Testing LLM and VLM serving accuracy {#ovmsdemoscontinuousbatchingaccuracy}

May 27, 2026 · View on GitHub

This guide shows how to access to LLM and VLM model over serving endpoint.

The lm-evaluation-harness framework provides a convenient method of evaluating the quality of the model exposed over OpenAI API. It reports end to end quality of served model from the client application point of view.

Note: Below steps have been verified on Linux

Preparing the lm-evaluation-harness framework

Install the framework via pip:

pip3 install --extra-index-url "https://download.pytorch.org/whl/cpu" lm_eval[api] langdetect immutabledict dotenv openai

Starting the model server

Example of LLM and VLM models deployment is documented in other demos like Agentic usage for LLM models Using VLM models

Running the tests for LLM models

lm-eval --model local-chat-completions --tasks gsm8k --model_args model=meta-llama/Meta-Llama-3.1-8B-Instruct,base_url=http://localhost:8000/v3/chat/completions,num_concurrent=1,max_retries=3,tokenized_requests=False --verbosity DEBUG  --log_samples --output_path test/ --seed 1 --apply_chat_template --limit 100

local-chat-completions ({'model': 'meta-llama/Meta-Llama-3.1-8B-Instruct', 'base_url': 'http://localhost:8000/v3/chat/completions', 'num_concurrent': 10, 'max_retries': 3, 'tokenized_requests': False}), gen_kwargs: ({}), limit: 100.0, num_fewshot: None, batch_size: 1
|Tasks|Version|     Filter     |n-shot|  Metric   |   |Value|   |Stderr|
|-----|------:|----------------|-----:|-----------|---|----:|---|-----:|
|gsm8k|      3|flexible-extract|     5|exact_match|↑  | 0.62|±  |0.0488|
|     |       |strict-match    |     5|exact_match|↑  | 0.17|±  |0.0378|

While testing the non chat model and completion endpoint, the command would look like this:

lm-eval --model local-completions --tasks gsm8k --model_args model=meta-llama/Meta-Llama-3.1-8B,base_url=http://localhost:8000/v3/completions,num_concurrent=1,max_retries=3,tokenized_requests=False --verbosity DEBUG  --log_samples --output_path results/ --seed 1 --limit 100

local-completions ({'model': 'meta-llama/Meta-Llama-3.1-8B', 'base_url': 'http://localhost:8000/v3/completions', 'num_concurrent': 10, 'max_retries': 3, 'tokenized_requests': False}), gen_kwargs: ({}), limit: 100.0, num_fewshot: None, batch_size: 1
|Tasks|Version|     Filter     |n-shot|  Metric   |   |Value|   |Stderr|
|-----|------:|----------------|-----:|-----------|---|----:|---|-----:|
|gsm8k|      3|flexible-extract|     5|exact_match|↑  | 0.43|±  |0.0498|
|     |       |strict-match    |     5|exact_match|↑  | 0.43|±  |0.0498|

Other examples are below:

lm-eval --model local-chat-completions --tasks leaderboard_ifeval --model_args model=meta-llama/Meta-Llama-3.1-8B-Instruct,base_url=http://localhost:8000/v3/chat/completions,num_concurrent=10,max_retries=3,tokenized_requests=False --verbosity DEBUG --log_samples --output_path test/ --seed 1 --limit 100 --apply_chat_template

lm-eval --model local-completions --tasks wikitext --model_args model=meta-llama/Meta-Llama-3.1-8B,base_url=http://localhost:8000/v3/completions,num_concurrent=10,max_retries=3,tokenized_requests=False --verbosity DEBUG --log_samples --output_path test/ --seed 1 --limit 100

Running the tests for VLM models

Use lmms-eval project - mme and mmmu_val tasks.

export OPENAI_BASE_URL=http://localhost:8000/v3
export OPENAI_API_KEY="unused"
git clone https://github.com/EvolvingLMMs-Lab/lmms-eval
cd lmms-eval
pip install -e . --extra-index-url "https://download.pytorch.org/whl/cpu"
python -m lmms_eval \
    --model openai_compatible \
    --model_args model_version=OpenVINO/InternVL2-8B_int4-ov,max_retries=1 \
    --tasks mme,mmmu_val \
    --batch_size 1 \
    --log_samples \
    --log_samples_suffix openai_compatible \
    --output_path ./logs

Results example:

openai_compatible (model_version=OpenGVLab/InternVL2_5-8B,max_retries=1), gen_kwargs: (), limit: None, num_fewshot: None, batch_size: 1
| Tasks  |Version|Filter|n-shot|       Metric       |   |  Value  |   |Stderr|
|--------|-------|------|-----:|--------------------|---|--------:|---|------|
|mme     |Yaml   |none  |     0|mme_cognition_score |↑  | 600.3571|±  |   N/A|
|mme     |Yaml   |none  |     0|mme_perception_score|↑  |1618.2984|±  |   N/A|
|mmmu_val|      0|none  |     0|mmmu_acc            |↑  |   0.5322|±  |   N/A|

Running the tests for agentic models with function calls

Use Berkeley function call leaderboard

git clone https://github.com/ShishirPatil/gorilla
cd gorilla/berkeley-function-call-leaderboard
git checkout 9b8a5202544f49a846aced185a340361231ef3e1
curl -s https://raw.githubusercontent.com/openvinotoolkit/model_server/refs/heads/main/demos/continuous_batching/accuracy/gorilla.patch | git apply -v
pip install -e . --extra-index-url "https://download.pytorch.org/whl/cpu"

The commands below assumes the models is deployed with the name ovms-model. It must match the name set in the bfcl_eval/constants/model_config.py.

export OPENAI_BASE_URL=http://localhost:8000/v3
export CHAT_TEMPLATE_KWARGS='{"enable_thinking":false, "reasoning_effort":"low", "preserve_reasoning":false}'

bfcl generate --model ovms-model --test-category simple_python,multiple,multi_turn_base --temperature 0.0 --num-threads 10 -o --result-dir model_name_dir
bfcl evaluate --model ovms-model --result-dir model_name_dir

Alternatively, use the model name ovms-model-stream to run the tests with stream requests. The results should be the same.

export OPENAI_BASE_URL=http://localhost:8000/v3
bfcl generate --model ovms-model-stream --test-category simple_python,multiple,multi_turn_base --temperature 0.0 --num-threads 10 -o --result-dir model_name_dir
bfcl evaluate --model ovms-model-stream --result-dir model_name_dir

Analyzing results The output artifacts will be stored in result and scores. For example:

cat score/openvino-qwen3-8b-int4-FC/BFCL_v4_simple_python_score.json | head -1
{"accuracy": 0.95, "correct_count": 380, "total_count": 400}

Those results can be compared with the reference from the berkeley leaderbaord.

Note: The same procedure can be used to validate vLLM component. The only needed change would be updating base_url including replacing /v3/ with /v1/.