Bespoke Labs
  • Welcome
  • BESPOKE CURATOR
    • Getting Started
      • Quick Tour
      • Key Concepts
      • Visualize your dataset with the Bespoke Curator Viewer
      • Automatic recovery and caching
      • Structured Output
    • Save $$$ on LLM inference
      • Using OpenAI for batch inference
      • Using Anthropic for batch inference
      • Using Gemini for batch inference
      • Using Mistral for batch inference
      • Using kluster.ai for batch inference
    • How-to Guides
      • Using vLLM with Curator
      • Using Ollama with Curator
      • Using LiteLLM with curator
      • Handling Multimodal Data in Curator
      • Executing LLM-generated code
      • Using HuggingFace inference providers with Curator
    • Data Curation Recipes
      • Generating a diverse QA dataset
      • Using SimpleStrat block for generating diverse data
      • Curate Reasoning data with Claude-3.7 Sonnet
      • Synthetic Data for function calling
    • Finetuning Examples
      • Aspect based sentiment analysis
      • Finetuning a model to identify features of a product
    • API Reference
  • Models
    • Bespoke MiniCheck
      • Self-Hosting
      • Integrations
      • API Service
    • Bespoke MiniChart
    • OpenThinker
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  1. BESPOKE CURATOR

Finetuning Examples

In these examples, we demonstrate how to use synthetically generated data to finetune small LLMs and get better performance than SOTA big LLMs.

  • Aspect based sentiment analysis

  • Finetuning a model to identify features of a product

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Last updated 13 days ago