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Llama-3.1-70B-Instruct-evals

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Dataset Card for Llama-3.1-70B-Instruct Evaluation Result Details

This dataset contains the Meta evaluation result details for Llama-3.1-70B-Instruct. The dataset has been created from 30 evaluation tasks. These tasks are human_eval, gorilla_api_bench__huggingface, mmlu_pro, infinite_bench, api_bank, human_eval_plus, ifeval__loose, mmlu__0_shot__cot, nih__multi_needle, multilingual_mmlu_de, mmlu, gsm8k, mgsm, multilingual_mmlu_fr, multilingual_mmlu_pt, math_hard, multilingual_mmlu_es, gpqa, zero_shot_scrolls, ifeval__strict, nexus, math, mbpp_plus, multilingual_mmlu_th, arc_challenge, multilingual_mmlu_hi, gorilla_api_bench__torchub, multilingual_mmlu_it, gorilla_api_bench__tensorhub, mbpp.

Each task detail can be found as a specific subset in each configuration and each subset is named using the task name plus the timestamp of the upload time and ends with "__details".

For more information about the eval tasks, please refer to this eval details page.

You can use the Viewer feature to view the dataset in the web browser easily. For most tasks, we provide an "is_correct" column, so you can quickly get our accuracy result of the task by viewing the percentage of "is_correct=True". For tasks that have both binary (eg. exact_match) and a continuous metrics (eg. f1), we will only consider the binary metric for adding the is_correct column. This might differ from the reported metric in the Llama 3.1 model card.

Additionally, there is a model metrics subset that contains all the reported metrics, like f1, macro_avg/acc, for all the tasks and subtasks. Please use this subset to find reported metrics in the model card.

Lastly, you can also use Huggingface Dataset APIs to load the dataset. For example, to load a eval task detail, you can use the following code:

from datasets import load_dataset
data = load_dataset(
    "meta-llama/Llama-3.1-70B-Instruct-evals",
    name="Llama-3.1-70B-Instruct-evals__mbpp__details",
    split="latest"
    )

Please check our eval recipe that demonstrates how to calculate the Llama 3.1 reported benchmark numbers using the lm-evaluation-harness library and the datasets in 3.1 evals collections on selected tasks.

Here are the explanations for each column in the task eval details:

task_type: Whether the eval task was run as a ‘Generative’ or ‘Choice’ task. Generative task returns the model output, whereas for choice tasks we return the negative log likelihoods of the completion. (The choice task approach is typically used for multiple choice tasks for non-instruct models)

task_name: Meta's internal eval task name

subtask_name: Meta internal subtask name in cases where the benchmark has subcategories (Ex. MMLU with domains)

input_question: The question from the input dataset when available. In cases when that data is overwritten as a part of the evaluation pipeline or it is a complex concatenation of input dataset fields, this will be the serialized prompt object as a string.

input_choice_list: In the case of multiple choice questions, this contains a map of the choice name to the text.

input_final_prompt: The final input text that is provided to the model for inference. For choice tasks, this will be an array of prompts provided to the model, where we calculate the likelihoods of the different completions in order to get the final answer provided by the model.

input_correct_responses: An array of correct responses to the input question.

output_prediction_text: The model output for a Generative task

output_parsed_answer: Typically answer we’ve parsed from the model output or calculated using negative log likelihoods. Sometimes this is a interpolation of answers parsed from the model output and fields from the benchmark example

output_choice_completions: For choice tasks, the list of completions we’ve provided to the model to calculate negative log likelihoods

output_choice_negative_log_likelihoods: For choice tasks, these are the corresponding negative log likelihoods normalized by different sequence lengths (text, token, raw) for the above completions.

output_metrics: Metrics calculated at the example level. Common metrics include:

acc - accuracy

em - exact_match

f1 - F1 score

pass@1 - For coding benchmarks, whether the output code passes tests

is_correct: Whether the parsed answer matches the target responses and is considered correct. (Only applicable for benchmarks which have such a boolean metric)

input_question_hash: The SHA256 hash of the question text encoded as UTF-8

input_final_prompts_hash: An array of SHA256 hash of the input prompt text encoded as UTF-8

benchmark_label: The commonly used benchmark name

eval_config: Additional metadata related to the configurations we used to run this evaluation:

num_generations - Generation parameter - how many outputs to generate

num_shots - How many few shot examples to include in the prompt.

max_gen_len - generation parameter (how many tokens to generate)

prompt_fn - The prompt function with jinja template when available

max_prompt_len - Generation parameter. Maximum number tokens for the prompt. If the input_final_prompt is longer than this configuration, we will truncat

return_logprobs - Generation parameter - Whether to return log probabilities when generating output.
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