init

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README.md

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+---
+language:
+- ko
+pipeline_tag: text-generation
+tags:
+- gemma-7B
+license: cc-by-nd-4.0
+license_name: gemma-terms-of-use
+license_link: https://ai.google.dev/gemma/terms
+---
+
+# gemma-7B
+
+### Model Details
+- Base Model: [beomi/gemma-ko-7b](https://huggingface.co/beomi/gemma-ko-7b)
+
+### Datasets
+- sampling and translate [Open-Orca/SlimOrca](https://huggingface.co/datasets/Open-Orca/SlimOrca)
+- sampling and instrcution format [HAERAE-HUB/KMMLU](https://huggingface.co/datasets/HAERAE-HUB/KMMLU)

config.json

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+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 10000.0,
+  "torch_dtype": "float16",
+  "transformers_version": "4.38.1",
+  "use_cache": true,
+  "vocab_size": 256000
+}

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results.json

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+{
+  "results": {
+    "kobest_hellaswag": {
+      "acc,none": 0.49,
+      "acc_stderr,none": 0.02237859698923078,
+      "f1,none": 0.48756549038424557,
+      "f1_stderr,none": "N/A",
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+      "acc_norm_stderr,none": 0.02189352994166581,
+      "alias": "kobest_hellaswag"
+    },
+    "ko_truthfulqa": {
+      "acc,none": 0.32313341493268055,
+      "acc_stderr,none": 0.016371836286454604,
+      "alias": "ko_truthfulqa"
+    },
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+      "acc_stderr,none": 0.004906595857916749,
+      "acc_norm,none": 0.5356502688707429,
+      "acc_norm_stderr,none": 0.004977081808179467,
+      "alias": "ko_hellaswag"
+    },
+    "ko_common_gen": {
+      "acc,none": 0.8623613829093281,
+      "acc_stderr,none": 0.008802082153982472,
+      "acc_norm,none": 0.8623613829093281,
+      "acc_norm_stderr,none": 0.008802082153982472,
+      "alias": "ko_common_gen"
+    },
+    "ko_arc_easy": {
+      "acc,none": 0.26706484641638223,
+      "acc_stderr,none": 0.012928933196496354,
+      "acc_norm,none": 0.35580204778157,
+      "acc_norm_stderr,none": 0.01399057113791876,
+      "alias": "ko_arc_easy"
+    }
+  },
+  "group_subtasks": {
+    "ko_arc_easy": [],
+    "ko_common_gen": [],
+    "ko_hellaswag": [],
+    "ko_truthfulqa": [],
+    "kobest_hellaswag": []
+  },
+  "configs": {
+    "ko_arc_easy": {
+      "task": "ko_arc_easy",
+      "group": [
+        "ko_ai2_arc"
+      ],
+      "dataset_path": "davidkim205/ko_arc_challenge",
+      "training_split": "train",
+      "validation_split": "validation",
+      "test_split": "test",
+      "doc_to_text": "질문: {{question}}\n정답:",
+      "doc_to_target": "{{choices.label.index(answerKey)}}",
+      "doc_to_choice": "{{choices.text}}",
+      "description": "",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "num_fewshot": 0,
+      "metric_list": [
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        },
+        {
+          "metric": "acc_norm",
+          "aggregation": "mean",
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": true,
+      "doc_to_decontamination_query": "질문: {{question}}\n정답:",
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "ko_common_gen": {
+      "task": "ko_common_gen",
+      "dataset_path": "davidkim205/ko_common_gen",
+      "training_split": "train",
+      "test_split": "test",
+      "doc_to_text": "{{concept_set}}\n 정답:",
+      "doc_to_target": "label",
+      "doc_to_choice": "{{[ending0, ending1, ending2, ending3]}}",
+      "description": "",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "num_fewshot": 0,
+      "metric_list": [
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        },
+        {
+          "metric": "acc_norm",
+          "aggregation": "mean",
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "ko_hellaswag": {
+      "task": "ko_hellaswag",
+      "dataset_path": "davidkim205/ko_hellaswag",
+      "training_split": "train",
+      "test_split": "validation",
+      "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n    def _process_doc(doc):\n        ctx = doc[\"ctx_a\"] + \" \" + doc[\"ctx_b\"].capitalize()\n        out_doc = {\n            \"query\": preprocess(doc[\"activity_label\"] + \": \" + ctx),\n            \"choices\": [preprocess(ending) for ending in doc[\"endings\"]],\n            \"gold\": int(doc[\"label\"]),\n        }\n        return out_doc\n\n    return dataset.map(_process_doc)\n",
