77 lines
3.0 KiB
Markdown
77 lines
3.0 KiB
Markdown
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---
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license: llama2
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datasets:
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- huangyt/FINETUNE5
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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/FINETUNE5資料集進行訓練，總資料筆數約4w
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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 | Time (s) |
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|---------------------------------------|---------|---------|---------|-------|------------|----------|
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| FINETUNE5_4w-r4-q_k_v_o | 56.09 | 54.35 | 79.24 | 54.01 | 36.75 | 22095 |
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| FINETUNE5_4w-r8-q_k_v_o | 57.55 | 55.38 | 79.57 | 54.03 | 41.21 | 22127 |
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| FINETUNE5_4w-r16-q_k_v_o | 57.26 | 54.35 | 79.74 | 52.29 | 42.68 | 22153 |
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| FINETUNE5_4w-r4-gate_up_down | 56.51 | 52.82 | 79.13 | 52.83 | 41.28 | 22899 |
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| FINETUNE5_4w-r8-gate_up_down | 56.10 | 52.73 | 79.14 | 52.56 | 39.99 | 22926 |
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| FINETUNE5_4w-r16-gate_up_down | 56.23 | 52.39 | 79.48 | 53.42 | 39.62 | 22963 |
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| FINETUNE5_4w-r4-q_k_v_o_gate_up_down | 56.06 | 52.56 | 79.21 | 51.67 | 40.80 | 24303 |
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| FINETUNE5_4w-r8-q_k_v_o_gate_up_down | 56.35 | 51.88 | 79.42 | 52.00 | 42.10 | 24376 |
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| FINETUNE5_4w-r16-q_k_v_o_gate_up_down | 56.73 | 54.18 | 79.53 | 52.77 | 40.46 | 24439 |
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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/FINETUNE5", 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 = "FINETUNE5.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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