93 lines
4.0 KiB
Markdown
93 lines
4.0 KiB
Markdown
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---
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license: llama2
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datasets:
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- huangyt/FINETUNE3
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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/FINETUNE3資料集進行訓練，總資料筆數約3.3w
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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/FINETUNE3 (共約3.3w筆訓練集)
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- **peft_type:** LoRA
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- **lora_rank:** 16
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- **lora_target:** q_proj, k_proj, v_proj, o_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 :** 4e-4
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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:** 0.579
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- **train_runtime:** 4:6:11 (use deepspeed)
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# Evaluation
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- 與Llama-2-13b比較4種Benchmark，包含**ARC**、**HellaSwag**、**MMLU**、**TruthfulQA**
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- 評估結果使用**本地**所測的分數，並使用load_in_8bit
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| Model |Average| ARC |HellaSwag| MMLU | TruthfulQA |
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|-----------------------------------------|-------|-------|---------|-------|------------|
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| FINETUNE3_3.3w-r4-q_k_v_o | 56.29 | 54.27 | 79.42 | 51.90 | 39.58 |
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| FINETUNE3_3.3w-r8-q_k_v_o | 56.53 | 52.99 | 79.45 | 53.53 | 40.14 |
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| FINETUNE3_3.3w-r16-q_k_v_o | 56.25 | 53.24 | 79.53 | 54.03 | 38.20 |
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| FINETUNE3_3.3w-r4-gate_up_down | 55.79 | 51.02 | 79.37 | 53.36 | 39.40 |
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| FINETUNE3_3.3w-r8-gate_up_down | 56.60 | 53.33 | 79.43 | 53.60 | 40.03 |
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| FINETUNE3_3.3w-r16-gate_up_down | 56.34 | 51.88 | 79.42 | 54.64 | 39.44 |
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| FINETUNE3_3.3w-r4-q_k_v_o_gate_up_down | 56.67 | 53.07 | 79.34 | 54.07 | 40.19 |
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| FINETUNE3_3.3w-r8-q_k_v_o_gate_up_down | 56.93 | 54.61 | 79.16 | 53.51 | 40.46 |
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| FINETUNE3_3.3w-r16-q_k_v_o_gate_up_down | 57.78 | 53.92 | 79.41 | 54.68 | 43.09 |
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-------------------------------------------------------------------------------------------
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- 評估結果來自**HuggingFaceH4/open_llm_leaderboard**
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| Model |Average| ARC |HellaSwag| MMLU | TruthfulQA |
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|-----------------------------------------|-------|-------|---------|-------|------------|
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| FINETUNE3_3.3w-r4-q_k_v_o | 58.34 | 59.04 | 81.15 | 53 | 40.16 |
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| FINETUNE3_3.3w-r8-q_k_v_o | 58.28 | 56.06 | 81.89 | 55.04 | 40.12 |
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| FINETUNE3_3.3w-r16-q_k_v_o | 58.55 | 59.3 | 81.2 | 55.58 | 38.13 |
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| FINETUNE3_3.3w-r4-gate_up_down | 57.79 | 56.4 | 81.93 | 53.63 | 39.23 |
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| FINETUNE3_3.3w-r8-gate_up_down | 58.17 | 57.25 | 81.79 | 53.96 | 39.66 |
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| FINETUNE3_3.3w-r16-gate_up_down | 58.91 | 58.7 | 81.89 | 56.08 | 38.95 |
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| FINETUNE3_3.3w-r4-q_k_v_o_gate_up_down | 58.42 | 57.76 | 80.78 | 54.32 | 40.8 |
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| FINETUNE3_3.3w-r8-q_k_v_o_gate_up_down | 58.26 | 57.94 | 81.19 | 53.43 | 40.48 |
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| FINETUNE3_3.3w-r16-q_k_v_o_gate_up_down | 59.62 | 59.22 | 81.52 | 54.94 | 42.83 |
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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/FINETUNE3", 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 = "FINETUNE3.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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