-generation supposedly allows 2 GPUs to be used simultaneously, whether you can mix and match Nvidia/AMD, and so on. If you use AdaFactor, then you need 4 bytes per parameter, or 28 GB of GPU memory. ExLlama : Dolphin-Llama2-7B-GPTQ Full GPU >> Output: 42. Beyond 3. 85 tokens/s |50 output tokens |23 input tokens Llama-2-7b-chat-GPTQ: 4bit-128g Llama 2 7B Chat - GGUF Model creator: Meta Llama 2; Original model: Llama 2 7B Chat; KoboldCpp, a fully featured web UI, with GPU accel across all platforms and GPU architectures. The exception is the A100 GPU which does not use 100% of GPU compute and therefore you get benefit from batching, but is hella expensive. It's based on Meta's original Llama-2 7B model and further pre-trained on a dataset of general SQL queries and then fine-tuned on a dataset composed of text-to-SQL pairs. 4 tokens generated per second for replies, though things slow down as the chat goes on. /orca_mini_v3_7B-GPTQ" temperature = 0. This is the repository for the 7B fine-tuned model, optimized for dialogue use cases and converted for the Hugging Face Transformers format. 2, but the same thing happens after upgrading to Ubuntu 22 and CUDA 11. 875 (0. q4_K_M. The size of Llama 2 70B fp16 is around 130GB so no you can't run Llama 2 70B fp16 with 2 x 24GB. This shows the suggested GPU for the latest Llama-3 -70B In this post, I’ll guide you through the minimum steps to set up Llama 2 on your local machine, assuming you have a medium-spec GPU like the RTX 3090. I could do 64B models. So 13B should be good on 3080/3090. Top. This encounters two immediate issues: a) the reward models we're using are incomplete and There is a big quality difference between 7B and 13B, so even though it will be slower you should use the 13B model. 5 on mistral 7b q8 and 2. Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This chart showcases a range of benchmarks for GPU performance while running large language models like LLaMA and Llama-2, using various quantizations. 36 1 1 bronze badge. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. Discover the best GPU VPS for Ollama at GPUMart. You need 2 x 80GB GPU or 4 x 48GB GPU or 6 x 24GB GPU to run fp16. I installed Ubuntu 22. More posts you may like r/buildapc. Also, CPU’s are just not good at doing floating point math compared to GPU’s. Based on LLaMA WizardLM 7B V1. Occasionally I'll load up a 7b model Nous Hermes Llama 2 7B - GGML Model creator: NousResearch; Original model: Nous Hermes Llama 2 7B; GGML files are for CPU + GPU inference using llama. 1,200 tokens per second for Llama 2 7B on H100! Discussion I don't think anything involving a $30k GPU is that relevant for personal use, or really needs to be posted in a sub about local inference. 02 tokens per second I also tried with LLaMA 7B f16, and the timings again show a slowdown when the GPU is introduced, eg 2. I think it might allow for API calls as well, but don't quote me on that. (Commercial entities could do 256. A Mad Llama Trying Fine-Tuning. Test Setup. I generally grab The Bloke's quantized Llama-2 70B models that are in the 38GB range or his 8bit 13B models. Llama-2-7b-chat-hf: Prompt: "hello there" Output generated in 27. 2. Power your AI workloads with the RTX A4000 VPS, designed for optimal performance and efficiency. The unique link is only good for 24 hours and you can only use it so many times. So do let KoboldCpp, a fully featured web UI, with GPU accel across all platforms and GPU architectures. Llama 2: Inferencing on a Single GPU Executive Honestly, I'm loving Llama 3 8b, it's incredible for its small size (yes, a model finally even better than Mistral 7b 0. Persisting GPU issues, white VGA light on mobo with two different RTX4070 cards Llama 2 7B - GPTQ Model creator: Meta; Original model: Llama 2 7B; Description Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements. Especially good for story telling. More posts you may like r/ChatGPT. While best practices for comprehensively evaluating a generative model is an open research question, the Run Llama 2 70B on Your GPU with ExLlamaV2 Notes. 