diff --git a/community_tasks/chart_qa_evals.py b/community_tasks/chart_qa_evals.py
new file mode 100644
index 000000000..ebe968551
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+++ b/community_tasks/chart_qa_evals.py
@@ -0,0 +1,97 @@
+# MIT License
+
+# Copyright (c) 2024 The HuggingFace Team
+
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
+# ruff: noqa: F405, F403, F401
+"""
+Task to evaluate VLMs on HuggingFaceM4/ChartQA.
+
+Example usage:
+lighteval accelerate "model_name=google/gemma-3-4b-it" "community|chart_qa:human|0" --custom-tasks community_tasks/chart_qa_evals.py --vision-model
+"""
+
+import numpy as np
+
+from lighteval.metrics.dynamic_metrics import MultilingualExtractiveMatchMetric
+from lighteval.metrics.metrics import SampleLevelMetric
+from lighteval.metrics.utils.extractive_match_utils import (
+    ExprExtractionConfig,
+    LatexExtractionConfig,
+)
+from lighteval.metrics.utils.metric_utils import SamplingMethod
+from lighteval.tasks.lighteval_task import LightevalTaskConfig
+from lighteval.tasks.requests import Doc
+from lighteval.utils.language import Language
+
+
+def prompt_fn(line, task_name: str = None):
+    return Doc(
+        task_name=task_name,
+        query="Answer the following question. The last line of your response should be of the following format: 'Answer: $ANSWER' (without quotes) where $ANSWER is the answer to the question.\n\n"
+        + line["query"],
+        gold_index=0,
+        choices=[line["label"]],
+        images=[line["image"]],
+    )
+
+
+extraction_targets = [ExprExtractionConfig(), LatexExtractionConfig()]
+metric = SampleLevelMetric(
+    metric_name="extractive_match",
+    sample_level_fn=MultilingualExtractiveMatchMetric(
+        language=Language.ENGLISH,
+        gold_extraction_target=extraction_targets,
+        pred_extraction_target=extraction_targets,
+        precision=4,
+    ),
+    category=SamplingMethod.GENERATIVE,
+    corpus_level_fn=np.mean,
+    higher_is_better=True,
+)
+
+task = LightevalTaskConfig(
+    name="chart_qa",
+    prompt_function=prompt_fn,
+    suite=["community"],
+    hf_repo="HuggingFaceM4/ChartQA",
+    hf_subset="default",
+    hf_avail_splits=["train", "val", "test"],
+    evaluation_splits=["test"],
+    few_shots_split=None,
+    few_shots_select=None,
+    metrics=[metric],
+)
+
+human_task = LightevalTaskConfig(
+    name="chart_qa:human",
+    prompt_function=prompt_fn,
+    suite=["community"],
+    hf_repo="HuggingFaceM4/ChartQA",
+    hf_subset="default",
+    hf_avail_splits=["train", "val", "test"],
+    evaluation_splits=["test"],
+    hf_filter=lambda line: line["human_or_machine"] == 0,
+    few_shots_split=None,
+    few_shots_select=None,
+    metrics=[metric],
+)
+
+TASKS_TABLE = [task, human_task]