judicial-summarization-llama-3-finetuned_mildsum_FL

README.md

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+---
+base_model: unsloth/llama-3-8b-bnb-4bit
+library_name: peft
+license: llama3
+tags:
+- trl
+- sft
+- unsloth
+- generated_from_trainer
+model-index:
+- name: judicial-summarization-llama-3-finetuned_mildsum_FL
+  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. -->
+
+# judicial-summarization-llama-3-finetuned_mildsum_FL
+
+This model is a fine-tuned version of [unsloth/llama-3-8b-bnb-4bit](https://huggingface.co/unsloth/llama-3-8b-bnb-4bit) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.7972
+
+## 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: 0.0002
+- train_batch_size: 2
+- eval_batch_size: 8
+- seed: 3407
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 8
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 5
+- num_epochs: 6
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss |
+|:-------------:|:------:|:----:|:---------------:|
+| 1.3073        | 0.9991 | 273  | 1.4746          |
+| 1.3533        | 1.9982 | 546  | 1.4690          |
+| 1.1871        | 2.9973 | 819  | 1.5012          |
+| 1.008         | 4.0    | 1093 | 1.5703          |
+| 0.8119        | 4.9991 | 1366 | 1.6773          |
+| 0.6565        | 5.9945 | 1638 | 1.7972          |
+
+
+### Framework versions
+
+- PEFT 0.12.0
+- Transformers 4.44.2
+- Pytorch 2.4.0+cu121
+- Datasets 2.21.0
+- Tokenizers 0.19.1

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