lmms_eval 0.6.0

A framework for evaluating large multi-modality language models

LMMs-Eval: Probing Intelligence in the Real World

We are building the unified evaluation toolkit for frontier models and probing the abilities in real world, shape what we build next.

🌐 Available in 17 languages

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📚 Documentation | 📖 100+ Tasks | 🌟 30+ Models | ⚡ Quickstart

🏠 Homepage | 💬 Discord | 🤝 Contributing

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Why lmms-eval?

We're on an exciting journey toward creating Artificial General Intelligence (AGI), much like the enthusiasm of the 1960s moon landing. This journey is powered by advanced large language models (LLMs) and large multimodal models (LMMs), which are complex systems capable of understanding, learning, and performing a wide variety of human tasks.

To gauge how advanced these models are, we use a variety of evaluation benchmarks. However, the landscape is fragmented. Benchmarks and datasets are spread across Google Drive, Dropbox, and various university and lab websites. Each comes with its own data format, evaluation script, and post-processing logic. When two teams evaluate the same model on the same benchmark but use different pipelines, they often get different numbers — not because their models differ, but because their evaluation code differs.

Even with a unified pipeline, evaluation has deeper challenges. Most scores are reported as single numbers without confidence intervals, making it impossible to tell real improvements from noise. Many benchmarks contain correlated questions (e.g., multiple questions about the same video), which inflates the apparent precision. A benchmark that cannot reliably indicate a model's true capabilities is not just unhelpful — it actively misleads research directions. And as model development accelerates, evaluation itself becomes a bottleneck: teams need to evaluate checkpoints continuously during training, not just once at the end.

In the field of language models, lm-evaluation-harness set a valuable precedent — integrated data and model interfaces, powering the open-llm-leaderboard and gradually becoming the underlying ecosystem of the foundation model era. We humbly absorbed its design and introduce lmms-eval — first, to give the open-source community reproducible and efficient multimodal evaluation; and beyond that, to explore evaluation's role on the path to frontier models. We believe better evals lead to better models: rigorous evaluation maps the capability frontier and shapes what we build next. That is what it means to probe intelligence in the real world.

What's New

February 2026 (v0.6) - Our previous versions were too slow, the architecture wasn't clean, and the results lacked statistical insight. v0.6 is a re-engineered release that addresses all three: evaluation runs as a standalone service (decoupled from training, serving queue-based eval requests), statistically grounded results that capture real model improvements rather than a single accuracy score (confidence intervals, clustered standard errors, paired comparison with t-test), and optimizations to max out your model runtime's capacity (~7.5x over previous versions). 50+ new tasks and 10+ new models. Release notes | Changelog.

October 2025 (v0.5) - Audio had been a gap. Models could hear, but we had no consistent way to test them. This release added comprehensive audio evaluation, response caching for efficiency, and 50+ benchmark variants spanning audio, vision, and reasoning. Release notes.

Below is a chronological list of recent tasks, models, and features added by our amazing contributors.

