Instructions to use Kxck/Finance_2408_v1_SFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kxck/Finance_2408_v1_SFT with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Kxck/Finance_2408_v1_SFT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
FinChart Phase 2D SFT-2408
This adapter continues Kxck/Finance_500_v1 on 2,408 ChartQA train-only records:
- 408 validated structured SFT examples from
train[0:500]; - 2,000 disjoint answer-supervised examples from
train[500:2500].
The frozen evaluation splits were excluded from training. This is an SFT scaling experiment, not a DPO model.
Frozen evaluation
- ChartQA
val[0:500]: 342 / 500 = 68.4% - ChartQA
test[0:2500]: 1,903 / 2,500 = 76.12%
Inference used private vLLM, greedy decoding, and the Phase 1 exact-or-numeric-tolerance matcher. Full manifests are included in this repository.
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Model tree for Kxck/Finance_2408_v1_SFT
Base model
Qwen/Qwen3-VL-4B-Instruct