Base model: tobil/qmd-query-expansion-qwen3.5-2B (Qwen3.5-2B VL, multimodal Image-Text-to-Text)
Quantization: 4-bit (4.503 bits per weight average)
Framework: MLX
Memory: ~1.0 GB (down from ~4 GB float16)
Use case: Query expansion for visual-language memory search (RecallForge)

Usage

python

from mlx_lm import load, generate

model, tokenizer = load("bmeyer2025/qmd-query-expansion-qwen3.5-2B-mlx-4bit")
response = generate(model, tokenizer, prompt="Expand this search query: transformer attention mechanism")

Purpose

This model generates lexical, semantic, and hypothetical document expansions for search queries in RecallForge, a cross-modal visual-language memory search system.

Given a query like "transformer attention", it produces:

Lexical expansion: Related keywords for BM25 search
Vector expansion: Semantically rich rephrasing for embedding search
HyDE expansion: Hypothetical document passage for retrieval

Quantization

Converted using mlx_lm.convert:

mlx_lm.convert --hf-path tobil/qmd-query-expansion-qwen3.5-2B -q --q-bits 4

License

Apache 2.0 (same as base model)