60GHz Memory: 16GB GPU: RTX 3090 (24GB). Estimated total emissions were 539 tCO2eq, 100% of which were offset by Meta’s sustainability program. 98 token/sec on CPU only, 2. More posts you may like r/RedMagic. Remove it if you don't have GPU acceleration. You can use an 8-bit quantized model of about 12 B (which generally means a 7B model, maybe a 13B if you have memory swap/cache). GGML files are for CPU + GPU inference using llama. Personally I think the MetalX/GPT4-x-alpaca 30b model destroy all other models i tried in logic and it's quite good at both chat and notebook mode. g5. 5. cpp or other similar models, you may feel tempted to purchase a used 3090, 4090, or an Apple M2 to run these models. 2, fine-tuning large language models to perform well on targeted domains is increasingly feasible. Resources To those who are starting out on the llama model with llama. Share. I know the Raspberry Pi 4 can run llama 7b, so I figure at double the ram and onboard NPU's, Orange Pi 5 should be pretty solid. cpp and libraries and UIs which support this format, such as: LM Studio is a good choice for a chat interface that supports GGML versions (to come) I have RTX 4090 (24G) , i've managed to run Llama-2-7b-instruct-hf on GPU only with half precision which used ~13GB of GPU RAM. 6 bit and 3 bit was quite significant. Best. 27 lower) LLaMA-7b I'm interested to see if 70b can be quantized on a 24GB gpu. The container Posted by u/plain1994 - 106 votes and 21 comments Is it possible to fine-tune GPTQ model - e. But you can run Llama 2 70B 4-bit GPTQ on 2 x 24GB and many people are doing this. We were able to successfully fine-tune the Llama 2 7B model on a single Nvidia’s A100 40GB GPU and will provide a deep dive on how to configure the software environment to run the fine-tuning flow on Dell PowerEdge R760xa 8-bit Lora Batch size 1 Sequence length 256 Gradient accumulation 4 That must fit in. More posts you may like r/OpenAI. Subreddit to discuss about ChatGPT and AI. Go big (30B+) or go home. Llama-2-7B-32K-Instruct is an open-source, Hello everyone,I'm currently running Llama-2 70b on an A6000 GPU using Exllama, and I'm achieving an average inference speed of 10t/s, with peaks up to 13t/s. Subreddit to discuss about Llama, the large language model created by Meta AI. cpp as the model loader. Worked with I've been trying to try different ones, and the speed of GPTQ models are pretty good since they're loaded on GPU, however I'm not sure which one would be the best option for what purpose. Links to other models can be found in the index at the bottom. This is the repository for the 7B pretrained model, converted for the Hugging Face Transformers format. 5-4. Click Download. /main -ngl 32 -m llama-2-7b. 7B model was the biggest I could run on the GPU (Not the Meta one as the 7B need more then 13GB memory on the graphic card), but you can actually use Quantization technic to make the model smaller, just to compare the sizes before and after (After quantization 13B was running smooth). g. If you infer at batch_size = 1 on a model like Llama 2 7B on a "cheap" GPU like a T4 or an L4 it'll use about 100% of the compute, which means you get no benefit from batching. The second difference is the per-GPU power consumption cap — RSC uses 400W while our production cluster uses 350W. I went with the Plus version. 2-2. /main -ngl 32 -m nous-hermes-llama-2-7b. Set the maximum GPU memory. To get 100t/s on q8 you would need to have 1. For a detailed overview of suggested GPU configurations model = ". From a dude running a 7B model and seen performance of 13M models, I would say don't. 04 For the dual GPU setup, we utilized both -sm row and -sm layer options in llama. This document describes how to deploy and run inferencing on a Meta Llama 2 7B parameter model using a single NVIDIA A100 GPU with 40GB memory. Q4_K_M. We were able to successfully fine-tune the Llama 2 7B model on a single Nvidia’s A100 40GB CO 2 emissions during pretraining. r/LocalLLaMA. 7B: 184320 13B: 368640 70B: 1720320 Top 1% Rank by size . 