lmms-eval-guide

name	lmms-eval-guide
description	Guides AI coding agents through the lmms-eval codebase - a unified evaluation framework for Large Multimodal Models (LMMs). Use when integrating new models, adding evaluation tasks/benchmarks, running YAML config-driven evaluations, orchestrating non-blocking training-time evaluation via the HTTP eval server, or navigating the evaluation pipeline architecture.
metadata	{"version":"v0.7"}

lmms-eval Codebase Guide

lmms-eval evaluates Large Multimodal Models across image, video, and audio tasks. 95 model backends (14 chat + 81 simple/legacy), 230 task directories, 1377 YAML configs.

Scope of This Skill

This file is the routing layer for agents. It gives architecture context, decision points, and fast guardrails.

Detailed implementation lives in references/*.md.

Use this skill to cover end-to-end workflows:

1. Add or update model backends.
2. Add or update task/benchmark definitions.
3. Run evaluations via YAML configs (--config).
4. Insert lmms-eval into training jobs via async API server calls.
5. Operate HTTP eval service (/evaluate, /jobs/{job_id}, /queue, /tasks, /models).
6. Verify small-sample eval before large-scale runs.
7. Run full-scale evaluations with proper cache, batch, and seed settings.
8. Debug pipeline failures systematically.

When to Use This Skill

Use this skill when requests include any of the following intents:

• "Add a new model" / "integrate model backend"
• "Add a new task/benchmark" / "write task yaml"
• "Run evaluation with YAML config" / "set up --config"
• "Insert lmms-eval into training job"
• "Run eval asynchronously without blocking training"
• "Start or debug HTTP eval server"
• "Configure reasoning tag stripping" / " tags in output"
• "Run full evaluation" / "production eval run"
• "Debug eval failure" / "eval is broken" / "something failed"
• "Where do I edit X?" / "which file handles Y?"
• "Upgrade from v0.6" / "breaking changes"

Instructions (Execution Order)

1. Classify the request into one of: model extension, task extension, config setup, training integration, service operations, debugging.
2. Load the matching reference(s) from the routing matrix below.
3. Follow existing patterns in neighboring files before writing new code/config.
4. Validate with a smoke eval (--limit 5 or --limit 8) before broad runs.
5. For YAML config workflows, use --config with CLI overrides for quick iteration.
6. For training loops, use async HTTP job submission and collect by job_id.
7. For queue or failure issues, inspect /queue and /jobs/{job_id} and handle terminal states.

Setup

uv sync && uv run pre-commit install
# Quick eval test
python -m lmms_eval --model qwen2_5_vl --model_args pretrained=Qwen/Qwen2.5-VL-3B-Instruct --tasks mme --batch_size 1 --limit 8
# Lint
uv run pre-commit run --all-files

Architecture

lmms_eval/
├── __main__.py              # CLI entry (python -m lmms_eval)
├── cli/                     # Subcommand dispatch (eval, tasks, models, ui, serve, power, version)
├── evaluator.py             # Core evaluation loop + reasoning tag stripping
├── protocol.py              # ChatMessages - multimodal message protocol
├── api/
│   ├── model.py             # Base class `lmms` - all models subclass this
│   ├── instance.py          # `Instance` - request object passed to models
│   ├── task.py              # ConfigurableTask - task loading from YAML
│   ├── reasoning.py         # strip_reasoning_tags(), parse_reasoning_tags_config()
│   └── registry.py          # @register_model, @register_task decorators
├── models/
│   ├── __init__.py           # AVAILABLE_SIMPLE_MODELS, AVAILABLE_CHAT_TEMPLATE_MODELS, MODEL_ALIASES
│   ├── registry_v2.py        # ModelManifest, ModelRegistryV2 - resolution prefers chat over simple
│   ├── chat/                 # Chat models (14+, RECOMMENDED for new models)
│   │   ├── async_openai.py   # OpenAI-compatible API (message_format parameter)
│   │   └── ...               # qwen2_5_vl, qwen3_vl, vllm, sglang, etc.
│   └── simple/               # Legacy models (81)
├── tasks/                    # Auto-registered from YAML (230 dirs, 1377 configs)
├── entrypoints/              # HTTP eval server (FastAPI)
│   ├── http_server.py        # REST endpoints: /evaluate, /jobs/{job_id}, /queue, /tasks, /models
│   ├── client.py             # EvalClient (sync), AsyncEvalClient
│   └── protocol.py           # EvaluateRequest, JobInfo
├── llm_judge/                # LLM-as-judge scoring
└── tui/                      # Web UI

Evaluation Pipeline

CLI --model X --tasks Y  (or --config experiment.yaml)
  1. CONFIG: merge defaults < YAML < CLI overrides -> resolved_cli_args
  2. MODEL: ModelRegistryV2.resolve() -> dynamic import -> cls.create_from_arg_string()
  3. TASKS: scan tasks/ for YAML -> ConfigurableTask(config)
  4. REQUESTS: build Instance objects per doc
     Chat models: (doc_to_messages, gen_kwargs, doc_id, task, split)  # 5 elements
     Simple models: (contexts, gen_kwargs, doc_to_visual, doc_id, task, split)  # 6 elements
  5. INFERENCE: model.generate_until(requests) or model.loglikelihood(requests)
  6. REASONING STRIP: strip_reasoning_tags(filtered_resps) if --reasoning_tags enabled
  7. SCORING: task.process_results(doc, filtered_resps) -> {metric: value}
  8. AGGREGATION: mean, stderr, confidence intervals -> results JSON

