langleu/qmd-query-expansion-lfm2.5-1.2b-instruct-v2
langleu/qmd-query-expansion-lfm2.5-1.2b-instruct-v2 is a 1.2 billion parameter instruction-tuned model, fine-tuned from LiquidAI/LFM2.5-1.2B-Instruct using LoRA for Query-Metadata (QMD) query expansion. It is specifically designed to expand search queries for QMD systems, supporting directives like /only:lex, /only:vec, and /only:hyde. The model provides both a BF16 Transformers checkpoint and QMD-ready GGUF quantizations, demonstrating high format compliance and entity preservation in QMD evaluations.
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Overview
This model, langleu/qmd-query-expansion-lfm2.5-1.2b-instruct-v2, is a 1.2 billion parameter instruction-tuned variant of LiquidAI/LFM2.5-1.2B-Instruct. It has been fine-tuned using LoRA specifically for Query-Metadata (QMD) query expansion with a v2 data recipe. The repository offers both a merged BF16 Transformers checkpoint and GGUF quantizations optimized for QMD/llama.cpp.
Key Capabilities
- Specialized Query Expansion: Designed to expand search queries for QMD systems, supporting specific directives.
- QMD-Specific Directives: Understands and processes
/only:lex,/only:vec, and/only:hydedirectives, with each output line prefixed accordingly. - High Performance: Achieves an average QMD reward of 94.42% and 99.68% format compliance in validation.
- GGUF Quantizations: Provides
q5_k_m.gguffiles for efficient deployment with QMD andllama.cpp.
When to Use This Model
- QMD Query Expansion: Ideal for applications requiring precise and structured query expansion within a QMD framework.
- Integration with QMD: Directly compatible with QMD systems, especially when using the provided GGUF files.
- Resource-Constrained Environments: The 1.2B parameter size and GGUF quantizations make it suitable for efficient inference.