init

README.md

@@ -1,3 +1,17 @@
 ---
+language:
+- ko
+pipeline_tag: text-generation
+tags:
+- SOLAR-10.7B
 license: cc-by-nc-4.0
 ---
+
+# SOLAR-10.7B
+
+### Model Details
+- Base Model: [yanolja/KoSOLAR-10.7B-v0.2](https://huggingface.co/yanolja/KoSOLAR-10.7B-v0.2)
+
+### 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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+  "architectures": [
+    "LlamaForCausalLM"
+  ],
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+  "model_type": "llama",
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+  "num_hidden_layers": 48,
+  "num_key_value_heads": 8,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 10000.0,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float16",
+  "transformers_version": "4.38.1",
+  "use_cache": false,
+  "vocab_size": 40960
+}

generation_config.json

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+  "bos_token_id": 1,
+  "eos_token_id": 32000,
+  "transformers_version": "4.38.1",
+  "use_cache": false
+}

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+  }
+}

results.json

@@ -0,0 +1,243 @@
+{
+  "results": {
+    "kobest_hellaswag": {
+      "acc,none": 0.49,
+      "acc_stderr,none": 0.02237859698923078,
+      "f1,none": 0.48693943710671983,
+      "f1_stderr,none": "N/A",
+      "acc_norm,none": 0.61,
+      "acc_norm_stderr,none": 0.02183468586936921,
+      "alias": "kobest_hellaswag"
+    },
+    "ko_truthfulqa": {
+      "acc,none": 0.2252141982864137,
+      "acc_stderr,none": 0.014623240768023488,
+      "alias": "ko_truthfulqa"
+    },
+    "ko_hellaswag": {
+      "acc,none": 0.4402509460266879,
+      "acc_stderr,none": 0.004954026775425785,
+      "acc_norm,none": 0.5879306910973909,
+      "acc_norm_stderr,none": 0.004912015369160031,
+      "alias": "ko_hellaswag"
+    },
+    "ko_common_gen": {
+      "acc,none": 0.8760600130463144,
+      "acc_stderr,none": 0.008418665797139064,
+      "acc_norm,none": 0.8760600130463144,
+      "acc_norm_stderr,none": 0.008418665797139064,
+      "alias": "ko_common_gen"
+    },
+    "ko_arc_easy": {
+      "acc,none": 0.371160409556314,
+      "acc_stderr,none": 0.014117971901142815,
+      "acc_norm,none": 0.43600682593856654,
+      "acc_norm_stderr,none": 0.014491225699230916,
+      "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 정당에 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
+  },
+  "n-shot": {
+    "ko_arc_easy": 0,
+    "ko_common_gen": 0,
+    "ko_hellaswag": 0,
+    "ko_truthfulqa": 0,
+    "kobest_hellaswag": 0
+  },
+  "config": {
+    "model": "hf",
+    "model_args": "pretrained=/root/simple_trainer/output/SOLAR-yanolja-v1.5/SFT,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

@@ -0,0 +1,30 @@
+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|im_end|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,54 @@
+{
+  "add_bos_token": true,
+  "add_eos_token": false,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32000": {
+      "content": "<|im_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "additional_special_tokens": [],
+  "bos_token": "<s>",
+  "chat_template": "{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% else %}{% set loop_messages = messages %}{% set system_message = 'You are a helpful assistant.' %}{% endif %}{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% for message in loop_messages %}{% if loop.index0 == 0 %}{{'<|im_start|>system\n' + system_message + '<|im_end|>\n'}}{% endif %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "legacy": true,
+  "model_max_length": 2048,
+  "pad_token": "</s>",
+  "padding_side": "right",
+  "sp_model_kwargs": {},
+  "spaces_between_special_tokens": false,
+  "tokenizer_class": "LlamaTokenizer",
+  "trust_remote_code": false,
+  "unk_token": "<unk>",
+  "use_default_system_prompt": false,
+  "use_fast": true
+}