colqwen3-8b-vetcoders-mlx

colqwen3-8b-vetcoders-mlx

colqwen3-8b-vetcoders-mlx is an MLX visual document retrieval model derived from tomoro-ai/Colqwen3-8B-base, built for image/page and text-query embedding workflows.

Intended use

• Visual document retrieval over page images and text queries
• Late-interaction ranking experiments for PDFs, scans, and visually rich documents
• Apple Silicon local retrieval pipelines that need MLX-native weights

Out of scope

• Safety-critical decisions without domain expert review
• Claims of benchmark superiority not backed by published evaluation data
• Non-MLX runtime guarantees; this card documents the shipped HF checkpoint, not every possible serving stack
• High-stakes visual interpretation without human review

Training and conversion metadata

This card only reports metadata present in the Hugging Face repository, existing card frontmatter, or public config files. Missing benchmark, dataset, or training-run details are left explicit rather than reconstructed.

Tested inference path

Inference for this checkpoint has been tested with LibraxisAI/mlx-batch-server.
This is the recommended tested path for operator-controlled local inference on Apple Silicon.

This does not claim compatibility with every possible serving stack. It documents the path that has been exercised for this published checkpoint.

Usage

Python

# Example shape for MLX document-retrieval workflows.
# Use the model-specific retrieval wrapper in your application code.
model_id = "LibraxisAI/colqwen3-8b-vetcoders-mlx"
query = "Which page discusses treatment protocol changes?"
document_image = "page.png"

Notes

• This checkpoint is for retrieval embeddings rather than free-form chat.
• Pair it with a ColBERT/MaxSim-style ranking implementation that supports the model layout.

Example output

No public sample output is currently declared for this checkpoint.

Comparison with the base model

Limitations

• No public benchmarks for this checkpoint are declared in the model metadata.
• No public benchmark claims are made by this card unless listed in the frontmatter.
• Validate outputs on your own domain data before relying on this checkpoint.
• Memory use and speed depend heavily on the exact Apple Silicon generation, unified-memory size, and prompt length.

License

apache-2.0. Check the upstream/base model license as well when a base model is declared.

Citation

@misc{libraxisai-colqwen3-8b-vetcoders-mlx,
  title = {colqwen3-8b-vetcoders-mlx},
  author = {LibraxisAI},
  year = {2026},
  howpublished = {\url{https://huggingface.co/LibraxisAI/colqwen3-8b-vetcoders-mlx}},
  note = {MLX checkpoint published by LibraxisAI}
}

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