• [2025-01] 🎓🎓 We have released our new benchmark: Video-MMMU: Evaluating Knowledge Acquisition from Multi-Discipline Professional Videos. Please refer to the project page for more details.
• [2024-12] 🎉🎉 We have presented MME-Survey: A Comprehensive Survey on Evaluation of Multimodal LLMs, jointly with MME Team and OpenCompass Team.
• [2024-11] 🔈🔊 The lmms-eval/v0.3.0 has been upgraded to support audio evaluations for audio models like Qwen2-Audio and Gemini-Audio across tasks such as AIR-Bench, Clotho-AQA, LibriSpeech, and more. Please refer to the blog for more details!
• [2024-10] 🎉🎉 We welcome the new task NaturalBench, a vision-centric VQA benchmark (NeurIPS'24) that challenges vision-language models with simple questions about natural imagery.
• [2024-10] 🎉🎉 We welcome the new task TemporalBench for fine-grained temporal understanding and reasoning for videos, which reveals a huge (>30%) human-AI gap.
• [2024-10] 🎉🎉 We welcome the new tasks VDC for video detailed captioning, MovieChat-1K for long-form video understanding, and Vinoground, a temporal counterfactual LMM benchmark composed of 1000 short natural video-caption pairs. We also welcome the new models: AuroraCap and MovieChat.
• [2024-09] 🎉🎉 We welcome the new tasks MMSearch and MME-RealWorld for inference acceleration
• [2024-09] ⚙️️⚙️️️️ We upgrade lmms-eval to 0.2.3 with more tasks and features. We support a compact set of language tasks evaluations (code credit to lm-evaluation-harness), and we remove the registration logic at start (for all models and tasks) to reduce the overhead. Now lmms-eval only launches necessary tasks/models. Please check the release notes for more details.
• [2024-08] 🎉🎉 We welcome the new model LLaVA-OneVision, Mantis, new tasks MVBench, LongVideoBench, MMStar. We provide new feature of SGlang Runtime API for llava-onevision model, please refer the doc for inference acceleration
• [2024-07] 👨‍💻👨‍💻 The lmms-eval/v0.2.1 has been upgraded to support more models, including LongVA, InternVL-2, VILA, and many more evaluation tasks, e.g. Details Captions, MLVU, WildVision-Bench, VITATECS and LLaVA-Interleave-Bench.
• [2024-07] 🎉🎉 We have released the technical report and LiveBench!
• [2024-06] 🎬🎬 The lmms-eval/v0.2.0 has been upgraded to support video evaluations for video models like LLaVA-NeXT Video and Gemini 1.5 Pro across tasks such as EgoSchema, PerceptionTest, VideoMME, and more. Please refer to the blog for more details!
• [2024-03] 📝📝 We have released the first version of lmms-eval, please refer to the blog for more details!

Quickstart

Install and run your first evaluation in under 5 minutes:

git clone https://github.com/EvolvingLMMs-Lab/lmms-eval.git
cd lmms-eval && uv pip install -e ".[all]"

# Run a quick evaluation (Qwen2.5-VL on MME, 8 samples)
python -m lmms_eval \
  --model qwen2_5_vl \
  --model_args pretrained=Qwen/Qwen2.5-VL-3B-Instruct \
  --tasks mme \
  --batch_size 1 \
  --limit 8

If it prints metrics, your environment is ready. For the full guide, see docs/quickstart.md.

Installation

Using uv (Recommended for consistent environments)

We use uv for package management to ensure all developers use exactly the same package versions. First, install uv:

curl -LsSf https://astral.sh/uv/install.sh | sh

For development with consistent environment:

git clone https://github.com/EvolvingLMMs-Lab/lmms-eval
cd lmms-eval
# Recommend
uv pip install -e ".[all]"
# If you want to use uv sync
# uv sync  # This creates/updates your environment from uv.lock

To run commands:

uv run python -m lmms_eval --help  # Run any command with uv run

To add new dependencies:

uv add <package>  # Updates both pyproject.toml and uv.lock

Alternative Installation

For direct usage from Git:

uv venv eval
uv venv --python 3.12
source eval/bin/activate
# You might need to add and include your own task yaml if using this installation
uv pip install git+https://github.com/EvolvingLMMs-Lab/lmms-eval.git

Reproduction of LLaVA-1.5's paper results

You can check the torch environment info and results check to reproduce LLaVA-1.5's paper results. We found torch/cuda versions difference would cause small variations in the results.

If you want to test on caption dataset such as coco, refcoco, and nocaps, you will need to have java==1.8.0 to let pycocoeval api to work. If you don't have it, you can install by using conda

conda install openjdk=8

you can then check your java version by java -version

Comprehensive Evaluation Results of LLaVA Family Models

As demonstrated by the extensive table below, we aim to provide detailed information for readers to understand the datasets included in lmms-eval and some specific details about these datasets (we remain grateful for any corrections readers may have during our evaluation process).

We provide a Google Sheet for the detailed results of the LLaVA series models on different datasets. You can access the sheet here. It's a live sheet, and we are updating it with new results.

We also provide the raw data exported from Weights & Biases for the detailed results of the LLaVA series models on different datasets. You can access the raw data here.

