Data Quality

  • Relevance: Ensure the data is relevant to the tasks your model will perform. For coding models, for instance, include various programming languages, frameworks, and problem-solving scenarios.

  • Accuracy: Data should be accurately labeled or annotated. This is crucial in supervised learning scenarios where the model learns directly from the dataset labels.

Data Diversity

  • Variety: Include a wide range of examples to cover different cases the model might encounter. For a coding model, this might mean different coding styles, libraries, and even code snippets with common errors to enhance error recognition capabilities.

  • Balance: Avoid biases by balancing the dataset. For example, if training a multilingual model, ensure all languages are adequately represented.

Data Volume

  • Scalability: More data generally leads to better model performance, but it’s crucial to balance quantity with quality. Ensure that the increase in data volume doesn’t dilute data quality.

  • Incremental Training: Start with a smaller, high-quality dataset to observe performance improvements before scaling up. This approach helps in identifying which types of data are most beneficial.

Data Formatting

  • Consistency: Keep data formats consistent, especially if you are aggregating data from multiple sources. Consistency in formatting helps in minimizing preprocessing errors.

  • Preprocessing: Clean and preprocess data to remove unnecessary or misleading information. This might include stripping irrelevant metadata or converting all data to a uniform format.

Use of Synthetic Data

  • Augmentation: In cases where data is scarce, consider using synthetic data generation techniques to augment your datasets. This can be particularly useful for edge cases or rare scenarios.

  • Validation: Ensure that synthetic data is realistic and validated against real-world criteria to avoid introducing biases or unrealistic scenarios into the training process.

Legal and Ethical Considerations

  • Compliance: Ensure that the dataset complies with data protection regulations (like GDPR in Europe) especially if it includes personally identifiable information.

  • Ethics: Consider the ethical implications of your data collection and usage. Ensure the data does not reinforce stereotypes or biases.

Tools and Frameworks

  • Automation Tools: Utilize tools for data scraping, cleaning, and annotation to streamline the data preparation process. Libraries like Beautiful Soup for web scraping or Pandas for data manipulation are very helpful.

  • Dataset Libraries: Leverage existing datasets available on platforms like Hugging Face or Kaggle as a starting point or as augmentation to your proprietary data.

Feedback Loop

  • Continuous Improvement: Use model performance feedback to continuously improve the dataset. Identify areas where the model underperforms and augment the dataset accordingly.

Data Structuring for Conversational Models

  1. Role Naming Flexibility: As discussed in your conversation, the ability to flexibly define roles like "User" and "Assistant" instead of the standard "human" and "gpt" can be beneficial. This approach can align the data more closely with the specific conversational roles used in your application. Implementing custom fields such as field_human and field_model in your configuration can allow for this flexibility without needing significant code modifications.

  2. Using YAML for Custom Configurations:

    • For integrating diverse data formats or custom naming conventions, leveraging YAML configurations can allow you to specify how each part of your dataset should be interpreted by the training framework. This can include renaming fields or defining specific preprocessing behaviors.

  3. Conversation Structure:

    • Ensure that your data mimics the conversational flow you expect the model to handle post-training. This includes the correct sequencing of prompts and responses and embedding contextual cues when necessary.

  4. Inclusion of System Prompts:

    • If your model needs to handle system-driven interactions (like instructions or prompts given by a system before a user input), including these in the training data can help the model learn appropriate responses based on the system's prompts.

Data Annotation and Format

  1. Annotation Guidelines:

    • Maintain clear and consistent annotation guidelines to ensure that the data is uniformly annotated. This consistency is crucial when the data involves subjective judgments or when multiple annotators are involved.

  2. Format Consistency:

    • Ensure all data shares a consistent format, especially if aggregated from multiple sources. This can involve standardizing the text encoding, the way dialogues are structured, or how metadata is attached.

Utilizing Metadata and Advanced Formatting

  1. Metadata Usage:

    • Include relevant metadata in your training datasets to allow models to utilize contextual information which could improve their understanding and generation capabilities. This can include information like the timestamp of a conversation, the platform it was captured from, or the geographical location involved.

  2. Advanced Formatting Techniques:

    • Consider using advanced text formatting techniques like embedding hidden context or control codes that can guide the model’s responses in subtle ways. This might involve coding certain behavioral traits or operational modes directly into the training data.

Validation and Testing

  1. Dataset Splitting:

    • Ensure that your data is split appropriately into training, validation, and testing sets. This helps in tuning the model on realistic data scenarios and validating its performance on unseen data.

