Llama3 70b memory requirements. I'd like to run it on GPUs with less than 32GB of memory.

Settings used are: split 14,20. The tuned versions use supervised fine Apr 18, 2024 · Variations Llama 3 comes in two sizes — 8B and 70B parameters — in pre-trained and instruction tuned variants. 24xlarge instance type, which has 8 NVIDIA A100 GPUs and 320GB of GPU memory. Llama 70B is a big model. The model istelf performed well on a wide range of industry benchmakrs and offers new To download the model without running it, use ollama pull wizardlm:70b-llama2-q4_0. cpp. Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. py Output <Remember to wrap the output in ```triple-quotes blocks```> Out o This lowers the memory and compute requirements; for instance, with 8-bit quantization, a 70B parameter model necessitates around 70-80 GB of GPU memory, whereas 4-bit quantization only requires at least 35 GB. PEFT, or Parameter Efficient Fine Tuning, allows Note: For Apple Silicon, check the recommendedMaxWorkingSetSize in the result to see how much memory can be allocated on the GPU and maintain its performance. Mar 4, 2023 · The most important ones are max_batch_size and max_seq_length. Nov 6, 2023 · The MFU is expected to decrease a little as a smaller per device batch size is needed to accommodate the additional memory pressure introduced by the larger sequence length since the sequence length axis is not sharded in 2D sharding. The 8B version, which has 8. To use a 1M+ context window, significantly more memory (100GB+) is needed. Aug 5, 2023 · Step 3: Configure the Python Wrapper of llama. The tuned versions use supervised fine-tuning Apr 18, 2024 · While the previous generation has been trained on a dataset of 2 trillion tokens the new one utilised 15 trillion tokens. To begin, start the server: For LLaMA 3 8B: python -m vllm. QLoRA introduces a number of innovations to save memory without sacrificing performance: (a) 4-bit NormalFloat (NF4), a new data type that is information theoretically optimal for normally distributed weights (b) Double Quantization to reduce the average memory footprint by quantizing the quantization constants, and (c) Paged Optimizers to Dec 4, 2023 · NVidia A10 GPUs have been around for a couple of years. Discussion. We’ll use the Python wrapper of llama. 48xlarge instance comes with 12 Inferentia2 accelerators that include 24 Neuron Cores. We will use a p4d. 5. Mar 3, 2023 · If so it would make sense as the memory requirements for a 65b parameter model is 65 * 4 = ~260GB as per LLM-Numbers. Intel® Xeon® 6 processors with Performance-cores (code-named Granite Rapids) show a 2x improvement on Llama 3 8B inference latency Mar 11, 2023 · SpeedyCraftah commented on Mar 21, 2023. per_device_train_batch_size and per_device_eval_batch_size: I set them to 1. Load the GPT: Navigate to the provided GPT link and load it with your task description. In general, it can achieve the best performance but it is also the most resource-intensive and time consuming: it requires most GPU resources and takes the longest. Using Llama 3 on Azure Jan 29, 2024 · Run Locally with Ollama. FAIR should really set the max_batch_size to 1 by default. Clear cache. Q-LoRA is a fine-tuning method that leverages quantization and Low-Rank Adapters to efficiently reduced computational requirements and memory footprint. Since Llama 3 70B is a large model, the activations, i. With the quantization technique of reducing the weights size to 4 bits, even the powerful Llama 2 70B model can be deployed on 2xA10 GPUs. The inf2. Simply click on the ‘install’ button. How to run Llama3 70B on a single GPU with just 4GB memory GPU The model architecture of Llama3 has not changed, so AirLLM actually already naturally supports running Llama3 70B perfectly! Llama 3 is a powerful open-source language model from Meta AI, available in 8B and 70B parameter sizes. Then, you need to run the Ollama server in the backend: ollama serve&. The minimum recommended vRAM needed for this model assumes using Accelerate or device_map="auto" and is denoted by the size of the "largest layer". In addition to running on Intel data center platforms Nov 30, 2023 · A simple calculation, for the 70B model this KV cache size is about: 2 * input_length * num_layers * num_heads * vector_dim * 4. This model is designed for general code synthesis and understanding. 70 * 4 bytes 32 / 16 * 1. More tests will be This is the memory allocated only to store parameters in the GPU memory without doing any work with it. ) Based on the Transformer kv cache formula. api_server \ --model meta-llama/Meta-Llama-3-8B-Instruct. 