Testing LLM and VLM serving accuracy

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.

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

Exporting the models

git clone https://github.com/openvinotoolkit/model_server.git
cd model_server
pip3 install -U -r demos/common/export_models/requirements.txt
mkdir models 
python demos/common/export_models/export_model.py text_generation --source_model meta-llama/Meta-Llama-3-8B-Instruct --weight-format fp16 --kv_cache_precision u8 --config_file_path models/config.json --model_repository_path models
python demos/common/export_models/export_model.py text_generation --source_model meta-llama/Meta-Llama-3-8B --weight-format fp16 --kv_cache_precision u8 --config_file_path models/config.json --model_repository_path models
python demos/common/export_models/export_model.py text_generation --source_model OpenGVLab/InternVL2_5-8B --weight-format fp16 --config_file_path models/config.json --model_repository_path models
python demos/common/export_models/export_model.py text_generation --source_model Qwen/Qwen3-8B --model_name openvino-qwen3-8b-int8 --weight-format int8 --config_file_path models/config.json --model_repository_path models --tools_model_type qwen3 --overwrite_models --enable_prefix_caching

Starting the model server

With Docker

docker run -d --rm -p 8000:8000 -v $(pwd)/models:/workspace:ro openvino/model_server:latest --rest_port 8000 --config_path /workspace/config.json

On Baremetal

ovms --rest_port 8000 --config_path ./models/config.json

Running the tests for LLM models

lm-eval --model local-chat-completions --tasks gsm8k --model_args model=meta-llama/Meta-Llama-3-8B-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-3-8B-Instruct,base_url=http://localhost:8000/v3/chat/completions,num_concurrent=10,max_retries=3,tokenized_requests=False), gen_kwargs: (None), 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-3-8B,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-3-8B,base_url=http://localhost:8000/v3/completions,num_concurrent=10,max_retries=3,tokenized_requests=False), gen_kwargs: (None), 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-3-8B-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-3-8B,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
git checkout f64dfa5fd063e989a0a665d2fd0615df23888c83
pip install -e . --extra-index-url "https://download.pytorch.org/whl/cpu"
python -m lmms_eval \
    --model openai_compatible \
    --model_args model_version=OpenGVLab/InternVL2_5-8B,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 ac37049f00022af54cc44b6aa0cad4402c22d1a0
curl -s https://raw.githubusercontent.com/openvinotoolkit/model_server/refs/heads/releases/2025/3/demos/continuous_batching/accuracy/gorilla.patch | git apply -v
pip install -e . 

The commands below assumes the models is deployed with the name openvino-qwen3-8b-int8. It must match the name set in the bfcl_eval/constants/model_config.py.

export OPENAI_BASE_URL=http://localhost:8000/v3
bfcl generate --model openvino-qwen3-8b-int8-FC --test-category multiple --num-threads 100 -o
bfcl evaluate --model openvino-qwen3-8b-int8-FC

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

cat score/openvino-qwen3-8b-int4-FC/BFCL_v3_simple_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.

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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/.