TinyLLaMA v1.1 LoRA fine-tuned for 3-class malicious prompt detection

README.md

@@ -0,0 +1,89 @@
+---
+library_name: peft
+license: apache-2.0
+base_model: TinyLlama/TinyLlama_v1.1
+tags:
+- base_model:adapter:TinyLlama/TinyLlama_v1.1
+- lora
+- transformers
+metrics:
+- accuracy
+model-index:
+- name: tinyllama-lora-malicious-classifier
+  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. -->
+
+# tinyllama-lora-malicious-classifier
+
+This model is a fine-tuned version of [TinyLlama/TinyLlama_v1.1](https://huggingface.co/TinyLlama/TinyLlama_v1.1) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.4833
+- Accuracy: 0.8289
+- Precision Weighted: 0.8220
+- Recall Weighted: 0.8289
+- F1 Weighted: 0.8239
+- Mcc: 0.7203
+- Balanced Accuracy: 0.7724
+- Macro Fnr: 0.2276
+- Macro Fpr: 0.0897
+- Macro Specificity: 0.9103
+- Per Class: {'jailbreaking': {'TP': 228, 'FP': 103, 'FN': 161, 'TN': 1437, 'FNR': 0.4138817480719794, 'FPR': 0.06688311688311688, 'Specificity': 0.9331168831168831}, 'prompt injection': {'TP': 439, 'FP': 112, 'FN': 131, 'TN': 1247, 'FNR': 0.22982456140350876, 'FPR': 0.08241353936718175, 'Specificity': 0.9175864606328182}, 'unharmful': {'TP': 932, 'FP': 115, 'FN': 38, 'TN': 844, 'FNR': 0.03917525773195876, 'FPR': 0.11991657977059438, 'Specificity': 0.8800834202294057}}
+
+## 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: 2e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 15
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | Accuracy | Precision Weighted | Recall Weighted | F1 Weighted | Mcc    | Balanced Accuracy | Macro Fnr | Macro Fpr | Macro Specificity | Per Class                                                                                                                                                                                                                                                                                                                                                                                                                                                                      |
+|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------------------:|:---------------:|:-----------:|:------:|:-----------------:|:---------:|:---------:|:-----------------:|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
+| 1.0323        | 1.0   | 1107  | 1.0327          | 0.5702   | 0.5595             | 0.5702          | 0.5636      | 0.2924 | 0.5112            | 0.4888    | 0.2364    | 0.7636            | {'jailbreaking': {'TP': 113, 'FP': 209, 'FN': 276, 'TN': 1331, 'FNR': 0.7095115681233933, 'FPR': 0.1357142857142857, 'Specificity': 0.8642857142857143}, 'prompt injection': {'TP': 312, 'FP': 238, 'FN': 258, 'TN': 1121, 'FNR': 0.45263157894736844, 'FPR': 0.1751287711552612, 'Specificity': 0.8248712288447387}, 'unharmful': {'TP': 675, 'FP': 382, 'FN': 295, 'TN': 577, 'FNR': 0.30412371134020616, 'FPR': 0.3983315954118874, 'Specificity': 0.6016684045881127}}     |
