README.md

metadata

license: apache-2.0
tags:
  - generated_from_trainer
base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
metrics:
  - accuracy
  - precision
  - recall
model-index:
  - name: tiny-llama-lora-new
    results: []

tiny-llama-lora-new

This model is a fine-tuned version of TinyLlama/TinyLlama-1.1B-Chat-v1.0 on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 1.2252
• Accuracy: 0.8203
• Precision: 0.8184
• Recall: 0.8203
• Precision Macro: 0.7732
• Recall Macro: 0.7380
• Macro Fpr: 0.0162
• Weighted Fpr: 0.0154
• Weighted Specificity: 0.9743
• Macro Specificity: 0.9863
• Weighted Sensitivity: 0.8203
• Macro Sensitivity: 0.7380
• F1 Micro: 0.8203
• F1 Macro: 0.7435
• F1 Weighted: 0.8173

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: 8
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 32
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	Precision Macro	Recall Macro	Macro Fpr	Weighted Fpr	Weighted Specificity	Macro Specificity	Weighted Sensitivity	Macro Sensitivity	F1 Micro	F1 Macro	F1 Weighted
No log	1.0	160	0.6615	0.8002	0.8040	0.8002	0.7266	0.6678	0.0182	0.0175	0.9726	0.9848	0.8002	0.6678	0.8002	0.6790	0.7959
No log	2.0	321	0.6996	0.8064	0.8110	0.8064	0.7448	0.7207	0.0177	0.0169	0.9737	0.9853	0.8064	0.7207	0.8064	0.7235	0.8039
No log	3.0	482	0.8202	0.8125	0.8119	0.8125	0.7577	0.7080	0.0171	0.0162	0.9711	0.9856	0.8125	0.7080	0.8125	0.7180	0.8085
0.2932	4.0	643	0.9493	0.8141	0.8204	0.8141	0.7593	0.7327	0.0166	0.0160	0.9744	0.9859	0.8141	0.7327	0.8141	0.7415	0.8154
0.2932	5.0	803	1.0610	0.8110	0.8110	0.8110	0.7596	0.7427	0.0172	0.0164	0.9738	0.9857	0.8110	0.7427	0.8110	0.7413	0.8087
0.2932	6.0	964	1.1362	0.8149	0.8160	0.8149	0.7731	0.7380	0.0167	0.0160	0.9741	0.9859	0.8149	0.7380	0.8149	0.7408	0.8128
0.0107	7.0	1125	1.1713	0.8102	0.8123	0.8102	0.7734	0.7310	0.0171	0.0165	0.9736	0.9856	0.8102	0.7310	0.8102	0.7343	0.8085
0.0107	8.0	1286	1.1786	0.8156	0.8141	0.8156	0.7656	0.7349	0.0166	0.0159	0.9740	0.9860	0.8156	0.7349	0.8156	0.7374	0.8128
0.0107	9.0	1446	1.1960	0.8187	0.8170	0.8187	0.7693	0.7368	0.0163	0.0156	0.9743	0.9862	0.8187	0.7368	0.8187	0.7400	0.8157
0.0016	10.0	1607	1.2049	0.8156	0.8150	0.8156	0.7659	0.7353	0.0166	0.0159	0.9741	0.9860	0.8156	0.7353	0.8156	0.7376	0.8131
0.0016	11.0	1768	1.2137	0.8156	0.8147	0.8156	0.7661	0.7353	0.0166	0.0159	0.9741	0.9860	0.8156	0.7353	0.8156	0.7377	0.8130
0.0016	12.0	1929	1.2158	0.8156	0.8145	0.8156	0.7664	0.7353	0.0166	0.0159	0.9739	0.9860	0.8156	0.7353	0.8156	0.7379	0.8129
0.0011	13.0	2089	1.2202	0.8187	0.8169	0.8187	0.7720	0.7372	0.0163	0.0156	0.9741	0.9862	0.8187	0.7372	0.8187	0.7425	0.8158
0.0011	14.0	2250	1.2229	0.8187	0.8169	0.8187	0.7720	0.7372	0.0163	0.0156	0.9741	0.9862	0.8187	0.7372	0.8187	0.7425	0.8158
0.0011	14.93	2400	1.2252	0.8203	0.8184	0.8203	0.7732	0.7380	0.0162	0.0154	0.9743	0.9863	0.8203	0.7380	0.8203	0.7435	0.8173

Framework versions

• Transformers 4.35.2
• Pytorch 2.1.0+cu121
• Datasets 2.19.0
• Tokenizers 0.15.1