add llama3 tasks

lm_eval/tasks/llama3/README.md

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+# Task-name
+
+### Paper
+
+Title: ``
+
+Abstract: ``
+
+
+Homepage: ``
+
+
+### Citation
+
+```
+
+```
+
+### Groups, Tags, and Tasks
+
+#### Groups
+
+
+
+#### Subgroups
+
+
+### Tasks
+
+* `llama_arc_challenge`: 25-shot multiple-choice ARC challenge.
+* `mgsm_chat`: 0-shot mgsm benchmark. Use with chat-template.
+
+### Checklist
+
+For adding novel benchmarks/datasets to the library:
+* [x] Is the task an existing benchmark in the literature?
+  * [x] Have you referenced the original paper that introduced the task?
+  * [x] If yes, does the original paper provide a reference implementation? If so, have you checked against the reference implementation and documented how to run such a test?
+
+
+If other tasks on this dataset are already supported:
+* [x] Is the "Main" variant of this task clearly denoted?
+* [x] Have you provided a short sentence in a README on what each new variant adds / evaluates?
+* [x] Have you noted which, if any, published evaluation setups are matched by this variant?

lm_eval/tasks/llama3/base/arc_challenge.yaml

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+tag:
+  - llama
+task: llama_arc_challenge
+dataset_path: allenai/ai2_arc
+dataset_name: ARC-Challenge
+output_type: multiple_choice
+training_split: train
+validation_split: validation
+test_split: test
+fewshot_split: train
+doc_to_text: "Question: {{question.strip()}}\nA. {{choices.text[0]}}\nB. {{choices.text[1]}}\nC. {{choices.text[2]}}{% if choices.text|length > 3 %}\nD. {{choices.text[3]}}{% endif %}\nAnswer:"
+fewshot_delimiter: "\n\n"
+doc_to_target: "{{ 'ABCD'[answerKey|int - 1] if answerKey|string in '1234' else answerKey }}"
+doc_to_choice: "{{ choices.label|map('replace', '1', 'A')|map('replace', '2', 'B')|map('replace', '3', 'C')|map('replace', '4', 'D')|list if choices.label[0] in '1234' else choices.label }}"
+num_fewshot: 25
+metric_list:
+  - metric: acc
+    aggregation: mean
+    higher_is_better: true
+  - metric: acc_norm
+    aggregation: mean
+    higher_is_better: true
+metadata:
+  version: 1.0

lm_eval/tasks/llama3/base/utils.py

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+import datasets
+
+
+def process_arc_c_docs(dataset: datasets.Dataset) -> datasets.Dataset:
+    COLUMNS = dataset.column_names
+
+    def map_(doc):
+        doc["doc_to_text"] = doc["input_final_prompts"][0].strip()[:-2].strip()
+        doc["doc_to_choice"] = [
+            x.replace("Answer:", "").strip() for x in doc["output_choice_completions"]
+        ]
+        doc["doc_to_target"] = doc["input_correct_responses"][0].strip()[-1]
+        return doc
+
+    return dataset.map(map_, remove_columns=COLUMNS)

lm_eval/tasks/llama3/instruct/mgsm_chat.yaml

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+tag: llama3
+task: mgsm_chat
+dataset_path: meta-llama/Llama-3.2-3B-Instruct-evals
+dataset_name: Llama-3.2-3B-Instruct-evals__mgsm__details
+output_type: generate_until
+test_split: latest
+doc_to_text: "{{
+    input_final_prompts
+    |first
+    |replace('<|eot_id|><|start_header_id|>assistant<|end_header_id|>', '')
+    |replace('<|start_header_id|>', '')
+    |replace('<|end_header_id|>', '')
+    |replace('<|eot_id|>', '')
+    |replace('^user', '')
+    |trim
+}}"
+doc_to_target: "input_correct_responses"
+process_results: !function utils.process_results_mgsm
+generation_kwargs:
+  until: []
+  do_sample: false
+  temperature: 0.0
+  max_gen_toks: 2048
+metric_list:
+  - metric: exact_match
+    aggregation: mean
+    higher_is_better: true
+    ignore_case: true
+    ignore_punctuation: true
+filter_list:
+  - name: "strict-match"
+    filter:
+      - function: "regex"
+        regex_pattern: "Answer: (\\-?[0-9\\.\\,]+)"
+      - function: "take_first"
+  - name: "flexible-extract"
+    filter:
+      - function: regex
+        group_select: -1
+        regex_pattern: "Answer: (-?[$0-9.,]{2,})|(-?[0-9]+)"
+      - function: take_first
+      - function: remove_whitespace
+      - function: take_first
+metadata:
+  version: 0.0

lm_eval/tasks/llama3/instruct/utils.py

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+from typing import List
+
+from lm_eval.api.metrics import exact_match_fn
+
+
+def process_results_mgsm(doc, prediction):
+    gold: List = doc["input_correct_responses"]
+    return {
+        "exact_match": int(
+            exact_match_fn(
+                predictions=prediction * len(gold), references=gold, ignore_case=True
+            )["exact_match"]
+            > 0
+        )
+    }