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Fine-tuning with QA Datasets

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FedRAG

VectorInsitute/fed-rag

Home
Getting started
Getting started
- Essentials
- Quick Starts
  Quick Starts
- Installation
- Standard Usage
- Import Patterns
- Tutorials
  Tutorials
  - Retriever Fine-tuning with LSR
  - Generator Fine-tuning with RALT
- Integrations
Case Studies
Case Studies
- RA-DIT
  RA-DIT
Notebooks
Notebooks
- Basic Starter Example
- Benchmarking RAG Systems (MMLU)
- Integrations
  Integrations
  - Using LlamaIndex for Inference
  - Using Qdrant for Knowledge Storage
API Reference
API Reference
- Bridges
  Bridges
  - LlamaIndex
- Data Collators
  Data Collators
  - Huggingface
- Data Structures
  Data Structures
  - Bridge
  - Evals
  - Knowledge Node
  - RAG
  - Results
- Decorators
  Decorators
- Evals
  Evals
  - Benchmarker
  - Benchmarks
    Benchmarks
    
    HuggingFace
    HuggingFace
    
    MMLU
  - Metrics
    Metrics
    
    Exact Match
- Exceptions
  Exceptions
  - Bridge
  - Evals
  - FL Tasks
  - Inspectors
  - Knowledge Stores
  - Loss
  - RAG Trainer
- Finetuning Datasets
  Finetuning Datasets
  - Pytorch
  - Huggingface
- FL Tasks
  FL Tasks
  - Pytorch
  - Huggingface
- Generators
  Generators
  - Huggingface
- Inspectors
  Inspectors
  - Pytorch
  - Huggingface
- Knowledge Nodes
  Knowledge Nodes
- Knowledge Stores
  Knowledge Stores
  - InMemory
  - Qdrant
  - Mixins
- Loss
  Loss
  - Pytorch
- RAG System
  RAG System
- RAG Trainer Managers
  RAG Trainer Managers
  - Pytorch
  - Huggingface
- Retrievers
  Retrievers
  - Huggingface
- Tokenizers
  Tokenizers
  - Huggingface
- Trainers
  Trainers
  - Pytorch
  - Huggingface
Community
Community
- Contributing
  Contributing
- Changelog
- Resources
  Resources
  - AI Pocket References

Fine-tuning with QA Datasets¶

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