Unlock AI Agents, Function Calls and Multi-Step RAG with LLMWare

Introducing SLIMs (Structured Language Instruction Models) - small, specialized, function-calling (1B parameter) LLMs that have been carefully fine-tuned to provide structured outputs that can be handled programmatically. SLIMs are designed to be stacked and mixed with other LLMs in a multi-step workflow and can handle the following functions: 1. Sentiment 2. Named Entity Recognition 3. Topic 4. Ratings 5. Emotions 6. Entities 7. SQL 8. Category 9. Natural Language Inference 10. Intent But these aren't just another set of models; they have something special. SLIM models produce programmatic outputs like Python dictionaries, JSON, and SQL. They will become crucial for building multi-step agent workflows that require structured output. Learn how we use a combination of these models in a workflow to assess customer service complaint to produce structured output to unlock limitless use cases. Please subscribe for more content. Also check us out on our open source library on Github and leave a star! https://github.com/llmware-ai/llmware Check out our models on Hugging Face: https://huggingface.co/llmware. Join our Discord: https://discord.gg/sgPz8988bE