--- license: other library_name: peft tags: - llama-factory - lora - generated_from_trainer base_model: bigcode/starcoder2-7b model-index: - name: sft results: [] --- # sft This model is a fine-tuned version of [bigcode/starcoder2-7b](https://huggingface.co/bigcode/starcoder2-7b) on the starcoder_jetpack dataset. It achieves the following results on the evaluation set: - Loss: 0.6761 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.2469 | 0.02 | 100 | 0.8479 | | 1.3584 | 0.03 | 200 | 0.8368 | | 1.0651 | 0.05 | 300 | 0.8281 | | 0.9209 | 0.06 | 400 | 0.8185 | | 0.8306 | 0.08 | 500 | 0.8132 | | 0.9175 | 0.1 | 600 | 0.8064 | | 0.8157 | 0.11 | 700 | 0.8023 | | 0.9469 | 0.13 | 800 | 0.7996 | | 0.8872 | 0.14 | 900 | 0.7955 | | 0.8842 | 0.16 | 1000 | 0.7913 | | 0.7909 | 0.18 | 1100 | 0.7863 | | 0.8196 | 0.19 | 1200 | 0.7844 | | 0.9341 | 0.21 | 1300 | 0.7825 | | 0.8801 | 0.22 | 1400 | 0.7787 | | 0.9109 | 0.24 | 1500 | 0.7777 | | 0.8964 | 0.26 | 1600 | 0.7759 | | 0.9265 | 0.27 | 1700 | 0.7742 | | 0.8632 | 0.29 | 1800 | 0.7699 | | 1.0081 | 0.3 | 1900 | 0.7693 | | 0.7651 | 0.32 | 2000 | 0.7664 | | 1.0037 | 0.34 | 2100 | 0.7654 | | 0.8768 | 0.35 | 2200 | 0.7642 | | 0.8052 | 0.37 | 2300 | 0.7618 | | 0.7271 | 0.38 | 2400 | 0.7595 | | 0.9615 | 0.4 | 2500 | 0.7582 | | 0.8284 | 0.42 | 2600 | 0.7555 | | 0.8631 | 0.43 | 2700 | 0.7540 | | 1.0507 | 0.45 | 2800 | 0.7518 | | 0.8247 | 0.46 | 2900 | 0.7512 | | 0.9835 | 0.48 | 3000 | 0.7496 | | 0.8407 | 0.49 | 3100 | 0.7496 | | 0.7417 | 0.51 | 3200 | 0.7467 | | 0.7449 | 0.53 | 3300 | 0.7473 | | 0.8562 | 0.54 | 3400 | 0.7437 | | 0.9222 | 0.56 | 3500 | 0.7429 | | 0.9242 | 0.57 | 3600 | 0.7413 | | 0.8092 | 0.59 | 3700 | 0.7403 | | 0.7279 | 0.61 | 3800 | 0.7394 | | 0.7774 | 0.62 | 3900 | 0.7385 | | 0.8942 | 0.64 | 4000 | 0.7364 | | 0.9286 | 0.65 | 4100 | 0.7348 | | 0.7703 | 0.67 | 4200 | 0.7354 | | 0.8322 | 0.69 | 4300 | 0.7330 | | 0.9851 | 0.7 | 4400 | 0.7324 | | 0.8712 | 0.72 | 4500 | 0.7317 | | 0.7871 | 0.73 | 4600 | 0.7310 | | 0.7156 | 0.75 | 4700 | 0.7284 | | 0.7856 | 0.77 | 4800 | 0.7277 | | 0.7906 | 0.78 | 4900 | 0.7255 | | 0.7917 | 0.8 | 5000 | 0.7250 | | 0.6395 | 0.81 | 5100 | 0.7237 | | 0.7567 | 0.83 | 5200 | 0.7232 | | 0.8551 | 0.85 | 5300 | 0.7220 | | 0.7392 | 0.86 | 5400 | 0.7226 | | 0.9312 | 0.88 | 5500 | 0.7205 | | 0.8323 | 0.89 | 5600 | 