70b models generally require at least 64GB of RAM; If you run into issues with higher quantization levels, try using the q4 model or shut down any other programs that are using a lot of memory. Code/Base Model - ollama run codellama:70b-code. May 13, 2024 · AdaFactor is an even more memory-efficient alternative but it can be unstable. , all the tensors created during the forward pass, consume a lot of memory. , 65 * 2 = ~130GB. Mistral-7B often seems fairly close to Llama2-70B. Install the LLM which you want to use locally. 13B => ~8 GB. Describe the bug Out of memory. Apr 30, 2024 · Based on the provided document, the hardware requirements for running the llama3-gradient model depend on the desired context window size: To use a 256k context window, at least 64GB of memory is required. That’s quite a lot of memory. Then, go back to the thread window. Note: We haven't tested GPTQ models yet. The tuned versions use supervised fine-tuning We would like to show you a description here but the site won’t allow us. Whether you're developing agents, or other AI-powered applications, Llama 3 in both 8B and Apr 18, 2024 · Accelerate Meta* Llama 3 with Intel AI Solutions. Since the original models are using FP16 and llama. The 7B model, for example, can be served on a single GPU. Apr 22, 2024 · FSDP + Q-Lora needs ~2x40GB GPUs. You can see first-hand the performance of Llama 3 by using Meta AI for coding tasks and problem solving. Memory requirements. When you step up to the big models like 65B and 70B models (llama-65B-GGML), you need some serious hardware. The tuned versions use supervised fine-tuning Apr 18, 2024 · Variations Llama 3 comes in two sizes — 8B and 70B parameters — in pre-trained and instruction tuned variants. Deploying Mistral/Llama 2 or other LLMs. Python Model - ollama run codellama:70b-python. This makes the model compatible with a dual-GPU setup such as dual RTX 3090, RTX 4090, or Tesla P40 GPUs. More tests will be . 🏥 Biomedical Specialization: OpenBioLLM-70B is tailored for the unique language and Aug 7, 2023 · 3. Running the following on a desktop OS will launch a tab in your web browser with a chatbot interface. By default, Ollama uses 4-bit Dec 12, 2023 · For beefier models like the Llama-2-13B-German-Assistant-v4-GPTQ, you'll need more powerful hardware. Intel Xeon processors address demanding end-to-end AI workloads, and Intel invests in optimizing LLM results to reduce latency. If you're using the GPTQ version, you'll want a strong GPU with at least 10 gigs of VRAM. Model Architecture Llama 3 is an auto-regressive language model that uses an optimized transformer architecture. Code Llama is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. Links to other models can be found in Before we get started we should talk about system requirements. Mar 4, 2024 · To operate 5-bit quantization version of Mixtral you need a minimum 32. Ensure your GPU has enough memory. Additionally, the memory usage will also increase as per the batch size. Now we need to install the command line tool for Ollama. In this blog post we will show how to Apr 18, 2024 · Written guide: https://schoolofmachinelearning. It's 32 now. e. Key features include an expanded 128K token vocabulary for improved multilingual performance, CUDA graph acceleration for up to 4x faster This guide provides information and resources to help you set up Llama including how to access the model, hosting, how-to and integration guides. If you use AdaFactor, then you need 4 bytes per parameter, or 28 GB of GPU memory. 3 GB of memory. But this time, Meta is going one step further, as they are officially putting these models to work. When performing inference, expect to add up to an additional 20% to this, as found by EleutherAI. Installing Command Line. The exact amount of memory required also depends on the rank parameter of LoRA, the selection of weights, and the optimization algorithm. Depends on what you want for speed, I suppose. The model has 70 billion parameters. With the optimizers of bitsandbytes (like 8 bit AdamW), you would need 2 bytes per parameter, or 14 GB of GPU memory. According to our monitoring, the entire inference process uses less than 4GB GPU memory! 02. Llama 3 represents a large improvement over Llama 2 and other openly available models: Trained on a dataset seven times larger than Llama 2. Naively this requires 140GB VRam. Thanks to improvements in pretraining and post-training, our pretrained and instruction-fine-tuned models are the best models existing today at the 8B and 70B parameter scale. I think htop shows ~56gb of system ram used as well as about ~18-20gb vram for offloaded layers. 