If you want to test VILA, you should install the following dependencies:

pip install s2wrapper@git+https://github.com/bfshi/scaling_on_scales

Our Development will be continuing on the main branch, and we encourage you to give us feedback on what features are desired and how to improve the library further, or ask questions, either in issues or PRs on GitHub.

Usage Examples

More examples can be found in examples/models

Evaluation with vLLM

Qwen2.5-VL:

bash examples/models/vllm_qwen2vl.sh

Qwen3-VL:

bash examples/models/vllm_qwen3vl.sh

Evaluation with SGLang

bash examples/models/sglang.sh

Evaluation of OpenAI-Compatible Model

bash examples/models/openai_compatible.sh

Evaluation of Qwen2.5-VL

bash examples/models/qwen25vl.sh

Evaluation of Qwen3-VL

bash examples/models/qwen3vl.sh

More Parameters

python3 -m lmms_eval --help

Environmental Variables

Before running experiments and evaluations, we recommend you to export following environment variables to your environment. Some are necessary for certain tasks to run.

export OPENAI_API_KEY="<YOUR_API_KEY>"
export HF_HOME="<Path to HF cache>"
export HF_TOKEN="<YOUR_API_KEY>"
export HF_HUB_ENABLE_HF_TRANSFER="1"
export REKA_API_KEY="<YOUR_API_KEY>"
# Other possible environment variables include
# ANTHROPIC_API_KEY,DASHSCOPE_API_KEY etc.

Common Environment Issues

Sometimes you might encounter some common issues for example error related to httpx or protobuf. To solve these issues, you can first try

python3 -m pip install httpx==0.23.3;
python3 -m pip install protobuf==3.20;
# If you are using numpy==2.x, sometimes may causing errors
python3 -m pip install numpy==1.26;
# Someties sentencepiece are required for tokenizer to work
python3 -m pip install sentencepiece;

Custom Model Integration

lmms-eval supports two types of models: Chat (recommended) and Simple (legacy).

Chat Models (Recommended) 🌟

• Location: lmms_eval/models/chat/
• Use: doc_to_messages function from task
• Input: Structured ChatMessages with roles (user, system, assistant) and content types (text, image, video, audio)
• Supports: Interleaved multimodal content
• Uses: Model's apply_chat_template() method
• Reference: lmms_eval/models/chat/qwen2_5_vl.py or lmms_eval/models/chat/qwen3_vl.py

Example input format:

[
    {"role": "user", "content": [
        {"type": "image", "url": <image>},
        {"type": "text", "text": "What's in this image?"}
    ]}
]

Simple Models (Legacy)

• Location: lmms_eval/models/simple/
• Use: doc_to_visual + doc_to_text functions from task
• Input: Plain text with <image> placeholders + separate visual list
• Supports: Limited (mainly images)
• Manual processing: No chat template support
• Reference: lmms_eval/models/simple/instructblip.py

Example input format:

# Separate visual and text
doc_to_visual -> [PIL.Image]
doc_to_text -> "What's in this image?"

Key Differences

Aspect	Chat Models	Simple Models
File location	models/chat/	models/simple/
Input method	doc_to_messages	doc_to_visual + doc_to_text
Message format	Structured (roles + content types)	Plain text with placeholders
Interleaved support	✅ Yes	❌ Limited
Chat template	✅ Built-in	❌ Manual/None
Recommendation	Use this	Legacy only

Why Use Chat Models?

• ✅ Built-in chat template support
• ✅ Interleaved multimodal content
• ✅ Structured message protocol
• ✅ Better video/audio support
• ✅ Consistent with modern LLM APIs

Chat Model Implementation Example

from lmms_eval.api.registry import register_model
from lmms_eval.api.model import lmms
from lmms_eval.protocol import ChatMessages

@register_model("my_chat_model")
class MyChatModel(lmms):
    is_simple = False  # Use chat interface

    def generate_until(self, requests):
        for request in requests:
            # 5 elements for chat models
            doc_to_messages, gen_kwargs, doc_id, task, split = request.args

            # Get structured messages
            raw_messages = doc_to_messages(self.task_dict[task][split][doc_id])
            messages = ChatMessages(messages=raw_messages)

            # Extract media and apply chat template
            images, videos, audios = messages.extract_media()
            hf_messages = messages.to_hf_messages()
            text = self.processor.apply_chat_template(hf_messages)

            # Generate...

For more details, see the Model Guide.