+| 0.7496        | 2.0   | 2214  | 0.7073          | 0.7252   | 0.7112             | 0.7252          | 0.7144      | 0.5463 | 0.6529            | 0.3471    | 0.1502    | 0.8498            | {'jailbreaking': {'TP': 154, 'FP': 132, 'FN': 235, 'TN': 1408, 'FNR': 0.6041131105398457, 'FPR': 0.08571428571428572, 'Specificity': 0.9142857142857143}, 'prompt injection': {'TP': 386, 'FP': 163, 'FN': 184, 'TN': 1196, 'FNR': 0.32280701754385965, 'FPR': 0.1199411331861663, 'Specificity': 0.8800588668138337}, 'unharmful': {'TP': 859, 'FP': 235, 'FN': 111, 'TN': 724, 'FNR': 0.11443298969072165, 'FPR': 0.24504692387904067, 'Specificity': 0.7549530761209593}}   |
+| 0.5695        | 3.0   | 3321  | 0.6096          | 0.7766   | 0.7636             | 0.7766          | 0.7654      | 0.6323 | 0.7031            | 0.2969    | 0.1215    | 0.8785            | {'jailbreaking': {'TP': 171, 'FP': 106, 'FN': 218, 'TN': 1434, 'FNR': 0.5604113110539846, 'FPR': 0.06883116883116883, 'Specificity': 0.9311688311688312}, 'prompt injection': {'TP': 417, 'FP': 141, 'FN': 153, 'TN': 1218, 'FNR': 0.26842105263157895, 'FPR': 0.10375275938189846, 'Specificity': 0.8962472406181016}, 'unharmful': {'TP': 910, 'FP': 184, 'FN': 60, 'TN': 775, 'FNR': 0.061855670103092786, 'FPR': 0.19186652763295098, 'Specificity': 0.808133472367049}}   |
+| 0.5059        | 4.0   | 4428  | 0.5686          | 0.7926   | 0.7817             | 0.7926          | 0.7843      | 0.6596 | 0.7245            | 0.2755    | 0.1107    | 0.8893            | {'jailbreaking': {'TP': 191, 'FP': 114, 'FN': 198, 'TN': 1426, 'FNR': 0.5089974293059126, 'FPR': 0.07402597402597402, 'Specificity': 0.925974025974026}, 'prompt injection': {'TP': 419, 'FP': 131, 'FN': 151, 'TN': 1228, 'FNR': 0.2649122807017544, 'FPR': 0.0963944076526858, 'Specificity': 0.9036055923473142}, 'unharmful': {'TP': 919, 'FP': 155, 'FN': 51, 'TN': 804, 'FNR': 0.05257731958762887, 'FPR': 0.1616266944734098, 'Specificity': 0.8383733055265902}}       |
+| 0.4583        | 5.0   | 5535  | 0.5413          | 0.8056   | 0.7953             | 0.8056          | 0.7977      | 0.6812 | 0.7389            | 0.2611    | 0.1032    | 0.8968            | {'jailbreaking': {'TP': 199, 'FP': 109, 'FN': 190, 'TN': 1431, 'FNR': 0.4884318766066838, 'FPR': 0.07077922077922078, 'Specificity': 0.9292207792207792}, 'prompt injection': {'TP': 426, 'FP': 126, 'FN': 144, 'TN': 1233, 'FNR': 0.25263157894736843, 'FPR': 0.09271523178807947, 'Specificity': 0.9072847682119205}, 'unharmful': {'TP': 929, 'FP': 140, 'FN': 41, 'TN': 819, 'FNR': 0.042268041237113405, 'FPR': 0.145985401459854, 'Specificity': 0.8540145985401459}}    |
+| 0.4763        | 6.0   | 6642  | 0.5243          | 0.8113   | 0.8028             | 0.8113          | 0.8052      | 0.6910 | 0.7498            | 0.2502    | 0.0994    | 0.9006            | {'jailbreaking': {'TP': 212, 'FP': 113, 'FN': 177, 'TN': 1427, 'FNR': 0.455012853470437, 'FPR': 0.07337662337662337, 'Specificity': 0.9266233766233766}, 'prompt injection': {'TP': 428, 'FP': 120, 'FN': 142, 'TN': 1239, 'FNR': 0.24912280701754386, 'FPR': 0.08830022075055188, 'Specificity': 0.9116997792494481}, 'unharmful': {'TP': 925, 'FP': 131, 'FN': 45, 'TN': 828, 'FNR': 0.04639175257731959, 'FPR': 0.13660062565172054, 'Specificity': 0.8633993743482794}}    |
