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.

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.

Aspect	Status
Tested runtime	`LibraxisAI/mlx-batch-server`
Target hardware	Apple Silicon
Inference mode	Local / self-hosted
Hugging Face Hosted Inference	Disabled for this repository (`inference: false`)

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

Usage

Python

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

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

bibtex

@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}
}