metadata

license: llama2
datasets:
  - huangyt/FINETUNE2

Model Card for Model ID

在llama-2-13b上使用huangyt/FINETUNE2資料集進行訓練，總資料筆數約3w

Fine-Tuning Information

GPU: RTX4090 (single core / 24564MiB)
model: meta-llama/Llama-2-13b-hf
dataset: huangyt/FINETUNE2 (共約3w筆訓練集)
peft_type: LoRA
lora_rank: 8
lora_target: q_proj, v_proj
per_device_train_batch_size: 8
gradient_accumulation_steps: 8
learning_rate : 5e-5
epoch: 1
precision: bf16
quantization: load_in_4bit

Fine-Tuning Detail

train_loss: 0.67
train_runtime: 3:27:00 (use deepspeed)

Evaluation

評估結果來自HuggingFaceH4/open_llm_leaderboard
與Llama-2-13b比較4種Benchmark，包含ARC、HellaSwag、MMLU、TruthfulQA

Model	Average	ARC	HellaSwag	MMLU	TruthfulQA
meta-llama/Llama-2-13b-hf	56.9	58.11	80.97	54.34	34.17
meta-llama/Llama-2-13b-chat-hf	59.93	59.04	81.94	54.64	44.12
CHIH-HUNG/llama-2-13b-FINETUNE2_3w-q_k_v_o_proj	58.21	58.53	82.47	53.9	37.92
CHIH-HUNG/llama-2-13b-FINETUNE2_3w	58.34	58.62	82.32	54.25	38.17
CHIH-HUNG/llama-2-13b-FINETUNE2_3w-gate_up_down_proj	58.65	57.42	82.42	55.57	39.19

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/FINETUNE2", 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 = "huangyt_FINETUNE2.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}")