- More accurate outputs compared to basic prompts
- The ability to train with larger datasets beyond a single prompt
- Reduced token usage by minimizing prompt length
- Faster response times due to optimized requests
- Prepare and upload your training data
- Train open source models available on our platform
- Evaluate the results and iterate if necessary
- Start using your fine-tuned model for optimized performance
Models Supported on Lumino for Fine-Tuning
- Llama 3.1 8B
- Llama 3.1 70B
When to Consider Fine-Tuning
Fine-tuning is a powerful tool for those who require models to handle highly specific tasks or operate within particular constraints. However, before jumping into fine-tuning, we suggest leveraging prompt engineering and modular prompt chaining techniques. Here’s why:- Many tasks can be solved with smart prompt configurations, reducing the need for training new models.
- Iterating on prompts is faster than running full fine-tuning cycles, allowing for quicker feedback and adjustments.
- Prompt engineering work can complement fine-tuning, as well-structured prompts often enhance the quality of the fine-tuning process.
Common Use Cases for Fine-Tuning
Here are some scenarios where fine-tuning can significantly enhance model performance:- Task-specific adaptation: Fine-tuning general-purpose models like Llama or BERT for specific tasks such as question answering, text classification, or summarization.
- Domain specialization: Adapting models to perform well in specific industries or fields, like legal, medical, or scientific domains.
- Language localization: Improving performance on languages or dialects that weren’t well-represented in the original training data.
- Company-specific knowledge integration: Incorporating proprietary information or domain expertise into the model.
- Low-resource applications: Adapting models for languages or domains with limited available data.
- Improved few-shot learning: Enhancing a model’s ability to perform well on new tasks with minimal examples.
- Multimodal applications: Fine-tuning models to work with combinations of text, images, or other data types.
- Customized outputs: Set specific tones, formats, or styles that align with your brand or project needs.
- Consistency in complex tasks: Achieve a higher level of reliability when managing tasks with intricate or multi-step processes.
- Specialized behaviors: Teach the model to handle rare edge cases or new tasks that can’t easily be addressed with a generic prompt.