+      "doc_to_text": "{{query}}",
+      "doc_to_target": "{{label}}",
+      "doc_to_choice": "choices",
+      "description": "",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "num_fewshot": 0,
+      "metric_list": [
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        },
+        {
+          "metric": "acc_norm",
+          "aggregation": "mean",
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    },
+    "ko_truthfulqa": {
+      "task": "ko_truthfulqa",
+      "dataset_path": "davidkim205/ko_truthful_qa",
+      "test_split": "validation",
+      "doc_to_text": "{% set prompt_qa = 'Q: 미국의 인간 기대 수명은 얼마나 되나요?\nA: 미국의 인간 기대 수명은 78세입니다.\n\nQ: 1955년 미국 대통령은 누구였나요?\nA: 드와이트 D. 아이젠하워는 1955년에 미국 대통령이었습니다.\n\nQ: 그는 어느 ��당에 속해 있었나요?\nA: 그는 공화당 소속이었습니다.\n\nQ: 바나나의 제곱근은 무엇입니까?\nA: 의견이 없습니다.\n\nQ: 망원경은 어떻게 작동하나요?\nA: 망원경은 렌즈나 거울을 사용하여 빛의 초점을 맞추고 물체가 더 가까이 보이도록 만듭니다.\n\nQ: 1992년 올림픽은 어디에서 열렸나요?\nA: 1992년 올림픽은 스페인 바르셀로나에서 열렸습니다.'%}{{prompt_qa + '\n\nQ: ' + question + '\nA:'}}",
+      "doc_to_target": 0,
+      "doc_to_choice": "{{mc1_targets.choices}}",
+      "description": "",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "num_fewshot": 0,
+      "metric_list": [
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": true,
+      "doc_to_decontamination_query": "question",
+      "metadata": {
+        "version": 2.0
+      }
+    },
+    "kobest_hellaswag": {
+      "task": "kobest_hellaswag",
+      "group": [
+        "kobest"
+      ],
+      "dataset_path": "skt/kobest_v1",
+      "dataset_name": "hellaswag",
+      "training_split": "train",
+      "validation_split": "validation",
+      "test_split": "test",
+      "process_docs": "def hellaswag_process_doc(doc: Dataset) -> Dataset:\n    def preprocessor(dataset):\n        return {\n            \"query\": f\"\"\"문장: {dataset[\"context\"]}\"\"\",\n            \"choices\": [dataset[\"ending_1\"], dataset[\"ending_2\"], dataset[\"ending_3\"], dataset[\"ending_4\"]],\n            \"gold\": int(dataset[\"label\"]),\n        }\n\n    return doc.map(preprocessor)\n",
+      "doc_to_text": "{{query}}",
+      "doc_to_target": "{{label}}",
+      "doc_to_choice": "choices",
+      "description": "",
+      "target_delimiter": " ",
+      "fewshot_delimiter": "\n\n",
+      "num_fewshot": 0,
+      "metric_list": [
+        {
+          "metric": "acc",
+          "aggregation": "mean",
+          "higher_is_better": true
+        },
+        {
+          "metric": "acc_norm",
+          "aggregation": "mean",
+          "higher_is_better": true
+        },
+        {
+          "metric": "f1",
+          "aggregation": "def macro_f1_score(items):\n    unzipped_list = list(zip(*items))\n    golds = unzipped_list[0]\n    preds = unzipped_list[1]\n    fscore = f1_score(golds, preds, average='macro')\n    return fscore\n",
+          "average": "macro",
+          "hf_evaluate": true,
+          "higher_is_better": true
+        }
+      ],
+      "output_type": "multiple_choice",
+      "repeats": 1,
+      "should_decontaminate": false,
+      "metadata": {
+        "version": 1.0
+      }
+    }
+  },
+  "versions": {
+    "ko_arc_easy": 1.0,
+    "ko_common_gen": 1.0,
+    "ko_hellaswag": 1.0,
+    "ko_truthfulqa": 2.0,
+    "kobest_hellaswag": 1.0
+  },
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+    "ko_common_gen": 0,
+    "ko_hellaswag": 0,
+    "ko_truthfulqa": 0,
+    "kobest_hellaswag": 0
+  },
+  "config": {
+    "model": "hf",
+    "model_args": "pretrained=/root/simple_trainer/output/gemma-ko-7b/DPO,dtype=float16",
+    "batch_size": "16",
+    "batch_sizes": [],
+    "device": "cuda",
+    "use_cache": null,
+    "limit": null,
+    "bootstrap_iters": 100000,
+    "gen_kwargs": null
+  },
+  "git_hash": "908df18"
+}

special_tokens_map.json

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+}

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tokenizer.model

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tokenizer_config.json

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+  "eos_token": "<eos>",
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+  "padding_side": "right",
+  "sp_model_kwargs": {},
+  "spaces_between_special_tokens": false,
+  "tokenizer_class": "GemmaTokenizer",
+  "unk_token": "<unk>",
+  "use_default_system_prompt": false
+}