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license: apache-2.0
base_model: meta-llama/Llama-3.2-3B-Instruct
datasets:
- 15juneee/hr-practitioner-adapted-v1
tags:
- hr
- adaption-autoscientist
- lora
language:
- en
---
# HR Practitioner (hr)
Fine-tuned for HR and people operations, trained with
[Adaption AutoScientist](https://docs.adaptionlabs.ai/guides/autoscientist-api/) for the
AutoScientist Challenge (Part 2).
- **Base model:** `meta-llama/Llama-3.2-3B-Instruct`
- **Training data:** [`15juneee/hr-practitioner-adapted-v1`](https://huggingface.co/datasets/15juneee/hr-practitioner-adapted-v1)
(also on [Kaggle](https://www.kaggle.com/datasets/junesdata/hr-practitioner-adapted-v1))
- **Method:** AutoScientist co-optimised data adaptation and training recipe
## Measured improvement
AutoScientist reported **best_win_rate = 0.6262** against `meta-llama/Llama-3.2-3B-Instruct` - the fine-tuned model is preferred over its own baseline in 62.6% of comparisons.
This model was trained with **DPO on preference pairs** generated by `datasets.run(training_type='preference_pairs')`. That is a substantial gain over the supervised fine-tune of the same data, which scored 0.5492: SFT teaches the style of good answers, whereas DPO optimises the pairwise preference that is actually being measured.
Evaluation methodology, including the position-swap and dual-judge controls, is in
`EVAL.md` in the project repository. The held-out split used is published alongside the
training data so the number can be reproduced.
## Intended use and limitations
Intended for HR and people operations assistance. **Output is not legal advice.** Employment law is jurisdiction-specific and the training data is not jurisdiction-tagged, so any compliance-sensitive guidance needs review by a qualified professional in the relevant jurisdiction.
Training data is drawn from English-language job adverts and generic HR questions weighted toward salaried office employment.
## Reproducing
The dataset build, training pipeline and evaluation harness are all scripted; see the
project repository.
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