How to Generate Structured Output with LLM?
Do your products or use cases need structured output?
Are you seeking convenient ways to coordinate multiple LLM steps for complex agent workflows?
If so, you might consider using SLIM models and "function calling".
🤩What are SLIM models?
SLIMs are small, specialized models crafted for natural language classification tasks. They are trained to generate programmatic outputs such as Python dictionaries, JSON, and SQL, rather than traditional text outputs.
There are 10 SLIM models available: Sentiment, NER (Named Entity Recognition), Topic, Ratings, Emotions, Entities, SQL, Category, NLI (Natural Language Inference), and Intent.
🚀Why SLIM Models?
SLIMs offer several benefits for enterprise deployment:
They modernize traditional bespoke classifiers, seamlessly integrating with LLM-based processes.
They follow a consistent training approach, enabling easy combination, stacking, and fine-tuning for specific use cases.
With quantized versions available, SLIM models allow multi-step workflows without the need for a GPU, facilitating the creation of agents and utilization of state-of-the-art question-answering DRAGON LLMs.
Difference between function calling and agents?
Agents handle complex workflows requiring multiple steps and LLM calls orchestration.
Function Calls involve generating structured output programmatically, essential for tasks like classification and clustering, often serving as the linchpin in such workflows.
Curious to delve deeper into this?
Join Professor Mehdi as he delves into SLIM models, discussing the technique, its pros and cons for production, in the video below!👇
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Source of images/quotes:
Huggingface: https://huggingface.co/llmware
🔨 Implementation: Colab: https://colab.research.google.com/drive/1MFU9jjCVrM4UKcv6urEfCthr8G8Rj70X?usp=sharing
📚 Also if you'd like to learn more about RAG systems, check out our book on the RAG system:
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