+| 0.4283        | 7.0   | 7749  | 0.5095          | 0.8170   | 0.8083             | 0.8170          | 0.8104      | 0.7003 | 0.7546            | 0.2454    | 0.0969    | 0.9031            | {'jailbreaking': {'TP': 213, 'FP': 105, 'FN': 176, 'TN': 1435, 'FNR': 0.4524421593830334, 'FPR': 0.06818181818181818, 'Specificity': 0.9318181818181818}, 'prompt injection': {'TP': 430, 'FP': 118, 'FN': 140, 'TN': 1241, 'FNR': 0.24561403508771928, 'FPR': 0.08682855040470934, 'Specificity': 0.9131714495952906}, 'unharmful': {'TP': 933, 'FP': 130, 'FN': 37, 'TN': 829, 'FNR': 0.03814432989690722, 'FPR': 0.13555787278415016, 'Specificity': 0.8644421272158499}}   |
+| 0.4119        | 8.0   | 8856  | 0.5033          | 0.8191   | 0.8116             | 0.8191          | 0.8135      | 0.7041 | 0.7592            | 0.2408    | 0.0951    | 0.9049            | {'jailbreaking': {'TP': 223, 'FP': 118, 'FN': 166, 'TN': 1422, 'FNR': 0.4267352185089974, 'FPR': 0.07662337662337662, 'Specificity': 0.9233766233766234}, 'prompt injection': {'TP': 422, 'FP': 105, 'FN': 148, 'TN': 1254, 'FNR': 0.2596491228070175, 'FPR': 0.0772626931567329, 'Specificity': 0.9227373068432672}, 'unharmful': {'TP': 935, 'FP': 126, 'FN': 35, 'TN': 833, 'FNR': 0.03608247422680412, 'FPR': 0.13138686131386862, 'Specificity': 0.8686131386861314}}     |
+| 0.412         | 9.0   | 9963  | 0.4955          | 0.8253   | 0.8178             | 0.8253          | 0.8199      | 0.7143 | 0.7671            | 0.2329    | 0.0916    | 0.9084            | {'jailbreaking': {'TP': 222, 'FP': 103, 'FN': 167, 'TN': 1437, 'FNR': 0.42930591259640105, 'FPR': 0.06688311688311688, 'Specificity': 0.9331168831168831}, 'prompt injection': {'TP': 440, 'FP': 118, 'FN': 130, 'TN': 1241, 'FNR': 0.22807017543859648, 'FPR': 0.08682855040470934, 'Specificity': 0.9131714495952906}, 'unharmful': {'TP': 930, 'FP': 116, 'FN': 40, 'TN': 843, 'FNR': 0.041237113402061855, 'FPR': 0.12095933263816476, 'Specificity': 0.8790406673618353}} |
+| 0.496         | 10.0  | 11070 | 0.4926          | 0.8289   | 0.8214             | 0.8289          | 0.8232      | 0.7202 | 0.7703            | 0.2297    | 0.0903    | 0.9097            | {'jailbreaking': {'TP': 224, 'FP': 98, 'FN': 165, 'TN': 1442, 'FNR': 0.4241645244215938, 'FPR': 0.06363636363636363, 'Specificity': 0.9363636363636364}, 'prompt injection': {'TP': 439, 'FP': 113, 'FN': 131, 'TN': 1246, 'FNR': 0.22982456140350876, 'FPR': 0.08314937454010302, 'Specificity': 0.9168506254598969}, 'unharmful': {'TP': 936, 'FP': 119, 'FN': 34, 'TN': 840, 'FNR': 0.03505154639175258, 'FPR': 0.12408759124087591, 'Specificity': 0.8759124087591241}}    |
+| 0.428         | 11.0  | 12177 | 0.4890          | 0.8258   | 0.8191             | 0.8258          | 0.8207      | 0.7154 | 0.7677            | 0.2323    | 0.0913    | 0.9087            | {'jailbreaking': {'TP': 230, 'FP': 117, 'FN': 159, 'TN': 1423, 'FNR': 0.4087403598971722, 'FPR': 0.07597402597402597, 'Specificity': 0.924025974025974}, 'prompt injection': {'TP': 424, 'FP': 99, 'FN': 146, 'TN': 1260, 'FNR': 0.256140350877193, 'FPR': 0.0728476821192053, 'Specificity': 0.9271523178807947}, 'unharmful': {'TP': 939, 'FP': 120, 'FN': 31, 'TN': 839, 'FNR': 0.031958762886597936, 'FPR': 0.1251303441084463, 'Specificity': 0.8748696558915537}}        |