ChatMessages Protocol (protocol.py)

Structured multimodal message format for chat models:

# Content types: text, image, video, audio
# Message: {"role": "user"|"system"|"assistant", "content": [{type, ...}, ...]}
messages = ChatMessages(messages=raw_messages)
images, videos, audios = messages.extract_media()
hf_messages = messages.to_hf_messages()      # for apply_chat_template()
oai_messages = messages.to_openai_messages()  # for OpenAI API

Model Registration

Models register in models/__init__.py via two dicts mapping model_id -> ClassName:

• AVAILABLE_CHAT_TEMPLATE_MODELS - chat models in models/chat/
• AVAILABLE_SIMPLE_MODELS - simple models in models/simple/

MODEL_ALIASES provides backward-compatible name mappings. Registry prefers chat over simple when both exist.

Reference Routing Matrix

Load only the reference(s) relevant to your current task.

You need to do	Load this reference	Why
Add a new model backend	references/models.md	Model class template, registration, message_format, validation
Add a new benchmark/task	references/tasks.md	YAML schema, reasoning_tags, utils.py contracts, aggregation patterns
Start/operate HTTP eval service	references/api-server.md	Server args, endpoints, client contracts
Insert eval into training jobs (non-blocking)	references/api-server.md	Training-loop integration patterns using async job submission
Diagnose queue/job states	references/api-server.md	Job lifecycle, polling, cancellation, queue inspection
Run with YAML config / smoke tests / full evaluations	references/workflows.md	--config usage, smoke test scripts, full run recipes, cache strategy
Debug pipeline failures	references/workflows.md	Step-by-step debug workflow, failure classification, diagnostic commands
Find which file to edit	references/workflows.md	Edit-routing table by intent (model, task, evaluator, output)

Multi-Track Requests

For requests spanning multiple tracks (for example, new model + new task + training integration), combine relevant references and execute in this order:

1. Model/task implementation.
2. Smoke validation.
3. HTTP service orchestration.

Key CLI Flags

Flag	Description
--config	YAML config file (replaces long CLI one-liners, see workflows.md)
--model	Model backend (e.g., qwen2_5_vl, openai, vllm)
--model_args	Comma key=value pairs (e.g., pretrained=org/model,device_map=auto)
--tasks	Comma-separated task names
--limit N	Evaluate first N samples only (always use when testing)
--batch_size N	Batch size for inference
--log_samples	Save per-sample predictions
--reasoning_tags	Control <think> tag stripping: default <think>...</think>, none to disable, or custom JSON pairs
--verbosity DEBUG	Detailed logging

Environment Variables

# Core
export OPENAI_API_KEY="..."                    # API-backed models
export HF_TOKEN="..."                          # Gated HuggingFace datasets
export HF_HOME="/path/to/cache"                # HF cache directory

# Video decode backends
export LMMS_VIDEO_DECODE_BACKEND="pyav"         # pyav (default) | torchcodec | dali
export LMMS_VIDEO_TORCHCODEC_THREADS="8"       # Thread count for TorchCodec (MUST set explicitly)
export LMMS_VIDEO_DALI_DEVICE="gpu"             # GPU decode with DALI

# MINERVA Lance mode
export MINERVA_LANCE_VIDEO_URI="hf://datasets/lmms-lab-eval/minerva/data/train.lance"
export MINERVA_VIDEO_DIR="/path/to/videos"      # Local-first mode

# Safety tasks
export PERSPECTIVE_API_KEY="..."                # Toxicity scoring (optional, falls back to keyword heuristic)

Common Errors

Error	Fix
ValueError: gen_kwargs['until']	until must be str or list[str]
NotImplementedError: loglikelihood	Implement loglikelihood() or use generate_until tasks
Visual is None or []	Guard: if visual is not None and len(visual) > 0
is_simple mismatch	Set is_simple = False on chat model classes
Unknown keys in config file	Typo in YAML config key. Error message lists valid keys.
<think> tokens in scored output	Enable --reasoning_tags (default) or check per-task reasoning_tags override

Upgrading from v0.6

Change	Migration
async_openai_qwen3_vl model removed	Use async_openai with message_format=qwen3_vl in --model_args
is_qwen3_vl flag removed	Use message_format=qwen3_vl instead
parse_reasoning_model_answer removed	Reasoning stripping is now pipeline-level via --reasoning_tags
read_video_pyav renamed	Use read_video. Backward-compat alias exists.

Constraints

• Package manager: uv only, never pip
• Linting and formatting: Ruff checks, sorts imports, and formats Python (line-length=240). Run uv run pre-commit run --all-files
• Testing: Always use --limit 5 or --limit 8 when testing changes
• Follow patterns: Match the style of neighboring files exactly