Train you own model

One of the advantages of using open-source software is that it allows you to optimize and fine-tune your models, which means that they perform better than their counterparts in commercial products, which must solve every task with a predetermined output.

There are several popular frameworks for training LLMs using open-source libraries such as PyTorch and TensorFlow. Some notable examples include GPT (Generative Pre-trained Transformer), BERT (Bidirectional Encoder Representations from Transformers), and RoBERTa (A Robustly Optimized BERT Pretraining Approach). These frameworks are typically designed to be modular and flexible, allowing you to customize the model architecture, training parameters, and evaluation metrics to fit your specific requirements. To get started with a framework, you can find tutorials online or reach out to a machine learning expert for guidance.

When working with LLMs, there are several key considerations to keep in mind when designing and implementing the model architecture. The first step is to select the right data format and preprocessing pipeline to use during training. This will depend on the task at hand and the type of input required by your model. Once you have identified the appropriate data format, you can start the training process using one of the aforementioned frameworks.

During training, it is important to monitor various metrics such as loss, accuracy, and performance over time to ensure that the model is improving. As the training progresses, you may need to adjust various parameters like learning rate, batch size, and hyperparameters to optimize the model's performance. Additionally, during evaluation, you should consider different types of tasks such as sentiment analysis, entity recognition, and question-answering to test the model's capabilities in real-world scenarios.

Example on:

MixTAO-7Bx2-MoE is a Mixure of Experts (MoE). This model is mainly used for large model technology experiments, and increasingly perfect iterations will eventually create high-level large language models.

• normalized accuracy on AI2 Reasoning Challenge (25-Shot)

  test setOpen LLM Leaderboard

• normalized accuracy on HellaSwag (10-Shot)

  validation setOpen LLM Leaderboard

• accuracy on MMLU (5-Shot)

  test setOpen LLM Leaderboard

• mc2 on TruthfulQA (0-shot)

  validation setOpen LLM Leaderboard

• accuracy on Winogrande (5-shot)

  validation setOpen LLM Leaderboard

• accuracy on GSM8k (5-shot)

  test setOpen LLM Leaderboard