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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
Fine-Tuning Information
- GPU: RTX4090 (single core / 24564MiB)
- model: meta-llama/Llama-2-13b-hf
- dataset: huangyt/FINETUNE3 (共約3.3w筆訓練集)
- peft_type: LoRA
- lora_rank: 16
- lora_target: q_proj, k_proj, v_proj, o_proj
- per_device_train_batch_size: 8
- gradient_accumulation_steps: 8
- learning_rate : 4e-4
- epoch: 1
- precision: bf16
- quantization: load_in_4bit
Fine-Tuning Detail
- train_loss: 0.579
- train_runtime: 4:6:11 (use deepspeed)
Evaluation
- 與Llama-2-13b比較4種Benchmark，包含ARC、HellaSwag、MMLU、TruthfulQA
- 評估結果使用本地所測的分數，並使用load_in_8bit
| Model | Average | ARC | HellaSwag | MMLU | TruthfulQA | Time (s) |
|---|---|---|---|---|---|---|
| FINETUNE5_4w-r4-q_k_v_o | 56.09 | 54.35 | 79.24 | 54.01 | 36.75 | 22095 |
| FINETUNE5_4w-r8-q_k_v_o | 57.55 | 55.38 | 79.57 | 54.03 | 41.21 | 22127 |
| FINETUNE5_4w-r16-q_k_v_o | 57.26 | 54.35 | 79.74 | 52.29 | 42.68 | 22153 |
| FINETUNE5_4w-r4-gate_up_down | 56.51 | 52.82 | 79.13 | 52.83 | 41.28 | 22899 |
| FINETUNE5_4w-r8-gate_up_down | 56.10 | 52.73 | 79.14 | 52.56 | 39.99 | 22926 |
| FINETUNE5_4w-r16-gate_up_down | 56.23 | 52.39 | 79.48 | 53.42 | 39.62 | 22963 |
| FINETUNE5_4w-r4-q_k_v_o_gate_up_down | 56.06 | 52.56 | 79.21 | 51.67 | 40.80 | 24303 |
| FINETUNE5_4w-r8-q_k_v_o_gate_up_down | 56.35 | 51.88 | 79.42 | 52.00 | 42.10 | 24376 |
| FINETUNE5_4w-r16-q_k_v_o_gate_up_down | 56.73 | 54.18 | 79.53 | 52.77 | 40.46 | 24439 |
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)
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("huangyt/FINETUNE5", split="train", streaming=True)
# 提取所需欄位並建立新的字典列表
extracted_data = []
for example in dataset:
extracted_example = {
"instruction": example["instruction"],
"input": example["input"],
"output": example["output"]
}
extracted_data.append(extracted_example)
# 指定 JSON 文件名稱
json_filename = "FINETUNE5.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}")
