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在llama-2-13b上使用huangyt/FINETUNE4資料集進行訓練，總資料筆數約3.8w
Fine-Tuning Information
- GPU: RTX4090 (single core / 24564MiB)
- model: meta-llama/Llama-2-13b-hf
- dataset: huangyt/FINETUNE3 (共約3.8w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 |
|---|---|---|---|---|---|
| FINETUNE4_3.8w-r4-q_k_v_o | 56.67 | 52.13 | 79.38 | 54.54 | 40.64 |
| FINETUNE4_3.8w-r8-q_k_v_o | 56.84 | 52.30 | 79.58 | 54.50 | 40.98 |
| FINETUNE4_3.8w-r16-q_k_v_o | 57.28 | 53.92 | 79.92 | 55.61 | 39.65 |
| FINETUNE4_3.8w-r4-gate_up_down | 55.93 | 51.71 | 79.13 | 53.24 | 39.63 |
| FINETUNE4_3.8w-r8-gate_up_down | 55.93 | 51.37 | 79.29 | 53.62 | 39.45 |
| FINETUNE4_3.8w-r16-gate_up_down | 56.35 | 52.56 | 79.28 | 55.27 | 38.31 |
| FINETUNE4_3.8w-r4-q_k_v_o_gate_up_down | 56.42 | 53.92 | 79.09 | 53.93 | 38.74 |
| FINETUNE4_3.8w-r8-q_k_v_o_gate_up_down | 56.11 | 51.02 | 79.24 | 53.11 | 41.08 |
| FINETUNE4_3.8w-r16-q_k_v_o_gate_up_down | 56.83 | 53.67 | 79.49 | 54.79 | 39.36 |
- 評估結果來自HuggingFaceH4/open_llm_leaderboard
| Model | Average | ARC | HellaSwag | MMLU | TruthfulQA |
|---|---|---|---|---|---|
| FINETUNE4_3.8w-r4-q_k_v_o | 57.98 | 54.78 | 81.4 | 54.73 | 41.02 |
| FINETUNE4_3.8w-r8-q_k_v_o | 58.96 | 57.68 | 81.91 | 54.95 | 41.31 |
| FINETUNE4_3.8w-r16-q_k_v_o | 58.46 | 56.23 | 81.98 | 55.87 | 39.76 |
| FINETUNE4_3.8w-r4-gate_up_down | 57.94 | 55.8 | 81.74 | 55.09 | 39.12 |
| FINETUNE4_3.8w-r8-gate_up_down | 57.85 | 54.35 | 82.13 | 55.33 | 39.6 |
| FINETUNE4_3.8w-r16-gate_up_down | 57.93 | 55.03 | 81.97 | 56.64 | 38.07 |
| FINETUNE4_3.8w-r4-q_k_v_o_gate_up_down | 58.04 | 56.31 | 81.43 | 55.3 | 39.11 |
| FINETUNE4_3.8w-r8-q_k_v_o_gate_up_down | 58.16 | 55.97 | 81.53 | 54.42 | 40.72 |
| FINETUNE4_3.8w-r16-q_k_v_o_gate_up_down | 58.61 | 57.25 | 81.49 | 55.9 | 39.79 |
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/FINETUNE4", 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 = "FINETUNE4.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}")