2. To enable GPU support, set certain environment variables before compiling: set Apr 20, 2024 · You can change /usr/bin/ollama to other places, as long as they are in your path. Model variants. Head over to Terminal and run the following command ollama run mistral. 7b models generally require at least 8GB of RAM; 13b models generally require at least 16GB of RAM; 70b models generally require at least 64GB of RAM; If you run into issues with higher quantization levels, try using the q4 model or shut down any other programs that are using a lot of memory. Llama 2 comes in 3 different sizes - 7B, 13B & 70B parameters. The tuned versions use supervised fine-tuning May 9, 2024 · Launch the Jan AI application, go to the settings, select the “Groq Inference Engine” option in the extension section, and add the API key. Links to other models can be found in the index at the bottom. With 3x3090/4090 or A6000+3090/4090 you can do 32K with a bit of room to spare. We're talking an A100 40GB, dual RTX 3090s or 4090s, A40, RTX A6000, or 8000. Minimal reproducible example I guess any A100 system with 8+ GPUs python example_chat_completion. Output Models generate text only. Effective today, we have validated our AI product portfolio on the first Llama 3 8B and 70B models. This guide will walk you through the process OpenBioLLM-70B is an advanced open source language model designed specifically for the biomedical domain. META LLAMA 3 COMMUNITY LICENSE AGREEMENT Meta Llama 3 Version Release Date: April 18, 2024 “Agreement” means the terms and conditions for use, reproduction, distribution and modification of the Llama Materials set forth herein. This tutorial showcased the capabilities of the Meta-Llama-3 model using Apple’s silicon chips and the MLX framework, demonstrating how to handle tasks from basic interactions to complex mathematical problems efficiently. Deploy Llama 3 to Amazon SageMaker. Apr 18, 2024 · Llama 3. Let’s define that a high-end consumer GPU, such as the NVIDIA RTX 3090 * or 4090. AMD 6900 XT, RTX 2060 12GB, RTX 3060 12GB, or RTX 3080 would do the trick. exllama scales very well with multi-gpu. This command will download and load the Llama 3 70b model, which is a large language model with 70 billion parameters. And TPU is very sensitive to batch size. For LLaMA 3 70B: May 3, 2024 · The output of Llama3’s response, formatted in LaTeX as our system request. Run purely on a dual GPU setup with no CPU offloading you can get around 54 t/s Meta-Llama-3-70B-Instruct-llamafile. This is the repository for the 70B pretrained model, converted for the Hugging Face Transformers format. A single A100 80GB wouldn’t be enough, although 2x A100 80GB should be enough to serve the Llama 2 70B You might be able to run a heavily quantised 70b, but I'll be surprised if you break 0. What is fascinating is how the smaller 8B version outperformed the bigger previus-gen 70B model in every benchmark listed on the model card: Llama 3 has also upped the context window size from 4k to 8k tokens. cpp, llama-cpp-python. Getting started with Meta Llama. Apr 25, 2024 · We will see that, while the memory consumption of Command-R+, Mixtral-8x22b, and Llama 3 70B is huge, there are several techniques to significantly reduce it, such as quantization and memory-efficient optimizers. Wait, I thought Llama was trained in 16 bits to begin with. These calculations were measured from the Model Memory Utility Space on the Hub. 8x. Deployment: Once fine-tuning is complete, you can deploy the model with a click of a button. If you want to find the cached configurations for Llama 3 70B, you can find them Apr 18, 2024 · Our new 8B and 70B parameter Llama 3 models are a major leap over Llama 2 and establish a new state-of-the-art for LLM models at those scales. Then, add execution permission to the binary: chmod +x /usr/bin/ollama. FSDP + Q-Lora + CPU offloading needs 4x24GB GPUs, with 22 GB/GPU and 127 GB CPU RAM with a sequence length of 3072 and a batch size of 1. Fine-Tune: Explain to the GPT the problem you want to solve using LLaMA 3. This model is the next generation of the Llama family that supports a broad range of use cases. Hardware requirements. At bfloat16 precision, a single model parameter requires 2 bytes of memory. Apr 18, 2024 · Llama 3 is a large language AI model comprising a collection of models capable of generating text and code in response to prompts. Apr 20, 2024 · LLaMa 3 70B, a 70-billion-parameter model with a knowledge cutoff of December 2023. May 4, 2024 · This approach effectively reduces the memory footprint to only the size of a single transformer layer, which, in the case of the LLaMa 3 70B model, is approximately 1. In case you use parameter-efficient Dec 4, 2023 · NVidia A10 GPUs have been around for a couple of years. 