metadata

license: llama2
datasets:
  - garage-bAInd/Open-Platypus

Model Card for Model ID

在llama-2-13b上使用garage-bAInd/Open-Platypus資料集進行訓練，總資料筆數約2.5w + ccp

Fine-Tuning Information

GPU: RTX4090 (single core / 24564MiB)
model: meta-llama/Llama-2-13b-hf
dataset: garage-bAInd/Open-Platypus (共約2.5w筆訓練集) + ccp (約1200筆)
peft_type: LoRA
lora_rank: 8
lora_target: gate_proj, up_proj, down_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: 4:07:24 (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
Open-Orca/OpenOrca-Platypus2-13B	63.19	61.52	82.27	58.85	50.11
CHIH-HUNG/llama-2-13b-Open_Platypus_and_ccp_2.6w	59.41	58.96	82.51	56.12	40.07

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instruction、input、output)
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("garage-bAInd/Open-Platypus", 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 = "Open-Platypus.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}")