---
base_model: mlx-community/Qwen3.5-9B-4bit
library_name: mlx
license: other
license_name: qwen
tags:
- lora
- ontology-learning
- ontology-extraction
- text2onto
- llms4ol-2026
- qwen3.5
---

# qwen3.5-9b-ontology-extraction-baseft-mlx

A LoRA adapter for **`Qwen/Qwen3.5-9B`** that extracts a *primitive ontology* — `[subject,
relation, object]` triples — from a raw text document.

Built by **Semantic Swingers** for the **LLMs4OL 2026** shared task (Task A, "flagship":
text → ontology triples, scored by graph similarity). If you have not heard of the challenge, the
short version: given a document, produce the taxonomy/typing triples it implies. This adapter is the
fine-tuned generator behind our submission.

## Which adapter is this

- **Regime:** base-FT (no exemplars).
- **Use `top_k = 0` at inference.** base-FT was trained without exemplars, so it is best run retrieval-free. Using the wrong `k` understates the adapter.
- **Hardware:** Apple Silicon (MLX). Runs natively on Apple Silicon via MLX.

- **Score:** this is the Apple-Silicon 4-bit MLX variant, a *separate artifact* from the bf16 champions — its score differs and is **not** the reported number.

## How to run it

Through our OntoLearner integration (the learner ships in the fork below):

```python
from ontolearner.learner.text2onto import SemanticSwingersText2OntoLearner

learner = SemanticSwingersText2OntoLearner(
    adapter="datagero/qwen3.5-9b-ontology-extraction-baseft-mlx",
    base_model_id="mlx-community/Qwen3.5-9B-4bit",
    backend="mlx",
    top_k=0,
)
learner.load()
# learner.fit(train_docs, task="text2onto"); learner.predict(eval_docs, task="text2onto")
```

## How it was made (the training code is part of the integration)

This adapter was produced by the **same package** that serves it — training is a first-class part
of the OntoLearner integration, not a separate script:

- **Training code:** [`semanticswingers_train.py`](https://github.com/matias-vizcaino/OntoLearner-semanticswingers/blob/feat/semanticswingers-llms4ol2026/ontolearner/learner/text2onto/semanticswingers_train.py) — LoRA SFT with prompt masking
  (loss on completion tokens only) and, for RA-FT, **leave-one-out** exemplar retrieval so a
  training document never sees its own gold.
- **Integration + reproduction:** [OntoLearner fork, PR #1](https://github.com/matias-vizcaino/OntoLearner-semanticswingers/pull/1) and the replication notebook
  therein (`notebooks/pipeline_ontolearner.ipynb`), which runs Tasks A/B/C end-to-end.

To reproduce: `learner = SemanticSwingersText2OntoLearner(train_mode="baseft",
train_backend="mlx", output_dir=...)` then `learner.fit(train_docs, task="text2onto")`.

## Intended use & limitations

Research replication for the LLMs4OL 2026 shared task. Domain: general ontology construction from
text; the training data is the challenge's Task A split. Not intended for production ontology
engineering without validation. Inherits the base model's license and limitations.