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README.md

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+---
+license: llama3.1
+base_model: meta-llama/Llama-3.1-8B-Instruct
+tags:
+- alignment-handbook
+- generated_from_trainer
+datasets:
+- meng-lab/Llama-3.1-8B-Instruct-xsum
+model-index:
+- name: Llama-3.1-8B-Instruct-sft-5e-3-epoch-100-xsum
+  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. -->
+
+[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/uva-llm/huggingface/runs/0cft2k89)
+# Llama-3.1-8B-Instruct-sft-5e-3-epoch-100-xsum
+
+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 meng-lab/Llama-3.1-8B-Instruct-xsum dataset.
+It achieves the following results on the evaluation set:
+- Loss: 6.7117
+- Loss Layer 4 Head: 1.7377
+- Loss Layer 8 Head: 1.4957
+- Loss Layer 12 Head: 1.4384
+- Loss Layer 16 Head: 0.9421
+- Loss Layer 20 Head: 0.5804
+- Loss Layer 24 Head: 0.3724
+- Loss Layer 28 Head: 0.1958
+
+## 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: 0.005
+- train_batch_size: 1
+- eval_batch_size: 2
+- seed: 42
+- distributed_type: multi-GPU
+- num_devices: 4
+- gradient_accumulation_steps: 32
+- total_train_batch_size: 128
+- total_eval_batch_size: 8
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 100
+
+### Training results
+
+| Training Loss | Epoch   | Step | Validation Loss | Loss Layer 4 Head | Loss Layer 8 Head | Loss Layer 12 Head | Loss Layer 16 Head | Loss Layer 20 Head | Loss Layer 24 Head | Loss Layer 28 Head |
+|:-------------:|:-------:|:----:|:---------------:|:-----------------:|:-----------------:|:------------------:|:------------------:|:------------------:|:------------------:|:------------------:|
+| 9.417         | 9.5522  | 200  | 10.6034         | 2.1800            | 2.1484            | 1.8370             | 1.5560             | 0.8850             | 0.7908             | 1.1904             |
+| 7.0666        | 19.1045 | 400  | 8.3242          | 2.0259            | 1.8363            | 1.7901             | 1.0876             | 0.8469             | 0.4822             | 0.2917             |
+| 6.5999        | 28.6567 | 600  | 7.8689          | 1.9122            | 1.7362            | 1.7044             | 1.0472             | 0.6722             | 0.4620             | 0.3698             |
+| 5.8586        | 38.2090 | 800  | 7.5812          | 2.0916            | 1.5734            | 1.6211             | 1.0056             | 0.6192             | 0.4660             | 0.2400             |
+| 5.4725        | 47.7612 | 1000 | 7.0153          | 1.8457            | 1.5162            | 1.4691             | 0.9794             | 0.6236             | 0.3980             | 0.2260             |
+| 5.3026        | 57.3134 | 1200 | 7.0204          | 1.9164            | 1.5058            | 1.5172             | 0.9522             | 0.5897             | 0.3804             | 0.2035             |
+| 4.9989        | 66.8657 | 1400 | 6.7446          | 1.7458            | 1.5005            | 1.4430             | 0.9468             | 0.5843             | 0.3757             | 0.1990             |
+| 4.9163        | 76.4179 | 1600 | 6.7228          | 1.7406            | 1.4972            | 1.4401             | 0.9436             | 0.5816             | 0.3734             | 0.1968             |
+| 4.9194        | 85.9701 | 1800 | 6.7132          | 1.7381            | 1.4960            | 1.4385             | 0.9424             | 0.5807             | 0.3726             | 0.1959             |
+| 4.9063        | 95.5224 | 2000 | 6.7117          | 1.7377            | 1.4957            | 1.4384             | 0.9421             | 0.5804             | 0.3724             | 0.1958             |
+
+
+### Framework versions
+
+- Transformers 4.43.2
+- Pytorch 2.4.1+cu121
+- Datasets 3.0.1
+- Tokenizers 0.19.1

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