End of training

README.md

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+---
+license: llama2
+base_model: lmsys/vicuna-7b-v1.5
+tags:
+- generated_from_trainer
+datasets:
+- truthful_qa
+metrics:
+- accuracy
+model-index:
+- name: vicuna_mc_finetune
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# vicuna_mc_finetune
+
+This model is a fine-tuned version of [lmsys/vicuna-7b-v1.5](https://huggingface.co/lmsys/vicuna-7b-v1.5) on the truthful_qa dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.8867
+- Accuracy: 0.2378
+
+## 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: 0.0001
+- train_batch_size: 6
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 5
+- num_epochs: 20
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| 1.7307        | 1.0   | 109  | 1.5327          | 0.4024   |
+| 1.4519        | 2.0   | 218  | 1.2341          | 0.7073   |
+| 0.0207        | 3.0   | 327  | 2.2924          | 0.6280   |
+| 1.5647        | 4.0   | 436  | 1.9344          | 0.2256   |
+| 2.2047        | 5.0   | 545  | 1.9401          | 0.2256   |
+| 2.016         | 6.0   | 654  | 1.8888          | 0.2256   |
+| 1.625         | 7.0   | 763  | 1.9068          | 0.1768   |
+| 2.0002        | 8.0   | 872  | 1.8909          | 0.1951   |
+| 1.7906        | 9.0   | 981  | 1.8828          | 0.2195   |
+| 1.5295        | 10.0  | 1090 | 1.8967          | 0.2195   |
+| 1.7018        | 11.0  | 1199 | 1.8845          | 0.2378   |
+| 1.8412        | 12.0  | 1308 | 1.8808          | 0.2073   |
+| 2.4396        | 13.0  | 1417 | 1.8816          | 0.2012   |
+| 1.8643        | 14.0  | 1526 | 1.8827          | 0.2012   |
+| 1.7271        | 15.0  | 1635 | 1.8844          | 0.2256   |
+| 1.851         | 16.0  | 1744 | 1.8720          | 0.2134   |
+| 1.7633        | 17.0  | 1853 | 1.8786          | 0.2134   |
+| 2.6586        | 18.0  | 1962 | 1.8723          | 0.25     |
+| 2.0078        | 19.0  | 2071 | 1.8770          | 0.2439   |
+| 1.4072        | 20.0  | 2180 | 1.8867          | 0.2378   |
+
+
+### Framework versions
+
+- Transformers 4.36.0.dev0
+- Pytorch 2.1.0+cu121
+- Datasets 2.13.1
+- Tokenizers 0.14.1

adapter_model.bin

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adapter_model.safetensors

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all_results.json

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+{
+    "epoch": 20.0,
+    "total_flos": 1.2663415269359616e+17,
+    "train_loss": 1.7466916341539105,
+    "train_runtime": 4318.1089,
+    "train_samples_per_second": 3.024,
+    "train_steps_per_second": 0.505
+}

train_results.json

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+{
+    "epoch": 20.0,
+    "total_flos": 1.2663415269359616e+17,
+    "train_loss": 1.7466916341539105,
+    "train_runtime": 4318.1089,
+    "train_samples_per_second": 3.024,
+    "train_steps_per_second": 0.505
+}