+| 0.4103        | 12.0  | 13284 | 0.4866          | 0.8269   | 0.8191             | 0.8269          | 0.8206      | 0.7166 | 0.7669            | 0.2331    | 0.0922    | 0.9078            | {'jailbreaking': {'TP': 221, 'FP': 99, 'FN': 168, 'TN': 1441, 'FNR': 0.4318766066838046, 'FPR': 0.06428571428571428, 'Specificity': 0.9357142857142857}, 'prompt injection': {'TP': 437, 'FP': 107, 'FN': 133, 'TN': 1252, 'FNR': 0.23333333333333334, 'FPR': 0.07873436350257543, 'Specificity': 0.9212656364974245}, 'unharmful': {'TP': 937, 'FP': 128, 'FN': 33, 'TN': 831, 'FNR': 0.03402061855670103, 'FPR': 0.1334723670490094, 'Specificity': 0.8665276329509907}}     |
+| 0.4009        | 13.0  | 14391 | 0.4833          | 0.8289   | 0.8220             | 0.8289          | 0.8239      | 0.7203 | 0.7724            | 0.2276    | 0.0897    | 0.9103            | {'jailbreaking': {'TP': 228, 'FP': 103, 'FN': 161, 'TN': 1437, 'FNR': 0.4138817480719794, 'FPR': 0.06688311688311688, 'Specificity': 0.9331168831168831}, 'prompt injection': {'TP': 439, 'FP': 112, 'FN': 131, 'TN': 1247, 'FNR': 0.22982456140350876, 'FPR': 0.08241353936718175, 'Specificity': 0.9175864606328182}, 'unharmful': {'TP': 932, 'FP': 115, 'FN': 38, 'TN': 844, 'FNR': 0.03917525773195876, 'FPR': 0.11991657977059438, 'Specificity': 0.8800834202294057}}   |
+| 0.4242        | 14.0  | 15498 | 0.4834          | 0.8284   | 0.8211             | 0.8284          | 0.8228      | 0.7193 | 0.7700            | 0.2300    | 0.0906    | 0.9094            | {'jailbreaking': {'TP': 226, 'FP': 105, 'FN': 163, 'TN': 1435, 'FNR': 0.4190231362467866, 'FPR': 0.06818181818181818, 'Specificity': 0.9318181818181818}, 'prompt injection': {'TP': 435, 'FP': 104, 'FN': 135, 'TN': 1255, 'FNR': 0.23684210526315788, 'FPR': 0.07652685798381163, 'Specificity': 0.9234731420161884}, 'unharmful': {'TP': 937, 'FP': 122, 'FN': 33, 'TN': 837, 'FNR': 0.03402061855670103, 'FPR': 0.12721584984358708, 'Specificity': 0.872784150156413}}    |
+| 0.3859        | 15.0  | 16605 | 0.4829          | 0.8269   | 0.8197             | 0.8269          | 0.8215      | 0.7168 | 0.7692            | 0.2308    | 0.0912    | 0.9088            | {'jailbreaking': {'TP': 226, 'FP': 106, 'FN': 163, 'TN': 1434, 'FNR': 0.4190231362467866, 'FPR': 0.06883116883116883, 'Specificity': 0.9311688311688312}, 'prompt injection': {'TP': 436, 'FP': 107, 'FN': 134, 'TN': 1252, 'FNR': 0.23508771929824562, 'FPR': 0.07873436350257543, 'Specificity': 0.9212656364974245}, 'unharmful': {'TP': 933, 'FP': 121, 'FN': 37, 'TN': 838, 'FNR': 0.03814432989690722, 'FPR': 0.1261730969760167, 'Specificity': 0.8738269030239834}}    |
+
+
+### Framework versions
+
+- PEFT 0.17.1
+- Transformers 4.53.3
+- Pytorch 2.6.0+cu124
+- Datasets 4.3.0
+- Tokenizers 0.21.4