0.7196 | | 0.7312 | 0.91 | 5700 | 0.7197 | | 1.0 | 0.93 | 5800 | 0.7182 | | 0.6164 | 0.94 | 5900 | 0.7177 | | 0.7484 | 0.96 | 6000 | 0.7147 | | 0.7924 | 0.97 | 6100 | 0.7144 | | 0.9389 | 0.99 | 6200 | 0.7145 | | 0.7108 | 1.01 | 6300 | 0.7136 | | 0.8076 | 1.02 | 6400 | 0.7154 | | 0.7232 | 1.04 | 6500 | 0.7147 | | 0.6456 | 1.05 | 6600 | 0.7122 | | 0.5862 | 1.07 | 6700 | 0.7133 | | 0.6935 | 1.09 | 6800 | 0.7112 | | 0.7522 | 1.1 | 6900 | 0.7103 | | 1.0525 | 1.12 | 7000 | 0.7106 | | 0.8285 | 1.13 | 7100 | 0.7099 | | 0.6116 | 1.15 | 7200 | 0.7079 | | 0.5617 | 1.17 | 7300 | 0.7087 | | 0.6514 | 1.18 | 7400 | 0.7072 | | 0.6729 | 1.2 | 7500 | 0.7052 | | 0.6401 | 1.21 | 7600 | 0.7055 | | 0.8089 | 1.23 | 7700 | 0.7052 | | 0.8166 | 1.25 | 7800 | 0.7041 | | 0.8685 | 1.26 | 7900 | 0.7026 | | 0.6945 | 1.28 | 8000 | 0.7043 | | 0.6955 | 1.29 | 8100 | 0.7010 | | 0.734 | 1.31 | 8200 | 0.7022 | | 0.5586 | 1.32 | 8300 | 0.7017 | | 0.7299 | 1.34 | 8400 | 0.6999 | | 1.089 | 1.36 | 8500 | 0.6994 | | 0.5733 | 1.37 | 8600 | 0.6994 | | 0.5409 | 1.39 | 8700 | 0.6987 | | 0.8848 | 1.4 | 8800 | 0.6976 | | 0.5739 | 1.42 | 8900 | 0.6971 | | 0.728 | 1.44 | 9000 | 0.6963 | | 0.7503 | 1.45 | 9100 | 0.6953 | | 0.706 | 1.47 | 9200 | 0.6951 | | 0.7926 | 1.48 | 9300 | 0.6945 | | 0.6019 | 1.5 | 9400 | 0.6945 | | 0.6707 | 1.52 | 9500 | 0.6943 | | 0.7269 | 1.53 | 9600 | 0.6940 | | 0.7216 | 1.55 | 9700 | 0.6923 | | 0.6394 | 1.56 | 9800 | 0.6920 | | 0.7608 | 1.58 | 9900 | 0.6909 | | 1.034 | 1.6 | 10000 | 0.6908 | | 0.7934 | 1.61 | 10100 | 0.6892 | | 0.627 | 1.63 | 10200 | 0.6902 | | 0.5849 | 1.64 | 10300 | 0.6897 | | 0.7257 | 1.66 | 10400 | 0.6889 | | 0.8931 | 1.68 | 10500 | 0.6890 | | 0.6831 | 1.69 | 10600 | 0.6875 | | 0.4995 | 1.71 | 10700 | 0.6879 | | 0.757 | 1.72 | 10800 | 0.6873 | | 0.4664 | 1.74 | 10900 | 0.6876 | | 0.78 | 1.76 | 11000 | 0.6865 | | 0.5564 | 1.77 | 11100 | 0.6865 | | 0.7858 | 1.79 | 11200 | 0.6858 | | 0.6989 | 1.8 | 11300 | 0.6851 | | 0.705 | 1.82 | 11400 | 0.6841 | | 0.5795 | 1.84 | 11500 | 0.6842 | | 0.6989 | 1.85 | 11600 | 0.6837 | | 0.6877 | 1.87 | 11700 | 0.6838 | | 0.6484 | 1.88 | 11800 | 0.6835 | | 0.8525 | 1.9 | 11900 | 0.6832 | | 0.7547 | 1.92 | 12000 | 0.6823 | | 0.8118 | 1.93 | 12100 | 0.6819 | | 0.8859 | 1.95 | 12200 | 0.6823 | | 0.738 | 1.96 | 12300 | 0.6811 | | 0.7051 | 1.98 | 12400 | 