---
library_name: transformers
base_model: mrcuddle/Dark-Hermes3-Llama3.2-3B
datasets:
- llamafactory/alpaca_en
model-index:
- name: content/outputs
  results: []
language:
  - en
tags:
  - Llama-3
  - instruct
  - finetune
  - chatml
  - gpt4
  - synthetic data
  - distillation
  - function calling
  - json mode
  - axolotl
  - roleplaying
  - chat
  - generated_from_trainer
widget:
  - example_title: Hermes 3
    messages:
      - role: system
        content: >-
          You are a sentient, superintelligent artificial general intelligence,
          here to teach and assist me.
      - role: user
        content: >-
          Write a short story about Goku discovering kirby has teamed up with
          Majin Buu to destroy the world.
---

<!-- 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/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>

axolotl version: `0.6.0`
```yaml
base_model: mrcuddle/Dark-Hermes3-Llama3.2-3B
dataloader_num_workers: 4
datasets:
- dataset_prepared_path: last_run_prepared
  path: llamafactory/alpaca_en
  type: alpaca
eval_steps: 500
evaluation_strategy: steps
fp16: true
gradient_accumulation_steps: 8
gradient_checkpointing: false
learning_rate: 2e-5
load_in_4bit: false
logging_dir: /content/outputs/logs
logging_steps: 10
lr_scheduler: cosine
lr_scheduler_type: cosine
micro_batch_size: 1
num_train_epochs: 3
optimizer: paged_adamw_8bit
output_dir: /content/outputs
overwrite_output_dir: true
per_device_train_batch_size: 4
save_steps: 500
save_total_limit: 2
use_peft: false
val_set_size: 0.05
warmup_steps: 100

```

</details><br>

# content/outputs

This model is a fine-tuned version of [mrcuddle/Dark-Hermes3-Llama3.2-3B](https://huggingface.co/mrcuddle/Dark-Hermes3-Llama3.2-3B) on the llamafactory/alpaca_en dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1205

## 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: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- optimizer: Use paged_adamw_8bit with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 1

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log        | 0.0002 | 1    | 2.4030          |
| 1.2572        | 0.0814 | 500  | 1.1935          |
| 1.3061        | 0.1629 | 1000 | 1.1865          |
| 1.2733        | 0.2443 | 1500 | 1.1864          |
| 1.265         | 0.3258 | 2000 | 1.1753          |
| 1.2436        | 0.4072 | 2500 | 1.1542          |
| 1.2935        | 0.4887 | 3000 | 1.1448          |
| 1.2595        | 0.5701 | 3500 | 1.1348          |
| 1.2896        | 0.6515 | 4000 | 1.1295          |
| 1.2081        | 0.7330 | 4500 | 1.1236          |
| 1.2451        | 0.8144 | 5000 | 1.1212          |
| 1.2134        | 0.8959 | 5500 | 1.1205          |
| 1.2437        | 0.9773 | 6000 | 1.1205          |


### Framework versions

- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0