Skip to content
← Models
Open weightsvis-nlp (Ahmed Masry et al.)/10 trustMetrics-checked

ChartGemma

3B PaliGemma-based specialist for chart reasoning in the wild: QA, summarization and fact-checking from chart images.

Visit official page →

About

ChartGemma is a 3B chart specialist built by instruction-tuning PaliGemma (SigLIP vision encoder plus Gemma-2B) on data generated directly from chart images rather than from their underlying data tables. That choice is the point: real-world charts carry trends, annotations and visual quirks that tables do not, and the authors report state-of-the-art results across five chart benchmarks covering question answering, summarization and fact-checking.

At 3B parameters it is small enough to run on a single GPU, and the released checkpoints, dataset and Hugging Face demo make it easy to test on your own charts. Qualitative studies in the paper suggest it writes more faithful summaries of complex real-world charts than larger contemporaries. The limits are familiar for academic releases: all headline results are author-reported, adoption outside research is modest, and it will still misread unusual chart types. For chart QA and figure understanding in documents, it is a strong open baseline.

Specs

Context window

8,192 tokens (Gemma-2B base context)

Pricing

Free / open weights

Free quota

Weights free to download from Hugging Face

API access

Hugging Face (ahmed-masry/chartgemma); self-hosted

Weights

Open weights

Released

Not disclosed

Benchmarks

  • State-of-the-art on 5 chart benchmarks (chart summarization, QA, fact-checking), author-reported (arXiv 2407.04172); no independent eval found

Provider-reported unless a source says otherwise. Benchmarks are marketing-adjacent by nature — treat them as directional.

Use cases

chart-understandingdata-visualizationdocument-understandingquestion-answering

Trust score — 6/10

/10 trustMetrics-checked

Scored 6/10 (TIER metrics, metrics-v1). Evidence: arXiv 2407.04172 (Jul 2024); HF ahmed-masry/chartgemma with demo Space and dataset release; GitHub vis-nlp/chartgemma. Strengths: SOTA claim across 5 chart benchmarks, trains on image-derived data (not data tables), small 3B footprint. Docked for: (1) all results are author-reported with no independent replication; (2) modest adoption outside research (low HF download counts); (3) license not re-verified on the card. License: none declared on model card.

How the trust score is built

Full methodology →
Adoption · stars/downloads, log-scaled vs peers
4.0/10
Maintenance · recency, releases, issue load
4.0/10
Openness · license + docs
7.0/10
Manual review · sanity read + red-flag check
6.0/10
  • · arXiv 2407.04172 (v1 2024-07-04); PaliGemma (SigLIP + Gemma-2B) instruction-tuned on image-derived chart data
  • · HF ahmed-masry/chartgemma with demo Space + dataset release; GitHub vis-nlp/chartgemma
  • · SOTA claim across 5 chart benchmarks is author-reported; no independent replication found
  • · Modest adoption outside research; license not re-verified on the card

Sources