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judicial-summarization-llama-3-finetuned_mildsum_TR
Browse files- README.md +70 -0
- adapter_model.safetensors +1 -1
README.md
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---
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base_model: unsloth/llama-3-8b-bnb-4bit
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library_name: peft
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license: llama3
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tags:
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- trl
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- sft
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- unsloth
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- generated_from_trainer
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model-index:
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- name: judicial-summarization-llama-3-finetuned_mildsum_TR
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# judicial-summarization-llama-3-finetuned_mildsum_TR
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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.
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It achieves the following results on the evaluation set:
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- Loss: 2.0157
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 0.0002
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- train_batch_size: 2
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- eval_batch_size: 8
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- seed: 3407
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 8
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_steps: 5
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- num_epochs: 6
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### Training results
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| Training Loss | Epoch | Step | Validation Loss |
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|:-------------:|:-----:|:----:|:---------------:|
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| 1.5351 | 1.0 | 273 | 1.6661 |
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| 1.3986 | 2.0 | 546 | 1.6676 |
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| 1.3575 | 3.0 | 819 | 1.7026 |
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| 1.2119 | 4.0 | 1092 | 1.7920 |
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| 1.0078 | 5.0 | 1365 | 1.8972 |
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| 0.7387 | 6.0 | 1638 | 2.0157 |
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### Framework versions
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- PEFT 0.12.0
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- Transformers 4.44.2
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- Pytorch 2.4.0+cu121
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- Datasets 2.21.0
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- Tokenizers 0.19.1
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adapter_model.safetensors
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@@ -1,3 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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-
oid sha256:
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size 167832240
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version https://git-lfs.github.com/spec/v1
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oid sha256:0330f6bf49abae9c42739c07b376111fe02864a5dc56b2fda64bda446974769d
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size 167832240
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