1 GPTQ 4bit runs well and fast, but some GGML models with 13B 4bit/5bit quantization are also good. NeMo Framework offers support for various parameter-efficient fine-tuning (PEFT) methods for Llama 2 model family. But let’s face it, the average Joe building RAG applications isn’t confident in their ability to fine-tune an LLM — training data are hard to collect . It is actually even on par with the LLaMA 1 34b model. You can use a 2-bit quantized model to about 48G (so many 30B models). 3 already came out). 4GB, performs efficiently on the RTX A4000, delivering a prompt evaluation rate of 63. Developer: Meta AI Parameters: Variants ranging from 7B to 70B parameters Pretrained on: A diverse dataset compiled from multiple sources, focusing on quality and variety Fine-Tuning: Supports fine-tuning on specific datasets for enhanced performance in niche tasks License Type: Open-source with restrictions on commercial use Features: High With the release of Meta’s Llama 3. 55 LLama 2 70B to Q2 LLama 2 70B and see just what kind of difference that makes. All using CPU inference. If that happens you need to request another unique url. Llama 2 7B Arguments - AWQ Model creator: Cristian Desivo; Original model: Llama 2 7B Arguments; Description This repo contains AWQ model files for Cristian Desivo's Llama 2 7B Arguments. r/OpenAI Llama 2 7B - GGML Model creator: Meta; Original model: Llama 2 7B; GGML files are for CPU + GPU inference using llama. Model card: Meta's Llama 2 7B Llama 2. Thanks in advance for your insights! Edit: Im using Text-generation-webui with max_seq_len 4096 and alpha_value 2. 3M GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). up RunPod and running a basic Llama-2 7B model GPU memory consumed Platform Llama 2-7B-chat FP-16 1 x A100-40GB 14. Open comment sort options. So, you might be able to run a 30B model if it's quantized at Q3 or Q2. 1. 1 -n -1 -p "{prompt}" Change -ngl 32 to the number of layers to offload to GPU. Then starts then waiting part. 8 system_message = '''### System: You are an expert image prompt designer. 2 to elevate its performance on specific tasks, making it a powerful tool for machine learning engineers and data scientists looking to specialize their models. In my case, the RTX 2060 has compute capability 7. 2, in my use-cases at least)! And from what I've heard, the Llama 3 70b model is a total beast (although it's way too big for me to even try). --model_name_or_path llama2/Llama-2-7b-hf \ --do_train \ --dataset alpaca_gpt4_en \ --finetuning_type full \ not FSDP, so you have to fit the whole model into every gpu. Make sure you grab the GGML version of your model, I've been liking Nous Hermes Llama 2 We can observe in the above graphs that the Best Response Time (at 1 user) is 2 seconds. Install the packages in the container using the commands below: LLaMA-2-7B-32K by togethercomputer New Model huggingface. We further measured the GPU memory usage for each scenario. We note that reward model accuracy is one of the most important proxies for the final performance of Llama 2-Chat. To give you a point of comparison, when I benchmarked Llama 2 7B quantized to 4-bit with GPTQ The following resources reference different checkpoints of the Llama 2 family of models, but can be easily modified to apply to Llama 2 7B by changing the reference to the model! P-Tuning and LoRA. LLaMA 2. 100% of the LLMs are GPU compute-bound. You excel at inventing new and unique prompts for generating images. To download from a specific branch, enter for example TheBloke/Llama-2-7b-Chat-GPTQ:gptq-4bit-64g-actorder_True; see Provided Files above for the list of branches for each option. More posts you may like r/LocalLLaMA. co Open. Though, there are ways to improve your performance on CPU, namely by understanding how different converted models work. 6 RPS, the latency increases drastically which means requests are being queued up. For Best Performance: Opt for a machine with a high-end GPU (like NVIDIA's latest RTX 3090 or RTX 4090) or dual GPU setup to accommodate the largest models (65B Hence, for a 7B model you would need 8 bytes per