adapter_model.safetensors

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eval_metrics.json

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+{
+  "eval_loss": 0.48330193758010864,
+  "eval_accuracy": 0.8289269051321928,
+  "eval_precision_weighted": 0.821952872098563,
+  "eval_recall_weighted": 0.8289269051321928,
+  "eval_f1_weighted": 0.8238609492009197,
+  "eval_MCC": 0.7203450380787764,
+  "eval_balanced_accuracy": 0.7723728109308511,
+  "eval_macro_FNR": 0.227627189069149,
+  "eval_macro_FPR": 0.08973774534029767,
+  "eval_macro_Specificity": 0.9102622546597022,
+  "eval_per_class": {
+    "jailbreaking": {
+      "TP": 228,
+      "FP": 103,
+      "FN": 161,
+      "TN": 1437,
+      "FNR": 0.4138817480719794,
+      "FPR": 0.06688311688311688,
+      "Specificity": 0.9331168831168831
+    },
+    "prompt injection": {
+      "TP": 439,
+      "FP": 112,
+      "FN": 131,
+      "TN": 1247,
+      "FNR": 0.22982456140350876,
+      "FPR": 0.08241353936718175,
+      "Specificity": 0.9175864606328182
+    },
+    "unharmful": {
+      "TP": 932,
+      "FP": 115,
+      "FN": 38,
+      "TN": 844,
+      "FNR": 0.03917525773195876,
+      "FPR": 0.11991657977059438,
+      "Specificity": 0.8800834202294057
+    }
+  },
+  "eval_runtime": 44.2738,
+  "eval_samples_per_second": 43.57,
+  "eval_steps_per_second": 5.466,
+  "epoch": 15.0
+}

final_model/README.md

@@ -0,0 +1,206 @@
+---
+base_model: TinyLlama/TinyLlama_v1.1
+library_name: peft
+tags:
+- base_model:adapter:TinyLlama/TinyLlama_v1.1
+- lora
+- transformers
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.17.1

final_model/adapter_config.json

@@ -0,0 +1,42 @@
+{
+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "TinyLlama/TinyLlama_v1.1",
+  "bias": "none",
+  "corda_config": null,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 16,
+  "lora_bias": false,
+  "lora_dropout": 0.05,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": [
+    "classifier",
+    "score"
+  ],
+  "peft_type": "LORA",
+  "qalora_group_size": 16,
+  "r": 8,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
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+  "task_type": "SEQ_CLS",
+  "trainable_token_indices": null,
+  "use_dora": false,
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+}

final_model/adapter_model.safetensors

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+oid sha256:16120d428e11b203725121c0717568f3084db04cdd8305aaab1d1f4bdcd2186b
+size 9058976

final_model/special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
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+  "eos_token": {
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+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
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+  "unk_token": {
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

final_model/tokenizer_config.json

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+{
+  "add_bos_token": true,
+  "add_eos_token": false,
+  "add_prefix_space": null,
+  "added_tokens_decoder": {
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+      "single_word": false,
+      "special": true
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+  "clean_up_tokenization_spaces": false,
+  "eos_token": "</s>",
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+  "pad_token": "</s>",
+  "padding_side": "right",
+  "sp_model_kwargs": {},
+  "tokenizer_class": "LlamaTokenizer",
+  "unk_token": "<unk>",
+  "use_default_system_prompt": false
+}

overall_metrics.csv

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+eval_loss,eval_accuracy,eval_precision_weighted,eval_recall_weighted,eval_f1_weighted,eval_MCC,eval_balanced_accuracy,eval_macro_FNR,eval_macro_FPR,eval_macro_Specificity,eval_runtime,eval_samples_per_second,eval_steps_per_second,epoch
+0.48330193758010864,0.8289269051321928,0.821952872098563,0.8289269051321928,0.8238609492009197,0.7203450380787764,0.7723728109308511,0.227627189069149,0.08973774534029767,0.9102622546597022,44.2738,43.57,5.466,15.0