75 lines
2.8 KiB
Markdown
75 lines
2.8 KiB
Markdown
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---
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license: llama2
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datasets:
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- huangyt/FINETUNE1
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---
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# Model Card for Model ID
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<!-- Provide a quick summary of what the model is/does. -->
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在llama-2-13b上使用huangyt/FINETUNE1資料集進行訓練，總資料筆數約17w
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# Fine-Tuning Information
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- **GPU:** RTX4090 (single core / 24564MiB)
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- **model:** meta-llama/Llama-2-13b-hf
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- **dataset:** huangyt/FINETUNE1 (共約17w筆訓練集)
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- **peft_type:** LoRA
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- **lora_rank:** 16
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- **lora_target:** gate_proj, up_proj, down_proj
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- **per_device_train_batch_size:** 8
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- **gradient_accumulation_steps:** 8
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- **learning_rate :** 5e-5
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- **epoch:** 1
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- **precision:** bf16
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- **quantization:** load_in_4bit
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# Fine-Tuning Detail
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- **train_loss:** 0.66
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- **train_runtime:** 16:26:58 (use deepspeed)
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# Evaluation
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- 評估結果來自**HuggingFaceH4/open_llm_leaderboard**
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- 與Llama-2-13b比較4種Benchmark，包含**ARC**、**HellaSwag**、**MMLU**、**TruthfulQA**
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| Model |Average| ARC |HellaSwag| MMLU |TruthfulQA|
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|--------------------------------------------------------|-------|-------|---------|-------|----------|
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|meta-llama/Llama-2-13b-hf | 56.9 | 58.11 | 80.97 | 54.34 | 34.17 |
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|meta-llama/Llama-2-13b-chat-hf | 59.93 | 59.04 | 81.94 | 54.64 | 44.12 |
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|CHIH-HUNG/llama-2-13b-Fintune_1_17w | 58.24 | 59.47 | 81 | 54.31 | 38.17 |
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|CHIH-HUNG/llama-2-13b-huangyt_Fintune_1_17w-q_k_v_o_proj| 58.49 | 59.73 | 81.06 | 54.53 | 38.64 |
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|CHIH-HUNG/llama-2-13b-Fintune_1_17w-gate_up_down_proj | 58.81 | 57.17 | 82.26 | 55.89 | 39.93 |
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|CHIH-HUNG/llama-2-13b-FINETUNE1_17w-r16 | 58.86 | 57.25 | 82.27 | 56.16 | 39.75 |
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|CHIH-HUNG/llama-2-13b-FINETUNE1_17w-r4 | 58.71 | 56.74 | 82.27 | 56.18 | 39.65 |
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# How to convert dataset to json
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- 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
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- 觀察該資料集的欄位名稱，填入**example**欄位中(例如system_prompt、question、response)
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- 最後指定json檔儲存位置 (**json_filename**)
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```py
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import json
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from datasets import load_dataset
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# 讀取數據集，take可以取得該數據集前n筆資料
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dataset = load_dataset("huangyt/FINETUNE1", split="train", streaming=True)
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# 提取所需欄位並建立新的字典列表
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extracted_data = []
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for example in dataset:
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extracted_example = {
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"instruction": example["instruction"],
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"input": example["input"],
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"output": example["output"]
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}
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extracted_data.append(extracted_example)
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# 指定 JSON 文件名稱
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json_filename = "huangyt_FINETUNE_1.json"
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# 寫入 JSON 文件
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with open(json_filename, "w") as json_file:
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json.dump(extracted_data, json_file, indent=4)
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print(f"數據已提取並保存為 {json_filename}")
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```
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