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

Parameter	Value
Repository	LibraxisAI/colqwen3-8b-vetcoders-mlx
Base model	tomoro-ai/Colqwen3-8B-base
Task	visual-document-retrieval
Library	mlx
Format	MLX / Apple Silicon checkpoint
Quantization	Not declared
Architecture	Qwen3VLForConditionalGeneration
Model files	9
Config model_type	qwen3_vl

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.

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. Run the usage example above against your own prompt or audio/image input to inspect behavior.

Comparison with the base model

Aspect	Base	This checkpoint
Lineage	tomoro-ai/Colqwen3-8B-base	LibraxisAI/colqwen3-8b-vetcoders-mlx
Runtime target	Upstream runtime format	MLX on Apple Silicon
Published benchmark delta	Not declared in public metadata	Not declared in public metadata

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}
}

Inference tested on

LibraxisAI/mlx-batch-server

Related

• Base model: tomoro-ai/Colqwen3-8B-base

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