selfrag_llama2_7b
| Property | Value |
|---|---|
| License | MIT |
| Framework | PyTorch |
| Paper | arXiv:2310.11511 |
| Training Infrastructure | 8 A100 40GB GPUs |
What is selfrag_llama2_7b?
selfrag_llama2_7b is an innovative language model built on the LLaMA 2 architecture that implements the Self-RAG (Retrieve, Generate, and Critique) methodology. This model uniquely combines text generation capabilities with self-reflection mechanisms, allowing it to not only generate responses but also critically evaluate its own output and retrieved information.
Implementation Details
The model is trained using a specialized instruction-following corpus that incorporates interleaved passages and reflection tokens. It utilizes a standard next-token prediction objective for efficient and stable learning with fine-grained feedback. The implementation supports both standalone text generation and retrieval-augmented generation with passage integration.
- Built on LLaMA 2 7B base architecture
- Implements reflection tokens for self-criticism
- Supports efficient inference through vllm
- Features adaptive retrieval system integration
Core Capabilities
- Adaptive retrieval calling based on query requirements
- Self-reflection and output criticism
- Passage integration with specialized formatting
- Fine-grained tree decoding for optimal output selection
- Support for both factual and non-factual query processing
Frequently Asked Questions
Q: What makes this model unique?
The model's distinctive feature is its ability to generate reflection tokens that enable self-criticism and adaptive retrieval. It can determine when to retrieve additional information and how to incorporate it effectively into responses, making it more reliable for both factual and analytical tasks.
Q: What are the recommended use cases?
The model is particularly well-suited for applications requiring fact-based responses with self-verification, educational contexts where explanation quality is crucial, and scenarios where adaptive information retrieval would enhance response accuracy. It can handle both direct question-answering and complex analytical tasks.