03 billion parameters, is small enough to run locally on consumer hardware. Llama 3 instruction-tuned models are fine-tuned and optimized for dialogue/chat use cases and outperform many of the available Apr 21, 2024 · Does Llama3’s breakthrough mean that open-source models have officially begun to surpass closed-source ones? Today we’ll also give our interpretation. Output Models generate text and code only. entrypoints. Developed by Saama AI Labs, this model leverages cutting-edge techniques to achieve state-of-the-art performance on a wide range of biomedical tasks. This environment offers a 4th Generation Intel® Xeon® CPU with 224 threads and 504 GB of memory, more than enough to run this code. On April 18, 2024, the AI community welcomed the release of Llama 3 70B, a state-of-the-art large language model (LLM). and max_batch_size of 1 and max_seq_length of 1024, the table looks like this now: Apr 22, 2024 · Generated with DALL-E. The training of Llama 3 70B with Flash Attention for 3 epochs with a dataset of 10k samples takes 45h on a g5. The response generation is so fast that I can't even keep up with it. Apr 18, 2024 · The most capable model. Apr 18, 2024 · 3. Tried to allocate X. has a maximum of 24 GB of VRAM. 3. Llama 2. Encodes language much more efficiently using a larger token vocabulary with 128K tokens. These impact the VRAM required (too large, you run into OOM. Double the context length of 8K from Llama 2. Meta Llama 3, a family of models developed by Meta Inc. 5t/s. Apr 18, 2024 · The Llama 3 release introduces 4 new open LLM models by Meta based on the Llama 2 architecture. As a close partner of Meta* on Llama 2, we are excited to support the launch of Meta Llama 3, the next generation of Llama models. Meta-Llama-3-8b: Base 8B model. If you are on Mac or Linux, download and install Ollama and then simply run the appropriate command for the model you want: Intruct Model - ollama run codellama:70b. That rules out almost everything except an A100 GPU which includes 40GB in the base model. Beyond that, I can scale with more 3090s/4090s, but the tokens/s starts to suck. Intentionally deceive or mislead others, including use of Meta Llama 3 related to the following: 1. openai. Notably, LLaMA3 models have recently been released and achieve impressive performance across various with super-large scale pre-training on over 15T tokens of data. Jul 18, 2023 · Memory requirements. LLM inference benchmarks show that performance metrics vary by hardware. This repository contains executable weights (which we call llamafiles) that run on Linux, MacOS, Windows, FreeBSD, OpenBSD, and NetBSD for AMD64 and ARM64. 4/18/2024. Use it if you need the fine-tuning to consume less memory. Jun 5, 2024 · LLama 3 Benchmark Across Various GPU Types. Now, you are ready to run the models: ollama run llama3. Variations Llama 2 comes in a range of parameter sizes — 7B, 13B, and 70B — as well as pretrained and fine-tuned variations. Quantized to 4 bits this is roughly 35GB (on HF it's actually as low as 32GB). The hardware requirements will vary based on the model size deployed to SageMaker. During training, optimizers also store gradients, and this is what explains that the actual memory usage is 2x - 3x bigger. Apr 18, 2024 · Llama 3 is also supported on the recently announced Intel® Gaudi® 3 accelerator. Mixtral runs circles around Llama2-70B and arguably ChatGPT-3. Only 70% of unified memory can be allocated to the GPU on 32GB M1 Max right now, and we expect around 78% of usable memory for the GPU on larger memory. For GPU inference and GPTQ formats, you'll want a top-shelf GPU with at least 40GB of VRAM. 48xlarge instance type, which has 192 vCPUs and 384 GB of accelerator memory. 5 bytes). Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content. Downloading and Running Llama 3 70b. Less than 1 ⁄ 3 of the false “refusals Dec 28, 2023 · I would like to run a 70B LLama 2 instance locally (not train, just run). May 23, 2024 · Llama 3 70B is a large model and requires a lot of memory. The most capable openly available LLM to date. com/2023/10/03/how-to-run-llms-locally-on-your-laptop-using-ollama/Unlock the power of AI right from your lapt Variations Llama 2 comes in a range of parameter sizes — 7B, 13B, and 70B — as well as pretrained and fine-tuned variations. #Allow git download of very large files; lfs is for git clone of very large files, such Apr 22, 2024 · Meta's LLaMA family has become one of the most powerful open-source Large Language Model (LLM) series. We would like to show you a description here but the site won’t allow us. For GPU inference, using exllama 70B + 16K context fits comfortably in 48GB A6000 or 2x3090/4090. We are going to use the inf2. If we quantize Llama 2 70B to 4-bit precision, we still need 35 GB of memory (70 billion * 0. Check their docs for more info and example prompts. This is the repository for the base 70B version in the Hugging Face Transformers format. The system will recommend a dataset and handle the fine-tuning. max_seq_len 16384. Specifically, we evaluate the 10 existing post-training quantization and LoRA-finetuning methods of LLaMa3 on 1-8 bits and diverse datasets to comprehensively reveal LLaMa3's low-bit quantization performance. May 3, 2024 · The output of Llama3’s response, formatted in LaTeX as our system request. Llama 3 has Apr 18, 2024 · Variations Llama 3 comes in two sizes — 8B and 70B parameters — in pre-trained and instruction tuned variants. Code Llama. ADMIN MOD. Input Models input text only. Thus, simply loading 70-billion parameters of Llama2-70B will require 140GB of device memory. Modify the Model/Training. cpp quantizes to 4-bit, the memory requirements are around 4 times smaller than the original: 7B => ~4 GB. In this blog post we will show how to Some of the steps below have been known to help with this issue, but you might need to do some troubleshooting to figure out the exact cause of your issue. are new state-of-the-art , available in both 8B and 70B parameter sizes (pre-trained or instruction-tuned). Jun 3, 2024 · Ollama is a powerful tool that allows users to run open-source large language models (LLMs) on their local machines efficiently and with minimal setup. In the model section, select the Groq Llama 3 70B in the "Remote" section and start prompting. They come in two sizes: 8B and 70B parameters, each with base (pre-trained) and instruct-tuned versions. Apr 18, 2024 · Intel Gaudi 2 accelerators have optimized performance on Llama 2 models – 7B, 13B, and 70B parameters – and now have initial performance measurements for the new Llama 3 model. XX GiB . Our experiment results indicate that LLaMa3 still suffers non-negligent degradation in these scenarios, especially in ultra-low bit-width. Generating, promoting, or furthering fraud or the creation or promotion of disinformation. PyTorch FSDP is a data/model parallelism technique that shards model across GPUs, reducing memory requirements and enabling the training of larger models more efficiently . Additionally, you will find supplemental materials to further assist you while building with Llama. Reduce the `batch_size`. The four models address different serving and latency requirements. Given the wide application of low-bit quantization for LLMs in resource-limited scenarios, we explore LLaMA3's capabilities when Dec 1, 2023 · Fine-tuning large language models (LLMs) with billions of parameters such as Llama2-70B is a challenging task that demands huge memory and high computational resources. On this page. For the CPU infgerence (GGML / GGUF) format, having enough RAM is key. I can tell you form experience I have a Very similar system memory wise and I have tried and failed at running 34b and 70b models at acceptable speeds, stuck with MOE models they provide the best kind of balance for our kind of setup. Model variants We’ve integrated Llama 3 into Meta AI, our intelligent assistant, that expands the ways people can get things done, create and connect with Meta AI. Mar 26, 2024 · GPU Memory Required for Serving Llama 70B. Llama 3 is currently available in two versions: 8B and 70B. You can run this tutorial on the Intel® Tiber® Developer Cloud free JupyterLab* environment. The exact requirements are not specified, but it's clear that Apr 23, 2024 · We are now looking to initiate an appropriate inference server capable of managing numerous requests and executing simultaneous inferences. Apr 19, 2024 · The WOQ Llama 3 will only consume ~10GB of RAM, meaning we can free ~50GB of RAM by releasing the full model from memory. Fine-tuning. “Documentation” means the specifications, manuals and documentation accompanying Meta Llama 3 distributed by Mar 21, 2023 · Hence, for a 7B model you would need 8 bytes per parameter * 7 billion parameters = 56 GB of GPU memory. Once Ollama is installed, open your terminal or command prompt and run the following command: ollama run llama3:70b. It loads entirely! Remember to pull the latest ExLlama version for compatibility :D. Apr 18, 2024 · Variations Llama 3 comes in two sizes — 8B and 70B parameters — in pre-trained and instruction tuned variants. . Model Architecture Llama 2 is an auto-regressive language model that uses an optimized transformer architecture. 