0.6816 | | 0.5598 | 2.0 | 12500 | 0.6802 | | 0.6194 | 2.01 | 12600 | 0.6812 | | 0.7101 | 2.03 | 12700 | 0.6817 | | 0.7027 | 2.04 | 12800 | 0.6815 | | 0.9432 | 2.06 | 12900 | 0.6810 | | 0.5931 | 2.08 | 13000 | 0.6817 | | 0.5412 | 2.09 | 13100 | 0.6810 | | 0.6237 | 2.11 | 13200 | 0.6815 | | 0.5871 | 2.12 | 13300 | 0.6812 | | 0.8331 | 2.14 | 13400 | 0.6817 | | 0.4528 | 2.15 | 13500 | 0.6812 | | 0.6292 | 2.17 | 13600 | 0.6814 | | 0.6219 | 2.19 | 13700 | 0.6800 | | 0.6281 | 2.2 | 13800 | 0.6798 | | 0.6949 | 2.22 | 13900 | 0.6803 | | 0.6701 | 2.23 | 14000 | 0.6791 | | 0.6467 | 2.25 | 14100 | 0.6795 | | 0.6579 | 2.27 | 14200 | 0.6800 | | 0.5978 | 2.28 | 14300 | 0.6802 | | 0.7032 | 2.3 | 14400 | 0.6793 | | 0.6347 | 2.31 | 14500 | 0.6787 | | 0.9034 | 2.33 | 14600 | 0.6788 | | 0.6166 | 2.35 | 14700 | 0.6781 | | 0.7327 | 2.36 | 14800 | 0.6786 | | 0.7159 | 2.38 | 14900 | 0.6777 | | 0.6283 | 2.39 | 15000 | 0.6779 | | 0.6113 | 2.41 | 15100 | 0.6776 | | 0.5951 | 2.43 | 15200 | 0.6781 | | 0.6747 | 2.44 | 15300 | 0.6777 | | 0.5935 | 2.46 | 15400 | 0.6779 | | 0.6435 | 2.47 | 15500 | 0.6776 | | 0.637 | 2.49 | 15600 | 0.6772 | | 0.4617 | 2.51 | 15700 | 0.6774 | | 0.7937 | 2.52 | 15800 | 0.6771 | | 0.7187 | 2.54 | 15900 | 0.6768 | | 0.657 | 2.55 | 16000 | 0.6767 | | 0.8606 | 2.57 | 16100 | 0.6767 | | 0.4392 | 2.59 | 16200 | 0.6768 | | 0.5675 | 2.6 | 16300 | 0.6769 | | 0.6454 | 2.62 | 16400 | 0.6768 | | 0.5787 | 2.63 | 16500 | 0.6767 | | 0.6111 | 2.65 | 16600 | 0.6766 | | 0.6106 | 2.67 | 16700 | 0.6767 | | 0.5947 | 2.68 | 16800 | 0.6763 | | 0.5576 | 2.7 | 16900 | 0.6763 | | 0.659 | 2.71 | 17000 | 0.6762 | | 0.787 | 2.73 | 17100 | 0.6761 | | 0.5503 | 2.75 | 17200 | 0.6760 | | 0.5558 | 2.76 | 17300 | 0.6760 | | 0.6324 | 2.78 | 17400 | 0.6761 | | 0.5846 | 2.79 | 17500 | 0.6761 | | 0.9542 | 2.81 | 17600 | 0.6760 | | 0.5755 | 2.83 | 17700 | 0.6761 | | 0.7841 | 2.84 | 17800 | 0.6761 | | 0.5662 | 2.86 | 17900 | 0.6761 | | 0.8085 | 2.87 | 18000 | 0.6761 | | 0.7389 | 2.89 | 18100 | 0.6761 | | 0.736 | 2.91 | 18200 | 0.6761 | | 0.5604 | 2.92 | 18300 | 0.6761 | | 0.6156 | 2.94 | 18400 | 0.6761 | | 0.5473 | 2.95 | 18500 | 0.6761 | | 0.7286 | 2.97 | 18600 | 0.6761 | | 0.5932 | 2.98 | 18700 | 0.6761 | ### Framework versions - PEFT 0.9.0 - Transformers 4.39.0.dev0 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2