---
base_model: /home/jovyan/workspace/PipeDec/checkpoint/Llama-3.1-8B-Instruct-sft-5e-3-epoch-100-humaneval-stage-2
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-humaneval-stage-3
  results: []
---

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[<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/86389vz6)
# Llama-3.1-8B-Instruct-sft-5e-3-epoch-100-humaneval-stage-3

This model is a fine-tuned version of [/home/jovyan/workspace/PipeDec/checkpoint/Llama-3.1-8B-Instruct-sft-5e-3-epoch-100-humaneval-stage-2](https://huggingface.co//home/jovyan/workspace/PipeDec/checkpoint/Llama-3.1-8B-Instruct-sft-5e-3-epoch-100-humaneval-stage-2) on the meng-lab/Llama-3.1-8B-Instruct-humaneval dataset.
It achieves the following results on the evaluation set:
- Loss: 11.0822
- Loss Three Hop Layer 8 Head: 3.3949
- Loss Three Hop Layer 16 Head: 2.9406
- Loss Three Hop Layer 24 Head: 2.6311
- Loss Three Hop Layer 32 Head: 2.4800

## 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: 8
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- total_eval_batch_size: 16
- 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 Three Hop Layer 8 Head | Loss Three Hop Layer 16 Head | Loss Three Hop Layer 24 Head | Loss Three Hop Layer 32 Head |
|:-------------:|:-------:|:----:|:---------------:|:---------------------------:|:----------------------------:|:----------------------------:|:----------------------------:|
| 17.1204       | 9.6677  | 200  | 17.4024         | 4.0165                      | 3.6938                       | 5.0117                       | 5.0980                       |
| 11.7831       | 19.3353 | 400  | 12.4640         | 3.8214                      | 3.1283                       | 2.8095                       | 3.0563                       |
| 11.1082       | 29.0030 | 600  | 12.3118         | 3.4779                      | 3.1955                       | 2.9590                       | 2.9857                       |
| 10.9205       | 38.6707 | 800  | 11.9277         | 3.7709                      | 3.0051                       | 2.8893                       | 2.6454                       |
| 10.1281       | 48.3384 | 1000 | 11.6923         | 3.4574                      | 2.9719                       | 2.8398                       | 2.7656                       |
| 9.4147        | 58.0060 | 1200 | 11.2543         | 3.4058                      | 2.9891                       | 2.6593                       | 2.5635                       |
| 8.9315        | 67.6737 | 1400 | 11.0952         | 3.3972                      | 2.9370                       | 2.6327                       | 2.4895                       |
| 8.9092        | 77.3414 | 1600 | 11.1042         | 3.4010                      | 2.9454                       | 2.6344                       | 2.4875                       |
| 8.8371        | 87.0091 | 1800 | 11.0849         | 3.3957                      | 2.9410                       | 2.6311                       | 2.4803                       |
| 8.8213        | 96.6767 | 2000 | 11.0822         | 3.3949                      | 2.9406                       | 2.6311                       | 2.4800                       |


### Framework versions

- Transformers 4.43.2
- Pytorch 2.6.0+cu124
- Datasets 3.2.0
- Tokenizers 0.19.1