parameter * 7 billion parameters = 56 GB of GPU memory. I was using K80 GPU for Llama-7B-chat but it's not work for me it's take all the resources from it. Free GPU options for LlaMA model experimentation . This list can help. Carbon Footprint Pretraining utilized a cumulative 3. GPU Recommended for Fine-tuning LLM. 6 RPS without a significant drop in latency. Full precision didn't load. Llama 2. KoboldCpp, a fully featured web UI, with GPU accel across all platforms and GPU architectures. Controversial Llama 2-chat ended up performing the best after three epochs on 10000 Fine-tuning LLMs like Llama-2-7b on a single GPU The use of techniques like parameter-efficient tuning and quantization Training a 7b param model on a single T4 GPU (QLoRA) Best Latency Deployment: Minimizing latency for real-time services We can see that GPTQ offers the best cost-effectiveness, allowing customers to deploy Llama 2 13B on a single GPU. Nytro. With CUBLAS, -ngl 10: 2. 91 tokens per second. Model Quantization Instance concurrent requests Latency (ms/token) median 7B Llama 2 achieved 16ms per token on ml. Results We swept through compatible combinations of the 4 variables of the experiment and present the most insightful trends below. Also, the RTX 3060 If inference speed and quality are my priority, what is the best Llama-2 model to run? 7B vs 13B 4bit vs 8bit vs 16bit GPTQ vs GGUF vs bitsandbytes Share Sort by: Best. and make sure to offload all the layers of the Neural Net to the GPU. I In text-generation-web-ui: Under Download Model, you can enter the model repo: TheBloke/Llama-2-70B-GGUF and below it, a specific filename to download, such as: llama-2-70b. Write a response that appropriately completes the The smallest Llama 2 chat model is Llama-2 7B Chat, with 7 billion parameters. ggmlv3. , TheBloke/Llama-2-7B-chat-GPTQ - on a system with a single NVIDIA GPU? It would be great to see some example code in Python on how to do it, if it is feasible at all. This model showcases the plan's ability to handle Orange Pi 5 Series is probably your best bang for buck as a SBC that can run a model. The user will send you examples of image prompts, and then you invent one more. 3. Lit-GPT is a similar repo that does support FSDP, but its much more messy than this one. This article provides a comprehensive guide on fine-tuning Llama 3. Not affiliated with OpenAI. LLM360 has released K2 65b, a fully reproducible open source LLM matching Llama 2 70b It mostly depends on your ram bandwith, with dual channel ddr4 you should have around 3. With a 4090rtx you can fit an entire 30b 4bit model assuming your not running --groupsize 128. Hey I am searching about that which is suite able GPU for llama-2-7B-chat & llama-2-70B-chat for run the model in live server. r/ChatGPT. 12xlarge. SqueezeLLM got strong results for 3 bit, but interestingly decided not to push 2 bit. As you can see the fp16 original 7B model has very bad performance with the same input/output. 31 tokens/sec partly offloaded to GPU with -ngl 4 I started with Ubuntu 18 and CUDA 10. 2 and 2-2. I recommend getting at least 16 GB RAM so you can run other programs alongside the LLM. Set n-gpu-layers to max, n_ctx to 4096 and usually that should be enough. It allows for GPU acceleration as well if you're into that down the road. Best Latency Deployment: Minimizing latency for real-time services We can see that GPTQ offers the best cost-effectiveness, allowing customers to deploy Llama 2 13B on a single GPU. gguf. koboldcpp. but it’s also the kind of overpowered hardware that you need to handle top end models such as 70b Llama 2 with ease. I am using A100 80GB, but still I have to wait, like the previous 4 days and the next 4 days. Hugging Face recommends using 1x Nvidia To keep this simple, the easiest way right now is to ensure you have an NVIDIA GPU with at least 6GB of VRAM that is CUDA compatible. This is a finetuned LLMs with human-feedback and optimized for dialogue use cases based on the 7-billion parameter Llama-2 pre-trained model. Reply reply Top 1% Rank by size . Loading Llama 2 70B requires 140 GB of memory (70 billion * 2 bytes). Once it's finished it will say "Done". can be used to fine-tune Llama 2 7B model on single GPU. With the optimizers of For a detailed overview of suggested GPU configurations for fine-tuning LLMs with various model sizes, precisions and fine-tuning techniques, refer to the table below. Top 2% Rank by size . RunPod is a cloud GPU platform that allows you to run ML models at affordable prices without having to secure or manage a physical GPU. Still, it might Run Llama 2 model on your local environment. 