---
license: apache-2.0
pipeline_tag: text-generation
tags:
  - lfm2
  - data-use
  - provenance
  - usage
  - impact
  - sft
  - lora
---

# lfm2.5-350M-multitask-datause

LoRA SFT of `LiquidAI/LFM2.5-350M` for data-mention provenance attributes
(producer / year / geography / acronym) and usage/impact classification
(data_type / usage_action / impact_label / usage_summary).

## Training
- base model: `LiquidAI/LFM2.5-350M`
- dataset: `rafmacalaba/data-use-sft`
- epochs: 5
- learning rate: 0.0002
- LoRA: r=16 alpha=32 dropout=0.05
- completion-only masking (loss on assistant JSON turn)

## real holdout

Holdout n=14776. Exact string match of each emitted attribute against the gold label.

| attribute | tp | fp | fn | precision | recall | f0.5 | f1 |
| --- | --- | --- | --- | --- | --- | --- | --- |
| producer | 1909 | 968 | 914 | 0.6635 | 0.6762 | 0.6660 | 0.6698 |
| year | 2264 | 520 | 631 | 0.8132 | 0.7820 | 0.8068 | 0.7973 |
| geography | 2632 | 984 | 968 | 0.7279 | 0.7311 | 0.7285 | 0.7295 |
| acronym | 1973 | 456 | 449 | 0.8123 | 0.8146 | 0.8127 | 0.8134 |
| **overall** | 8778 | 2928 | 2962 | 0.7499 | 0.7477 | 0.7494 | 0.7488 |

Usage/impact macro-F1 (per head):
- data_type: 0.7005
- usage_action: 0.6554
- impact_label: 0.5842
- usage_summary: mean_sim=0.5522 grounded_rate=0.6513

Verbatim rate (emitted values that are substrings of the context): 11659/11706 = 0.9960

## synthetic holdout

Holdout n=2305. Exact string match of each emitted attribute against the gold label.

| attribute | tp | fp | fn | precision | recall | f0.5 | f1 |
| --- | --- | --- | --- | --- | --- | --- | --- |
| producer | 231 | 39 | 56 | 0.8556 | 0.8049 | 0.8449 | 0.8294 |
| year | 265 | 50 | 40 | 0.8413 | 0.8689 | 0.8466 | 0.8548 |
| geography | 344 | 63 | 53 | 0.8452 | 0.8665 | 0.8494 | 0.8557 |
| acronym | 145 | 84 | 36 | 0.6332 | 0.8011 | 0.6609 | 0.7073 |
| **overall** | 985 | 236 | 185 | 0.8067 | 0.8419 | 0.8135 | 0.8239 |

Usage/impact macro-F1 (per head):
- data_type: 0.7818
- usage_action: 0.6489
- impact_label: 0.5147
- usage_summary: mean_sim=0.5752 grounded_rate=0.6768

Verbatim rate (emitted values that are substrings of the context): 1216/1221 = 0.9959