Custom Dataset Integration

Task Configuration with doc_to_messages

Implement doc_to_messages to transform dataset documents into structured chat messages:

def my_doc_to_messages(doc, lmms_eval_specific_kwargs=None):
    # Extract visuals and text from doc
    visuals = my_doc_to_visual(doc)
    text = my_doc_to_text(doc, lmms_eval_specific_kwargs)

    # Build structured messages
    messages = [{"role": "user", "content": []}]

    # Add visuals first
    for visual in visuals:
        messages[0]["content"].append({"type": "image", "url": visual})

    # Add text
    messages[0]["content"].append({"type": "text", "text": text})

    return messages

YAML Configuration

task: "my_benchmark"
dataset_path: "my-org/my-dataset"
test_split: test
output_type: generate_until

# For chat models (recommended)
doc_to_messages: !function utils.my_doc_to_messages

# OR legacy approach:
doc_to_visual: !function utils.my_doc_to_visual
doc_to_text: !function utils.my_doc_to_text

process_results: !function utils.my_process_results
metric_list:
  - metric: acc

Key Features

doc_to_messages

• Transforms dataset document into structured chat messages
• Returns: List of message dicts with role and content
• Content supports: text, image, video, audio types
• Protocol: Defined in lmms_eval/protocol.py (ChatMessages class)
• Auto-fallback: If not provided, uses doc_to_visual + doc_to_text

For more details, see the Task Guide.

Web UI

LMMS-Eval includes an optional Web UI for interactive evaluation configuration.

Requirements

• Node.js 18+ (for building the frontend, auto-built on first run)

Usage

# Start the Web UI (opens browser automatically)
uv run lmms-eval-ui

# Custom port
LMMS_SERVER_PORT=3000 uv run lmms-eval-ui

The web UI provides:

• Model selection from all available models
• Task selection with search/filter
• Real-time command preview
• Live evaluation output streaming
• Start/Stop evaluation controls

For more details, see Web UI README.

HTTP Evaluation Server

LMMS-Eval includes a production-ready HTTP server for remote evaluation workflows.

Why Use Eval Server?

• Decoupled evaluation: Run evaluations on dedicated GPU nodes while training continues
• Async workflow: Submit jobs without blocking training loops
• Queue management: Sequential job processing with automatic resource management
• Remote access: Evaluate models from any machine

Start Server

from lmms_eval.entrypoints import ServerArgs, launch_server

# Configure server
args = ServerArgs(
    host="0.0.0.0",
    port=8000,
    max_completed_jobs=200,
    temp_dir_prefix="lmms_eval_"
)

# Launch server
launch_server(args)

Server runs at http://host:port with auto-generated API docs at /docs

Client Usage

Sync Client:

from lmms_eval.entrypoints import EvalClient

client = EvalClient("http://eval-server:8000")

# Submit evaluation (non-blocking)
job = client.evaluate(
    model="qwen2_5_vl",
    tasks=["mmmu_val", "mme"],
    model_args={"pretrained": "Qwen/Qwen2.5-VL-7B-Instruct"},
    num_fewshot=0,
    batch_size=1,
    device="cuda:0",
)

# Continue training...
# Later, retrieve results
result = client.wait_for_job(job["job_id"])
print(result["result"])

Async Client:

from lmms_eval.entrypoints import AsyncEvalClient

async with AsyncEvalClient("http://eval-server:8000") as client:
    job = await client.evaluate(
        model="qwen3_vl",
        tasks=["mmmu_val"],
        model_args={"pretrained": "Qwen/Qwen3-VL-4B-Instruct"},
    )
    result = await client.wait_for_job(job["job_id"])

Server API Endpoints

Endpoint	Method	Description
/health	GET	Server health check
/evaluate	POST	Submit evaluation job
/jobs/{job_id}	GET	Get job status and results
/queue	GET	View queue status
/tasks	GET	List available tasks
/models	GET	List available models
/jobs/{job_id}	DELETE	Cancel queued job
/merge	POST	Merge FSDP2 sharded checkpoints

