Evaluation, benchmark, and scorecard, targeting for performance on throughput and latency, accuracy on popular evaluation harness, safety, and hallucination

• Install from Pypi

pip install -r requirements.txt
pip install opea-eval

notes: We have to install requirements.txt at first, cause Pypi can't have direct dependency with specific commit.

• Build from Source

git clone https://github.com/opea-project/GenAIEval
cd GenAIEval
pip install -r requirements.txt
pip install -e .

For evaluating the models on text-generation tasks, we follow the lm-evaluation-harness and provide the command line usage and function call usage. Over 60 standard academic benchmarks for LLMs, with hundreds of subtasks and variants implemented, such as ARC, HellaSwag, MMLU, TruthfulQA, Winogrande, GSM8K and so on.

# pip install --upgrade-strategy eager optimum[habana]
cd evals/evaluation/lm_evaluation_harness/examples
python main.py \
    --model gaudi-hf \
    --model_args pretrained=EleutherAI/gpt-j-6B \
    --tasks hellaswag \
    --device hpu \
    --batch_size 8

cd evals/evaluation/lm_evaluation_harness/examples
python main.py \
    --model hf \
    --model_args pretrained=EleutherAI/gpt-j-6B \
    --tasks hellaswag \
    --device cpu \
    --batch_size 8

from evals.evaluation.lm_evaluation_harness import LMEvalParser, evaluate

args = LMevalParser(
    model="hf",
    user_model=user_model,
    tokenizer=tokenizer,
    tasks="hellaswag",
    device="cpu",
    batch_size=8,
)
results = evaluate(args)

1. setup a separate server with GenAIComps

# build cpu docker
docker build -f Dockerfile.cpu -t opea/lm-eval:latest .

# start the server
docker run -p 9006:9006 --ipc=host  -e MODEL="hf" -e MODEL_ARGS="pretrained=Intel/neural-chat-7b-v3-3" -e DEVICE="cpu" opea/lm-eval:latest

2. evaluate the model

• set base_url, tokenizer and --model genai-hf

cd evals/evaluation/lm_evaluation_harness/examples

python main.py \
    --model genai-hf \
    --model_args "base_url=https://{your_ip}:9006,tokenizer=Intel/neural-chat-7b-v3-3" \
    --tasks  "lambada_openai" \
    --batch_size 2

For evaluating the models on coding tasks or specifically coding LLMs, we follow the bigcode-evaluation-harness and provide the command line usage and function call usage. HumanEval, HumanEval+, InstructHumanEval, APPS, MBPP, MBPP+, and DS-1000 for both completion (left-to-right) and insertion (FIM) mode are available.

cd evals/evaluation/bigcode_evaluation_harness/examples
python main.py \
    --model "codeparrot/codeparrot-small" \
    --tasks "humaneval" \
    --n_samples 100 \
    --batch_size 10 \
    --allow_code_execution

from evals.evaluation.bigcode_evaluation_harness import BigcodeEvalParser, evaluate

args = BigcodeEvalParser(
    user_model=user_model,
    tokenizer=tokenizer,
    tasks="humaneval",
    n_samples=100,
    batch_size=10,
    allow_code_execution=True,
)
results = evaluate(args)

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