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I found this a useful guide to understanding LLMs and that they are all one thing it is just that to use them for a particular purpose such as running at the Edge you would likely want to optimise them to remove those parts which are not needed for your intended purpose.
An LLM for handling Macca’s drive through orders would likely not need the ability to discuss quantum mechanics:
Pankaj Pandey
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Jan 13, 2024
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Photo by 1981 Digital on Unsplash
Large Language Models (LLMs) hold immense potential to revolutionize diverse workflows, but unlocking their full potential for specific use cases requires thoughtful optimization.
Below are some guides for developers looking to optimize LLMs for their own use cases:
Customize a model with Azure OpenAI Service — Azure OpenAI | Microsoft Learn
Please note: This guide provides a general framework. Specific steps and tools may vary depending on your chosen LLM, framework and use case.”
This is what Brainchip’s engineers and scientists have been doing that Dr. Lewis described as achieving SOTA performance.
My opinion only DYOR
Fact Finder
I found this a useful guide to understanding LLMs and that they are all one thing it is just that to use them for a particular purpose such as running at the Edge you would likely want to optimise them to remove those parts which are not needed for your intended purpose.
An LLM for handling Macca’s drive through orders would likely not need the ability to discuss quantum mechanics:
“Optimizing LLMs for Your Use Cases: A Developer’s Guide
Pankaj Pandey
·
Follow
4 min read
·
Jan 13, 2024
Listen
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Photo by 1981 Digital on Unsplash
Large Language Models (LLMs) hold immense potential to revolutionize diverse workflows, but unlocking their full potential for specific use cases requires thoughtful optimization.
Below are some guides for developers looking to optimize LLMs for their own use cases:
1. Analyze Your Use Case:
- Define the task: Identify the specific task you want the LLM to perform (e.g., code generation, text summarization, question answering).
- Data analysis: Assess the type and size of data available for training and fine-tuning. Is it curated, labeled and relevant to your specific domain?
- Evaluation metrics:Determine how you’ll measure success. Are you aiming for accuracy, speed, creativity or something else?
2. LLM Selection and Fine-tuning:
- Model selection: Choose an LLM with capabilities aligning with your task. Consider pre-trained models for your domain may help you do this task easily.
For Example, choose GPT-3 as it excels in natural language understanding and generation, which aligns well with the conversational nature of the customer support chatbot. - Fine-tuning: Adapt the LLM to your specific data using transfer learning. Popular frameworks like Hugging Face offer tools and tutorials for fine-tuning.
Fine-tune GPT-3 using a dataset of customer support interactions. Provide examples of user queries and corresponding responses to help the model understand the specific context and language used in the customer support domain. - Hyperparameter optimization: Adjust settings like learning rate, batch size and optimizer to maximize performance on your data. Consider using automated Hyperparameter Optimization (HPO) tools.
Experiment with smaller variants of GPT-3 or adjust hyperparameters to find the right balance between model size and performance. For a latency-sensitive application like customer support, a smaller model might be preferred.
3. Data Wrangling and Augmentation:
- Data quality: Ensure data cleanliness and relevance. Label inconsistencies, biases and irrelevant examples can negatively impact performance.
Apply quantization to reduce model precision, making it more efficient. Prune unnecessary connections to create a more compact model without compromising performance. - Data augmentation: Artificially expand your data with techniques like synonym substitution, back-translation or paraphrasing to improve model generalization.
- Active learning: Interactively query the LLM to identify informative data points for further labeling, focusing resources on areas where the model needs improvement.
4. Integration and Deployment:
- API integration: Connect the LLM to your application or workflow through APIs offered by platforms like OpenAI or Google Cloud AI.
- Latency optimization: Optimize resource allocation and inference techniques to minimize response time and improve user experience.
- Monitoring and feedback: Continuously monitor model performance and gather feedback from users. Use this data to further refine the LLM and iterate on your solution.
5. Caching and Memorization:
- Implement caching and memorization strategies to store and reuse intermediate results during inference. This can significantly reduce redundant computations and improve response times.
- Implement caching of frequently used responses. For commonly asked questions, store and reuse the model’s previous outputs to reduce redundant computations and improve response times.
6. User Feedback Loop:
- Establish a feedback loop with end-users to understand their experience and gather insights for further optimization. User feedback can help refine the model and identify areas for improvement.
For Example, gather feedback from users regarding the effectiveness of the chatbot’s responses. Use this feedback to identify areas for improvement, update the model accordingly and enhance the overall user experience.
Additional Tips:
- Consider interpretability: Choose LLMs with built-in explainability features to understand their reasoning and build trust with users.
- Utilize transfer learning techniques: Leverage pre-trained knowledge from similar tasks to accelerate development and improve performance.
- Collaborate with the LLM community: Stay informed about advances in LLM research and best practices, participate in forums and contribute your findings.
Helpful Resources:
Fine-tuning — OpenAI APICustomize a model with Azure OpenAI Service — Azure OpenAI | Microsoft Learn
Please note: This guide provides a general framework. Specific steps and tools may vary depending on your chosen LLM, framework and use case.”
This is what Brainchip’s engineers and scientists have been doing that Dr. Lewis described as achieving SOTA performance.
My opinion only DYOR
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