End of training

README.md

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+---
+license: mit
+library_name: peft
+tags:
+- generated_from_trainer
+base_model: microsoft/Phi-3-mini-128k-instruct
+model-index:
+- name: results
+  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. -->
+
+# results
+
+This model is a fine-tuned version of [microsoft/Phi-3-mini-128k-instruct](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1119
+
+## 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: 4
+- eval_batch_size: 4
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 3
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss |
+|:-------------:|:-----:|:----:|:---------------:|
+| 0.1029        | 0.5   | 100  | 0.1042          |
+| 0.0507        | 1.0   | 200  | 0.1324          |
+| 0.0123        | 1.5   | 300  | 0.1853          |
+| 0.0648        | 2.0   | 400  | 0.1053          |
+| 0.0032        | 2.5   | 500  | 0.1233          |
+| 0.0639        | 3.0   | 600  | 0.1119          |
+
+
+### Framework versions
+
+- PEFT 0.10.0
+- Transformers 4.40.0
+- Pytorch 2.2.1+cu121
+- Datasets 2.19.0
+- Tokenizers 0.19.1

adapter_config.json

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+{
+  "auto_mapping": null,
+  "base_model_name_or_path": "microsoft/Phi-3-mini-128k-instruct",
+  "inference_mode": true,
+  "num_attention_heads": 32,
+  "num_layers": 32,
+  "num_transformer_submodules": 1,
+  "num_virtual_tokens": 8,
+  "peft_type": "PROMPT_TUNING",
+  "prompt_tuning_init": "TEXT",
+  "prompt_tuning_init_text": "Classify if the text contains hateful speech or not: ",
+  "revision": null,
+  "task_type": "CAUSAL_LM",
+  "token_dim": 3072,
+  "tokenizer_kwargs": null,
+  "tokenizer_name_or_path": "microsoft/Phi-3-mini-128k-instruct"
+}

adapter_model.safetensors

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+size 98424

training_args.bin

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