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Model: CHIH-HUNG/llama-2-13b-OpenOrca_5w
Source: Original Platform

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README.md

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+---
+license: llama2
+datasets:
+- Open-Orca/OpenOrca
+---
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+在llama-2-13b上使用open orca前5萬筆資料集進行訓練
+
+# Fine-Tuning Information
+- **GPU:** RTX4090 (single core / 24564MiB)
+- **model:** meta-llama/Llama-2-13b-hf
+- **dataset:** Open-Orca/OpenOrca (取前5w筆訓練集)
+- **peft_type:** LoRA
+- **lora_rank:** 8
+- **lora_target:** q_proj, v_proj
+- **per_device_train_batch_size:** 8
+- **gradient_accumulation_steps:** 8
+- **learning_rate :** 5e-5
+- **epoch:** 1
+- **precision:** bf16
+- **quantization:** load_in_4bit
+
+# Fine-Tuning Detail
+- **train_loss:** 0.903261117822906
+- **train_runtime:** 7:19:57 (use deepspeed)
+
+# Evaluation
+- 評估結果來自**HuggingFaceH4/open_llm_leaderboard**
+- 與Llama-2-13b和其他使用Open-Orca的模型比較4種Benchmark
+- Benchmark包含**ARC**、**HellaSwag**、**MMLU**、**TruthfulQA**
+
+| Model                                   |Average|  ARC  |HellaSwag| MMLU  | TruthfulQA |
+|-----------------------------------------|-------|-------|---------|-------|------------|
+|meta-llama/Llama-2-13b-hf                | 56.9  | 58.11 |  80.97  | 54.34 |   34.17    |
+|meta-llama/Llama-2-13b-chat-hf           | 59.93 | 59.04 |  81.94  | 54.64 |   44.12    |
+|Open-Orca/OpenOrca-Platypus2-13B         | 64.6  | 62.8  |  83.15  | 59.39 |   53.08    |
+|Open-Orca/OpenOrcaxOpenChat-Preview2-13B | 63.81 | 62.37 |  82.96  | 58.68 |   51.23    |
+|circulus/Llama-2-13b-orca-v1             | 62.91 | 62.03 |  82.27  | 57.71 |   49.61    |
+|CHIH-HUNG/llama-2-13b-open_orca_20w      | 60.46 | 59.9  |  82.51  | 56.3  |   43.14    |
+|CHIH-HUNG/llama-2-13b-OpenOrca_5w        | 61.2  | 61.01 |  82.82  | 56.09 |   44.87    |
+
+
+# How to convert dataset to json
+
+- 在**load_dataset**中輸入資料集名稱，並且在**take**中輸入要取前幾筆資料
+- 觀察該資料集的欄位名稱，填入**example**欄位中(例如system_prompt、question、response)
+- 最後指定json檔儲存位置 (**json_filename**)
+
+```py
+import json
+from datasets import load_dataset
+
+# 讀取數據集，take可以取得該數據集前n筆資料
+dataset = load_dataset("Open-Orca/OpenOrca", split="train", streaming=True).take(50000)
+
+# 提取所需欄位並建立新的字典列表
+extracted_data = []
+for example in dataset:
+    extracted_example = {
+        ### open orca
+        "system_prompt": example["system_prompt"],
+        "question": example["question"],
+        "response": example["response"]
+    }
+    extracted_data.append(extracted_example)
+
+# 指定 JSON 文件名稱
+json_filename = "open_orca.json"
+
+# 寫入 JSON 文件
+with open(json_filename, "w") as json_file:
+    json.dump(extracted_data, json_file, indent=4)
+
+print(f"數據已提取並保存為 {json_filename}")
+```

config.json

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+{
+  "_name_or_path": "meta-llama/Llama-2-13b-hf",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 5120,
+  "initializer_range": 0.02,
+  "intermediate_size": 13824,
+  "max_position_embeddings": 4096,
+  "model_type": "llama",
+  "num_attention_heads": 40,
+  "num_hidden_layers": 40,
+  "num_key_value_heads": 40,
+  "pad_token_id": 0,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float16",
+  "transformers_version": "4.31.0",
+  "use_cache": true,
+  "vocab_size": 32000
+}

generation_config.json

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+{
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+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "pad_token_id": 0,
+  "transformers_version": "4.31.0"
+}

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