93 lines
4.0 KiB
Markdown
93 lines
4.0 KiB
Markdown
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---
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license: llama2
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datasets:
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- huangyt/FINETUNE4
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---
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# Model Card for Model ID
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<!-- Provide a quick summary of what the model is/does. -->
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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
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# Fine-Tuning Information
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- **GPU:** RTX4090 (single core / 24564MiB)
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- **model:** meta-llama/Llama-2-13b-hf
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- **dataset:** huangyt/FINETUNE3 (共約3.8w筆訓練集)
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- **peft_type:** LoRA
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- **lora_rank:** 16
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- **lora_target:** q_proj, k_proj, v_proj, o_proj
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- **per_device_train_batch_size:** 8
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- **gradient_accumulation_steps:** 8
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- **learning_rate :** 4e-4
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- **epoch:** 1
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- **precision:** bf16
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- **quantization:** load_in_4bit
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# Fine-Tuning Detail
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- **train_loss:** 0.579
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- **train_runtime:** 4:6:11 (use deepspeed)
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# Evaluation
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- 與Llama-2-13b比較4種Benchmark，包含**ARC**、**HellaSwag**、**MMLU**、**TruthfulQA**
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- 評估結果使用**本地**所測的分數，並使用load_in_8bit
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| Model |Average| ARC |HellaSwag| MMLU | TruthfulQA |
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|-----------------------------------------|-------|-------|---------|-------|------------|
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| FINETUNE4_3.8w-r4-q_k_v_o | 56.67 | 52.13 | 79.38 | 54.54 | 40.64 |
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| FINETUNE4_3.8w-r8-q_k_v_o | 56.84 | 52.30 | 79.58 | 54.50 | 40.98 |
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| FINETUNE4_3.8w-r16-q_k_v_o | 57.28 | 53.92 | 79.92 | 55.61 | 39.65 |
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| FINETUNE4_3.8w-r4-gate_up_down | 55.93 | 51.71 | 79.13 | 53.24 | 39.63 |
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| FINETUNE4_3.8w-r8-gate_up_down | 55.93 | 51.37 | 79.29 | 53.62 | 39.45 |
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| FINETUNE4_3.8w-r16-gate_up_down | 56.35 | 52.56 | 79.28 | 55.27 | 38.31 |
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| FINETUNE4_3.8w-r4-q_k_v_o_gate_up_down | 56.42 | 53.92 | 79.09 | 53.93 | 38.74 |
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| FINETUNE4_3.8w-r8-q_k_v_o_gate_up_down | 56.11 | 51.02 | 79.24 | 53.11 | 41.08 |
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| FINETUNE4_3.8w-r16-q_k_v_o_gate_up_down | 56.83 | 53.67 | 79.49 | 54.79 | 39.36 |
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------------------------------------------------------------------------------------------
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- 評估結果來自**HuggingFaceH4/open_llm_leaderboard**
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| Model |Average| ARC |HellaSwag| MMLU | TruthfulQA |
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|-----------------------------------------|-------|-------|---------|-------|------------|
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| FINETUNE4_3.8w-r4-q_k_v_o | 57.98 | 54.78 | 81.4 | 54.73 | 41.02 |
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| FINETUNE4_3.8w-r8-q_k_v_o | 58.96 | 57.68 | 81.91 | 54.95 | 41.31 |
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| FINETUNE4_3.8w-r16-q_k_v_o | 58.46 | 56.23 | 81.98 | 55.87 | 39.76 |
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| FINETUNE4_3.8w-r4-gate_up_down | 57.94 | 55.8 | 81.74 | 55.09 | 39.12 |
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| FINETUNE4_3.8w-r8-gate_up_down | 57.85 | 54.35 | 82.13 | 55.33 | 39.6 |
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| FINETUNE4_3.8w-r16-gate_up_down | 57.93 | 55.03 | 81.97 | 56.64 | 38.07 |
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| FINETUNE4_3.8w-r4-q_k_v_o_gate_up_down | 58.04 | 56.31 | 81.43 | 55.3 | 39.11 |
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| FINETUNE4_3.8w-r8-q_k_v_o_gate_up_down | 58.16 | 55.97 | 81.53 | 54.42 | 40.72 |
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| FINETUNE4_3.8w-r16-q_k_v_o_gate_up_down | 58.61 | 57.25 | 81.49 | 55.9 | 39.79 |
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# How to convert dataset to json
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- 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
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- 觀察該資料集的欄位名稱，填入**example**欄位中(例如system_prompt、question、response)
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- 最後指定json檔儲存位置 (**json_filename**)
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```py
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import json
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from datasets import load_dataset
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# 讀取數據集，take可以取得該數據集前n筆資料
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dataset = load_dataset("huangyt/FINETUNE4", split="train", streaming=True)
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# 提取所需欄位並建立新的字典列表
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extracted_data = []
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for example in dataset:
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extracted_example = {
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"instruction": example["instruction"],
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"input": example["input"],
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"output": example["output"]
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}
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extracted_data.append(extracted_example)
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# 指定 JSON 文件名稱
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json_filename = "FINETUNE4.json"
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# 寫入 JSON 文件
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with open(json_filename, "w") as json_file:
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json.dump(extracted_data, json_file, indent=4)
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print(f"數據已提取並保存為 {json_filename}")
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```
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