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Essential Configurations for Evaluation

General Settings

  1. --batch_size works for both open-source and api model evaluation. When evaluating open-source models, you have to adjust the batch_size according to the GPU memory; when evaluating api models, --batch_size specifies the number of parallel calls to the target api model. You should set it properly according to your OpenAI user tier to avoid rate limits.

  2. --api_parallel_num specifies the number of parallel calls to the model parser api. In general, if you are a Tier-5 user, you can set --api_parallel_num to 100 or more to parse results in 30 seconds.

  3. Specify the --api_base_url if you wish to use other api such as llama.cpp server and Azure OpenAI API.

  4. You can use --max_gpu_memory to specify the maximum memory per GPU for storing model weights. This allows it to allocate more memory for activations, so you can use longer context lengths or larger batch_size. E.g., with 4 GPUs, we can set --max_gpu_memory 5GiB for gemma_11_7b_instruct.

  5. Model response files and scores will be saved to <output_folder>/<model_name>/<benchmark>/<version>/, for example, mix_eval/data/model_responses/gemma_11_7b_instruct/mixeval_hard/2024-06-01/. We take the overall score as the reported score in Leaderboard.

  6. There is a resuming mechanism, which means that if you run evaluation with the same config as the run you want to resume, it will resume from where it stopped last time.

  7. If you are evaluating base models, set the --extract_base_model_response flag to only retain the meaningful part in models' response when parsing to get more stablized parsing results.

  8. If you are evaluating api models, you should add a line in .env. E.g., for OpenAI key, you should add:

    k_oai=<your openai api key>

    The key name here is 'k_oai'. You can find the key name in the model's class. For example, claude_3_haiku's key can be found in mixeval.models.claude_3_haiku's __init__ function: api_key=os.getenv('k_ant'), where k_ant is the key name.

Evaluating Local Checkpoint

If you are evaluating a local checkpoint, specify the --model_path <your model path> and --model_name local_chat (or --model_name local_base if you are evaluating a base model):

python -m mix_eval.evaluate \
    --model_name local_chat \
    --model_path <your model path> \
    --benchmark mixeval_hard \
    --version 2024-06-01 \
    --batch_size 20 \
    --max_gpu_memory 5GiB \
    --output_dir mix_eval/data/model_responses/ \
    --api_parallel_num 20

Modify the mix_eval/models/local_chat.py or mix_eval/models/local_base.py according to your model config. You need to overwrite the build_model function if your checkpoint cannot be loaded by 'transformers.AutoModelForCausalLM.from_pretrained'. The same applies to build_tokenizer.

Use Other APIs for Model Parser

Azure OpenAI Endpoint

Some of you might use Azure OpenAI endpoint instead of direct usage of OpenAI API. You can simply drop you Azure credentials in the .env like this:

OPENAI_API_TYPE=azure
OPENAI_API_KEY=xyz
OPENAI_API_BASE=xyz
OPENAI_API_VERSION=2023-07-01-preview

❗ If you are using Azure, there shouldn't be a MODEL_PARSER_API entry in .env, otherwise it will still use the OpenAI api.

Other APIs

Specify the --api_base_url if you wish to use other api such as llama.cpp server.