Example Workflow

# Training loop pseudocode
for epoch in range(num_epochs):
    train_one_epoch()

    # After every N epochs, evaluate checkpoint
    if epoch % 5 == 0:
        checkpoint_path = f"checkpoints/epoch_{epoch}"

        # Submit async evaluation (non-blocking)
        eval_job = client.evaluate(
            model="vllm",
            model_args={"model": checkpoint_path},
            tasks=["mmmu_val", "mathvista"],
        )

        # Training continues immediately
        print(f"Evaluation job submitted: {eval_job['job_id']}")

# After training completes, retrieve all results
results = []
for job_id in eval_jobs:
    result = client.wait_for_job(job_id)
    results.append(result)

Security Note

⚠️ This server is intended for trusted environments only. Do NOT expose to untrusted networks without additional security layers (authentication, rate limiting, network isolation).

For more details, see the v0.6 release notes.

Frequently Asked Questions

What models does lmms-eval support?

We support 30+ model families out of the box, including Qwen2.5-VL, Qwen3-VL, LLaVA-OneVision, InternVL-2, VILA, and more. Any OpenAI-compatible API endpoint is also supported. See the full list in lmms_eval/models/.

What benchmarks and tasks are available?

Over 100 evaluation tasks across image, video, and audio modalities, including MMMU, MME, MMBench, MathVista, VideoMME, EgoSchema, and many more. Check docs/current_tasks.md for the full list.

How do I add my own benchmark?

Create a YAML config under lmms_eval/tasks/ with dataset path, splits, and a doc_to_messages function. See docs/task_guide.md for a step-by-step guide.

Can I evaluate a model behind an API (e.g., GPT-4o, Claude)?

Yes. Use --model openai with --model_args model=gpt-4o and set OPENAI_API_KEY. Any OpenAI-compatible endpoint works, including local vLLM/SGLang servers.

How do I run evaluations on multiple GPUs?

Use accelerate launch or pass --device cuda with tensor parallelism via vLLM/SGLang backends. See docs/commands.md for multi-GPU flags.

How do I cite lmms-eval?

Use the BibTeX entries below, or click the "Cite this repository" button in the GitHub sidebar (powered by our CITATION.cff).

Acknowledgement

lmms_eval is a fork of lm-eval-harness. We recommend you to read through the docs of lm-eval-harness for relevant information.

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Below are the changes we made to the original API:

• Build context now only pass in idx and process image and doc during the model responding phase. This is due to the fact that dataset now contains lots of images and we can't store them in the doc like the original lm-eval-harness otherwise the cpu memory would explode.
• Instance.args (lmms_eval/api/instance.py) now contains a list of images to be inputted to lmms.
• lm-eval-harness supports all HF language models as single model class. Currently this is not possible of lmms because the input/output format of lmms in HF are not yet unified. Therefore, we have to create a new class for each lmms model. This is not ideal and we will try to unify them in the future.

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Citations

@misc{zhang2024lmmsevalrealitycheckevaluation,
      title={LMMs-Eval: Reality Check on the Evaluation of Large Multimodal Models},
      author={Kaichen Zhang and Bo Li and Peiyuan Zhang and Fanyi Pu and Joshua Adrian Cahyono and Kairui Hu and Shuai Liu and Yuanhan Zhang and Jingkang Yang and Chunyuan Li and Ziwei Liu},
      year={2024},
      eprint={2407.12772},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2407.12772},
}

@misc{lmms_eval2024,
    title={LMMs-Eval: Accelerating the Development of Large Multimoal Models},
    url={https://github.com/EvolvingLMMs-Lab/lmms-eval},
    author={Bo Li*, Peiyuan Zhang*, Kaichen Zhang*, Fanyi Pu*, Xinrun Du, Yuhao Dong, Haotian Liu, Yuanhan Zhang, Ge Zhang, Chunyuan Li and Ziwei Liu},
    publisher    = {Zenodo},
    version      = {v0.1.0},
    month={March},
    year={2024}
}