06 from NVIDIA NGC. Improve this answer. My local environment: OS: Ubuntu 20. You can use a 4-bit quantized model of about 24 B. bin" --threads 12 --stream. The Qwen2:7b model, with a size of 4. Add a comment | 0 Here are hours spent/gpu. According to open leaderboard on HF, Vicuna 7B 1. LM Studio, a fully featured local GUI with GPU acceleration on both Windows (NVidia and AMD), and macOS. The model will start downloading. 3 top_k = 250 top_p = 0. Use this !CMAKE_ARGS="-DLLAMA_CUBLAS=on" FORCE_CMAKE=1 pip install llama-cpp-python. Then click Download. 5 LTS Hardware: CPU: 11th Gen Intel(R) Core(TM) i5-1145G7 @ 2. It’s a powerful and accessible LLM for fine-tuning because with fewer parameters it is an ideal candidate for In this blog post, we deploy a Llama 2 model in Oracle Cloud Infrastructure (OCI) Data Science Service and then take it for a test drive with a simple Gradio UI chatbot client application. The Llama 2-Chat model deploys in a custom container in the OCI Data Science service using the model deployment feature for online inferencing. It isn't clear to me whether consumers can cap out at 2 NVlinked GPUs, or more. . Llama The unquantized Llama 2 7b is over 12 gb in size. 00 seconds |1. One fp16 parameter weighs 2 bytes. . Use llama. Good inference speed in AutoGPTQ and GPTQ-for-LLaMa. The largest and best model of the Llama 2 family has 70 billion parameters. It would be interesting to compare Q2. Use the from_pretrained` method from the `AutoModelForCausalLM` class to load a pre-trained Hugging Face model in 4-bit precision using the model name and the Nytro. Training Data The general SQL queries are the SQL subset from The Stack, containing 1M training Just to let you know: I've quantized Together Computer, Inc. q4_K_S. Otherwise you have to close them all to reserve 6-8 GB RAM for a 7B model to run without slowing down from swapping. ) I don't have any useful GPUs yet, so I can't verify this. You'll need to stick to 7B to fit onto the 8gb gpu Under Download custom model or LoRA, enter TheBloke/Llama-2-7b-Chat-GPTQ. 1 -n -1 -p "Below is an instruction that describes a task. It's gonna be complex and brittle though. We hope the benchmark will Reason being it'll be difficult to hire the "right" amount of GPU to match you SaaS's fluctuating demand. Follow answered Dec 16, 2023 at 15:53. To achieve 139 tokens per second, we required only a single A100 GPU for optimal performance. Share NVLink for the 30XX allows co-op processing. TheBloke/Llama-2-7b-Chat-GPTQ · Hugging Face. The model under investigation is Llama-2-7b-chat-hf [2]. 5 TB/s bandwidth on GPU dedicated entirely to the model on highly optimized backend (rtx 4090 have just under 1TB/s but you can get like 90-100t/s with mistral 4bit GPTQ) In 8 GB RAM and 16 GB RAM laptops of recent vintage, I'm getting 2-4 t/s for 7B models, 10 t/s for 3B and Phi-2. 7 --repeat_penalty 1. To download from a specific branch, enter for example TheBloke/llama-2-7B-Guanaco-QLoRA-GPTQ:main; see Provided Files Heres my result with different models, which led me thinking am I doing things right. Maybe there's some optimization under the hood when I train with the 24GB GPU, that increases the memory usage to ~14GB. Share Sort by: Best. With -sm row, the dual RTX 3090 demonstrated a higher inference speed of 3 tokens per second (t/s), whereas the dual RTX 4090 With RLHF, the primary performance metric used during training is monotonic increases in the reward from the preference model. A100 40GB GPU Nytro. Especially good for This makes the models very large and difficult to store in either system or GPU RAM. gguf --color -c 4096 --temp 0. 