  2. Dynamic Dataset Updates:

    • Implement mechanisms to update the dataset dynamically as new data becomes available or as the model’s application domain evolves. This can involve periodically retraining the model on updated datasets or incrementally training on new data.

Semantic Consistency

  • Ensure the data remains semantically consistent after transformations or style changes. This is crucial when rewriting texts in different styles to maintain the original meaning.

2. Balanced Representation

  • Strive for a balanced representation of styles, topics, and lengths to avoid model bias towards any specific style or subject matter.

3. Iterative Refinement

  • Use iterative refinement of data samples based on model feedback. This could involve adjusting the style intensity or clarity based on how well models perform with initial datasets.

4. Contextual Richness

  • For RAG tasks, enrich the dataset with diverse contexts that can help the model learn to retrieve and integrate relevant information effectively. This can include adding background information, related facts, or even conflicting viewpoints to enhance learning depth.

5. Dynamic Sampling

  • Implement dynamic sampling techniques to expose the model to a variety of data samples during training sessions. This helps in improving generalization by preventing overfitting on specific styles or patterns.

6. Quality Control Mechanisms

  • Establish robust quality control mechanisms to regularly assess and ensure the quality of data being fed into the model. This includes checking for data corruption, mislabeling, and ensuring stylistic accuracy in case of style-based rewriting tasks.

7. Automated and Manual Annotations

  • Combine automated tools with manual review to annotate data, especially for complex tasks like style transfer where nuanced understanding of style and content is necessary.

8. Data Augmentation

  • Use data augmentation techniques judiciously to expand dataset size without compromising quality, such as paraphrasing, back-translation, or synthetic data generation.

9. Feedback Loops

  • Create feedback loops where initial model outputs are reviewed and corrections are fed back into the training regime to continually refine the model's understanding and generation capabilities.

10. Use of External Knowledge Bases

  • For RAG implementations, integrate external knowledge bases during training to simulate how the model should perform retrieval tasks during actual deployment. This can help the model learn to pull in relevant information when needed.

11. Preprocessing Pipelines

  • Develop robust preprocessing pipelines that standardize data before it enters the training workflow, ensuring consistency in how data is handled and reducing the chance of errors during model training.

12. Experimentation with Tokenization

  • Experiment with different tokenization strategies to understand their impact on the model’s ability to understand and generate text accurately, especially for different languages or specialized vocabularies.

. Structured JSONL Format

  • Utilize structured JSONL format for your datasets as it facilitates easier parsing and manipulation of the data. This format allows you to represent conversation turns explicitly, which can be directly utilized by models without additional preprocessing.

2. Role-based Data Entries

  • Define clear roles within your dataset entries, such as "user" and "assistant", to help the model learn context-specific responses. This is particularly useful in dialog systems where maintaining the role context is crucial.

3. Incorporating External Knowledge

  • Consider integrating external knowledge sources directly into your dataset to enhance the model's ability to perform knowledge-based tasks. This can be done through links to additional resources or embedding supplementary data within the dataset entries.

4. Multi-format Support

  • Prepare your datasets in multiple formats to test the flexibility of your model across different data handling frameworks. This includes variations in tokenization, annotation styles, and data structuring.

5. Dynamic Data Generation

  • Use models to generate dynamic training data based on existing patterns. This can involve creating variations of data samples through model-generated paraphrasing, summarization, or style transfer.

6. Simulating Real-World Scenarios

  • Structure your data to simulate real-world usage scenarios as closely as possible. This includes creating datasets that reflect the diversity of real-world applications, from casual conversations to technical support scenarios.

8. Version Control and Experiment Tracking

  • Maintain version control for your datasets and use experiment tracking tools to log how changes in data affect model performance. This helps in understanding the impact of specific data adjustments and optimizing the training process.

9. Utilizing Instruction-Based Tuning

  • Leverage instruction-based tuning where the dataset is enriched with explicit instructions that guide the model's output. This is particularly effective in scenarios where the model needs to perform specific tasks based on varied inputs.

10. Quality and Consistency Checks

  • Regularly perform quality checks and consistency assessments on your datasets to ensure high data quality and reliability. This can include automated scripts to detect anomalies and manual reviews to ensure contextual accuracy.

11. Adaptive Learning

  • Design datasets that enable adaptive learning, where data samples are selected and utilized based on the model’s current learning state. This helps in focusing training on areas where the model is underperforming.

12. Explorative Data Analysis

  • Before finalizing your dataset, conduct explorative data analysis to understand the data's characteristics fully. This can help in identifying potential biases, imbalances, or other issues that could impact training effectiveness.

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This documentation is for the Axolotl community