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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
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 |
|---|---|---|---|---|---|
| FINETUNE3_3.3w-r4-q_k_v_o | 56.29 | 54.27 | 79.42 | 51.90 | 39.58 |
| FINETUNE3_3.3w-r8-q_k_v_o | 56.53 | 52.99 | 79.45 | 53.53 | 40.14 |
| FINETUNE3_3.3w-r16-q_k_v_o | 56.25 | 53.24 | 79.53 | 54.03 | 38.20 |
| FINETUNE3_3.3w-r4-gate_up_down | 55.79 | 51.02 | 79.37 | 53.36 | 39.40 |
| FINETUNE3_3.3w-r8-gate_up_down | 56.60 | 53.33 | 79.43 | 53.60 | 40.03 |
| FINETUNE3_3.3w-r16-gate_up_down | 56.34 | 51.88 | 79.42 | 54.64 | 39.44 |
| FINETUNE3_3.3w-r4-q_k_v_o_gate_up_down | 56.67 | 53.07 | 79.34 | 54.07 | 40.19 |
| FINETUNE3_3.3w-r8-q_k_v_o_gate_up_down | 56.93 | 54.61 | 79.16 | 53.51 | 40.46 |
| FINETUNE3_3.3w-r16-q_k_v_o_gate_up_down | 57.78 | 53.92 | 79.41 | 54.68 | 43.09 |
- 評估結果來自HuggingFaceH4/open_llm_leaderboard
| Model | Average | ARC | HellaSwag | MMLU | TruthfulQA |
|---|---|---|---|---|---|
| FINETUNE3_3.3w-r4-q_k_v_o | 58.34 | 59.04 | 81.15 | 53 | 40.16 |
| FINETUNE3_3.3w-r8-q_k_v_o | 58.28 | 56.06 | 81.89 | 55.04 | 40.12 |
| FINETUNE3_3.3w-r16-q_k_v_o | 58.55 | 59.3 | 81.2 | 55.58 | 38.13 |
| FINETUNE3_3.3w-r4-gate_up_down | 57.79 | 56.4 | 81.93 | 53.63 | 39.23 |
| FINETUNE3_3.3w-r8-gate_up_down | 58.17 | 57.25 | 81.79 | 53.96 | 39.66 |
| FINETUNE3_3.3w-r16-gate_up_down | 58.91 | 58.7 | 81.89 | 56.08 | 38.95 |
| FINETUNE3_3.3w-r4-q_k_v_o_gate_up_down | 58.42 | 57.76 | 80.78 | 54.32 | 40.8 |
| FINETUNE3_3.3w-r8-q_k_v_o_gate_up_down | 58.26 | 57.94 | 81.19 | 53.43 | 40.48 |
| FINETUNE3_3.3w-r16-q_k_v_o_gate_up_down | 59.62 | 59.22 | 81.52 | 54.94 | 42.83 |
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/FINETUNE3", 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 = "FINETUNE3.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}")