0 Uncensored is the best one IMO, though it can't compete with any Llama 2 fine tunes Waiting for WizardLM 7B V1. If you really must though I'd suggest wrapping this in an API and doing a hybrid local/cloud setup to minimize cost while having ability to scale. Original model card: Meta's Llama 2 7b Chat Llama 2. with ```···--alpha_value 2 - The Mistral 7b AI model beats LLaMA 2 7b on all benchmarks and LLaMA 2 13b in many benchmarks. Then we deployed those models into Dell server and measured their performance. 70b Llama 2 is competitive with the free-tier of ChatGPT Original model card: Meta's Llama 2 7B Llama 2. 04. Both have been trained with a context length of 32K - and, provided that you have enough RAM, you can benefit from such large contexts right away! I am wondering if the 3090 is really the most cost effectuent and best GPU overall for inference on 13B/30B parameter model. 4xlarge instance: Llama 2. 14 t/s (134 tokens, context 780) vram ~8GB LLM360 has released K2 65b, a fully reproducible open source LLM matching Llama 2 70b upvotes Best GPU for 1440P (3440x1440)? KoboldCpp, a fully featured web UI, with GPU accel across all platforms and GPU architectures. 8 Under Download custom model or LoRA, enter TheBloke/llama-2-7B-Guanaco-QLoRA-GPTQ. With that kind of budget you can easily do this. Llama-2-7B-32K-Instruct is an open-source, long-context chat model finetuned from Llama-2-7B-32K, over [Edited: Yes, I've find it easy to repeat itself even in single reply] I can not tell the diffrence of text between TheBloke/llama-2-13B-Guanaco-QLoRA-GPTQ with chronos-hermes-13B-GPTQ, except a few things. Check with nvidia-smi command how much you have headroom and play with parameters until VRAM is 80% occupied. devyy devyy devyy devyy. More. So I made a quick video about how to deploy this model on an A10 GPU on an AWS EC2 g5. 's LLaMA-2-7B-32K and Llama-2-7B-32K-Instruct models and uploaded them in GGUF format - ready to be used with llama. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for I know the "best" can be a bit subjective so I think the better question is, what 7b model do people use the most nowaday? GGML format would be best on my case. The Llama 2 language model represents Meta AI’s latest advancement in large language models, boasting a 40% performance boost and increased data size compared to its predecessor, Llama 1. Minstral 7B works fine on inference on 24GB RAM (on my NVIDIA rtx3090). LM Studio, This ends up preventing Llama 2 70B fp16, whose weights alone take up 140GB, from comfortably fitting into the 160GB GPU memory available at tensor parallelism 2 (TP-2). Tried llama-2 7b-13b-70b and variants. Model Details Update: Interestingly, when training on the free Google Colab GPU instance w/ 15GB T4 GPU, I am observing a GPU memory usage of ~11GB. We can increase the number of users to throw more traffic at the model - we can see the throughput increasing till 3. The data covers a set of GPUs, from Apple Silicon M series chips to Nvidia GPUs, helping you make an informed decision if you’re considering using a large language model locally. 3(As 13B V1. ai uses technology that works best in other browsers. LLaMA-2-7b: Transformers 16-bit 5. So, maybe it is possible to QLoRA fine-tune a 7B model with 12GB VRAM! Original model card: Meta Llama 2's Llama 2 7B Chat Llama 2. cpp. This paper looked at 2 bit-s effect and found the difference between 2 bit, 2. Time: total GPU time required for training each model. Install the NVIDIA-container toolkit for the docker container to use the system GPU. New. New Pure GPU gives better inference speed This shows the suggested LLM inference GPU requirements for the latest Llama-3-70B model and the older Llama-2-7B model. exe --model "llama-2-13b. There are some great open box deals on ebay from trusted sources. cpp and libraries and UIs which support this format, such as: (CUDA and OpenCL). tumzg qxm xgeyoequ hzxe imvz anzdi lah gjmrmcs jytxaq sszh