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README.md
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library_name: transformers
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model_name: Llama-3.1-8B-KAM
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tags:
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- generated_from_trainer
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- trl
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- sft
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licence: license
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---
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# Model Card for Llama-3.1-8B-KAM
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This model is a fine-tuned version of [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct).
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## Quick start
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## Training procedure
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### Framework versions
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- Transformers: 4.46.2
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- Pytorch: 2.4.0
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- Datasets: 3.0.1
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- Tokenizers: 0.20.0
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## Citations
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Cite TRL as:
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```bibtex
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@misc{vonwerra2022trl,
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title = {{TRL: Transformer Reinforcement Learning}},
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author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
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year = 2020,
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journal = {GitHub repository},
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publisher = {GitHub},
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howpublished = {\url{https://github.com/huggingface/trl}}
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}
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```
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library_name: transformers
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model_name: Llama-3.1-8B-KAM
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tags:
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- trl
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- Llama
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- sft
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- generated_from_trainer
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licence: license
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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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# Model Card for Llama-3.1-8B-KAM
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This model is a fine-tuned version of [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) on the None dataset.
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## Model description
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More information needed
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## Quick start
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## Training procedure
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This model was trained with SFT.
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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: 1
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- eval_batch_size: 8
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- seed: 3407
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- gradient_accumulation_steps: 16
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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: 20
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- training_steps: 500
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- mixed_precision_training: Native AMP
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### Training results
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#### Step Training Loss
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- 50 2.158200
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- 100 1.845900
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- 150 1.832200
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- 200 1.805300
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- 250 1.783800
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- 300 1.767500
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- 350 1.744800
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- 400 1.745600
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- 450 1.749500
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- 500 1.756100
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### Framework versions
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- Transformers: 4.46.2
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- Pytorch: 2.4.0
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- Datasets: 3.0.1
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- Tokenizers: 0.20.0
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