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LLM_BENCHMARKS_TEXT = f"""
# 🧰 Train a Model
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 Powered-by-Intel LLM 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® Data Center GPU Max 1100
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:
- Intel® Gaudi® Accelerators
- [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)
- Intel® 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)
- Intel® Data Center GPU Max Series
- [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
""" |