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在llama-2-13b上使用huangyt/FINETUNE2資料集進行訓練，採用與platypus相近參數
Fine-Tuning Information
- GPU: RTX4090 (single core / 24564MiB)
- base model: meta-llama/Llama-2-13b-hf
- dataset: huangyt/FINETUNE2 (共約3w筆訓練集)
- peft_type: LoRA
- lora_rank: 16
- lora alpha: 8
- lora dropout: 0.05
- lora_target: gate_proj, up_proj, down_proj
- per_device_train_batch_size: 8
- gradient_accumulation_steps: 8
- learning_rate : 4e-4
- epoch: 1
- batch size: 8
- microbatch size: 4
- warmup steps: 100
- weight decay: cosine
- cutoff length: 2048
- precision: bf16
- group_by_length: True
- load_in_8bit: True
Fine-Tuning Detail
- train_loss: 0.0823
- train_runtime: 02:40:01
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 | 58.24 | 58.62 | 82.32 | 54.25 | 38.17 |
| CHIH-HUNG/llama-2-13b-huangyt_Fintune_1_17w-q_k_v_o_proj | 58.49 | 59.73 | 81.06 | 54.53 | 38.64 |
| 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-gate_up_down_proj | 58.81 | 57.42 | 82.42 | 55.57 | 39.19 |
| wei123602/llama2-13b-fintune2 | wait | wait | wait | wait | wait |