30B => ~16 GB. Apr 27, 2024 · Click the next button. Full finetuning is slow and memory-hungry. I run llama2-70b-guanaco-qlora-ggml at q6_K on my setup (r9 7950x, 4090 24gb, 96gb ram) and get about ~1 t/s with some variance, usually a touch slower. Conclusion. 2 = 168 GB. Lower the Precision. Step 1: Enable Git to Download Large Files. Can it entirely fit into a single consumer GPU? This is challenging. The models come in both base and instruction-tuned versions designed for dialogue applications. Reply reply. You can adjust these hyperparameters based on your specific requirements. Let’s calculate the GPU memory required for serving Llama 70B, loading it in 16 bits. Meta developed and released the Meta Llama 3 family of large language models (LLMs), a collection of pretrained and instruction tuned generative text models in 8 and 70B sizes. To get it down to ~140GB you would have to load it in bfloat/float-16 which is half-precision, i. I'd like to run it on GPUs with less than 32GB of memory. Aug 31, 2023 · For 65B and 70B Parameter Models. 12xlarge. Full parameter fine-tuning is a method that fine-tunes all the parameters of all the layers of the pre-trained model. Resources. They are much cheaper than the newer A100 and H100, however they are still very capable of running AI workloads, and their price point makes them cost-effective. Mar 15, 2024 · Big thank you to Peter for the helpful guide through llama. Below is a set up minimum requirements for each model size we tested. Apr 19, 2024 · Available in both 8B and 70B configurations, LLaMA-3 showcases improvements over its predecessor with enhancements in tokenizer efficiency and attention mechanisms, promising superior performance Dec 4, 2023 · This reduces model capacity requirements and improves the effective memory bandwidth for operations that interact with the model state by 1. Model variants We would like to show you a description here but the site won’t allow us. Llama2 70B GPTQ full context on 2 3090s. For Llama 2, 70B parameters, the performance decrease is as low as 4%. Replace llama3-8b with llama3-70b to fine-tune the larger 70B model. The primary advantage is that you can spec out more memory with the M3 Max to fit larger models, but with the exception of CodeLlama-70B today, it really seems like the trend is for models to be getting smaller and better, not bigger. With parameter-efficient fine-tuning (PEFT) methods such as LoRA, we don’t need to fully fine-tune the model but instead can fine-tune an adapter on top of it. I made a notebook that can automatically estimate the memory consumption of a transformer model for inference and fine-tuning. With input length 100, this cache = 2 * 100 * 80 * 8 * 128 * 4 = 30MB GPU memory. alpha_value 4. To deploy Llama 3 70B to Amazon SageMaker we create a HuggingFaceModel model class and define our endpoint configuration including the hf_model_id, instance_type etc. The performance of rotary positional embedding (RoPE) operations—state-of-the-art algorithms employed by many recent LLM architectures—has also increased. 6GB — a mere fraction of Apr 18, 2024 · Variations Llama 3 comes in two sizes — 8B and 70B parameters — in pre-trained and instruction tuned variants. Token counts refer to pretraining data We would like to show you a description here but the site won’t allow us. We will load the model in the most optimal way currently possible but it still requires at least 35GB of GPU memory. Sep 27, 2023 · Llama 2 70B is substantially smaller than Falcon 180B. All the variants can be run on various types of consumer hardware and have a context length of 8K tokens. The 34B and 70B models return the best results and allow for better coding assistance, but the smaller 7B and 13B models are faster and more suitable for tasks that require low latency, like real-time code completion. This command fine-tunes the Llama 3 8B model on the specified dataset, using a learning rate of 1e-5, a batch size of 8, and running for 5 epochs. za dv ha iz xr fo et yv ax rn