| LLM_BENCHMARKS_TEXT = f""" | |
| # Use the Resources Below to Start Training a Model Today | |
| Intel offers a variety of platforms that can be used to train LLMs including datacenter and consumer grade cpus, gpus, and ASICs. | |
| Below, you'll find documentation on how to access free and paid resources to train a model and submit it to the "Intel Inside Leaderboard". | |
| ## Intel Developer Cloud - Quick Start | |
| The Intel Developer Cloud is one of the best places to access free and paid compute instances for model training. Intel offers Jupyter Notebook instances supported by | |
| 224 Core 4th Generation Xeon Baremetal nodes with 4x Max Series GPU 1100 GPUs. To access these resources please follow the instructions below: | |
| 1. Visit [cloud.intel.com](cloud.intel.com) and create a free account. | |
| 2. Navigate to the "Training" module under the "Software" section in the left panel | |
| 3. Under the GenAI Essentials section, select the LLM Fine-Tuning with QLoRA notebook and click "Launch" | |
| 4. Follow the instructions in the notebook to train your model using Intel Max Series 1100 GPUs | |
| 5. Upload your model to the Hugging Face Model Hub | |
| 6. Go to the "Submit" tab follow instructions to create a leaderboard evaluation request | |
| ## Additional Training Code Samples | |
| Below you will find a list of additional resources for training models on different intel hardware platforms: | |
| - Gaudi Processors | |
| - [Parameter Efficient Fine-Tuning of Llama-2 70B](https://github.com/HabanaAI/Gaudi-tutorials/blob/main/PyTorch/llama2_fine_tuning_inference/llama2_fine_tuning_inference.ipynb) | |
| - Xeon Processors | |
| - [Distributed Training of GPT2 LLMs on AWS](https://github.com/intel/intel-cloud-optimizations-aws/tree/main/distributed-training) | |
| - [Fine-tuning Falcon 7B on Xeon Processors](https://medium.com/@eduand-alvarez/fine-tune-falcon-7-billion-on-xeon-cpus-with-hugging-face-and-oneapi-a25e10803a53) | |
| - Max Series GPUs | |
| - [LLM Fine-tuning with QLoRA on Max Series GPUs](https://console.idcservice.net/training/detail/159c24e4-5598-3155-a790-2qv973tlm172) | |
| ## Submitting your Model to the Hub | |
| Once you have trained your model, it is a straighforward process to upload and open source it on the Hugging Face Hub. | |
| ```python | |
| # Logging in to Hugging Face | |
| from huggingface_hub import notebook_login, Repository | |
| # Login to Hugging Face | |
| notebook_login() | |
| # Model and Tokenize Loading | |
| from transformers import AutoModelForSequenceClassification, AutoTokenizer | |
| # Define the path to the checkpoint | |
| checkpoint_path = "" # Replace with your checkpoint folder | |
| # Load the model | |
| model = AutoModelForSequenceClassification.from_pretrained(checkpoint_path) | |
| # Load the tokenizer | |
| tokenizer = AutoTokenizer.from_pretrained("") #add name of your model's tokenizer on Hugging Face OR custom tokenizer | |
| #Saving and Uploading the Model and Tokenizer | |
| # Save the model and tokenizer | |
| model_name_on_hub = "desired-model-name" | |
| model.save_pretrained(model_name_on_hub) | |
| tokenizer.save_pretrained(model_name_on_hub) | |
| # Push to the hub | |
| model.push_to_hub(model_name_on_hub) | |
| tokenizer.push_to_hub(model_name_on_hub) | |
| # Congratulations! Your fine-tuned model is now uploaded to the Hugging Face Model Hub. | |
| # You can view and share your model using its URL: https://huggingface.co./your-username/your-model-name | |
| ``` | |
| """ | |
| SUBMIT_TEXT = f""" | |
| # Use the Resource Below to Start Training a Model Today | |
| """ |