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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-humaneval
+model-index:
+- name: Llama-3.1-8B-Instruct-sft-5e-3-epoch-100-human-eval-final
+  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/b08pi5fy)
+# Llama-3.1-8B-Instruct-sft-5e-3-epoch-100-human-eval-final
+
+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-humaneval dataset.
+It achieves the following results on the evaluation set:
+- Loss: 5.3754
+- Loss Layer 4 Head: 1.6774
+- Loss Layer 8 Head: 1.3806
+- Loss Layer 12 Head: 1.2795
+- Loss Layer 16 Head: 0.6378
+- Loss Layer 20 Head: 0.3110
+- Loss Layer 24 Head: 0.1844
+- Loss Layer 28 Head: 0.0864
+
+## 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 |
+|:-------------:|:-------:|:----:|:---------------:|:-----------------:|:-----------------:|:------------------:|:------------------:|:------------------:|:------------------:|:------------------:|
+| 7.7477        | 9.6823  | 200  | 7.6952          | 1.9941            | 1.7442            | 1.9609             | 1.0923             | 0.4414             | 0.2459             | 0.4381             |
+| 5.8078        | 19.3646 | 400  | 6.4289          | 1.9090            | 1.5288            | 1.4099             | 0.9812             | 0.3976             | 0.2383             | 0.1448             |
+| 4.8435        | 29.0469 | 600  | 5.9964          | 1.8480            | 1.5236            | 1.3836             | 0.6737             | 0.3976             | 0.2537             | 0.1092             |
+| 4.6084        | 38.7292 | 800  | 6.0069          | 1.8460            | 1.7121            | 1.3111             | 0.6743             | 0.3436             | 0.2146             | 0.0977             |
+| 4.0625        | 48.4115 | 1000 | 5.7159          | 1.8920            | 1.4329            | 1.3107             | 0.6548             | 0.3220             | 0.1980             | 0.0920             |
+| 3.7565        | 58.0938 | 1200 | 5.4530          | 1.7095            | 1.3997            | 1.2900             | 0.6451             | 0.3159             | 0.1877             | 0.0897             |
+| 3.5758        | 67.7761 | 1400 | 5.4088          | 1.6897            | 1.3862            | 1.2843             | 0.6413             | 0.3125             | 0.1860             | 0.0880             |
+| 3.5369        | 77.4584 | 1600 | 5.3933          | 1.6839            | 1.3837            | 1.2815             | 0.6409             | 0.3124             | 0.1856             | 0.0870             |
+| 3.51          | 87.1407 | 1800 | 5.3780          | 1.6781            | 1.3809            | 1.2799             | 0.6378             | 0.3111             | 0.1843             | 0.0865             |
+| 3.4762        | 96.8230 | 2000 | 5.3754          | 1.6774            | 1.3806            | 1.2795             | 0.6378             | 0.3110             | 0.1844             | 0.0864             |
+
+
+### Framework versions
+
+- Transformers 4.43.2
+- Pytorch 2.4.1+cu121
+- Datasets 3.0.1
+- Tokenizers 0.19.1

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