---
library_name: transformers
license: llama3
base_model: mrcuddle/Dark-Hermes3-Llama3.2-3B
tags:
- axolotl
- generated_from_trainer
datasets:
- NousResearch/hermes-function-calling-v1
model-index:
- name: Dark-Hermes3-Llama3.2-3B-Func
  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/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: `0.8.0.dev0`
```yaml
base_model: mrcuddle/Dark-Hermes3-Llama3.2-3B
hub_model_id: mrcuddle/Dark-Hermes3-Llama3.2-3B-Func
dataloader_num_workers: 8
datasets:
- chat_template: alpaca
  field_messages: conversations
  message_property_mappings:
    content: value
    role: from
  path: NousResearch/hermes-function-calling-v1
  split: train
  type: chat_template
eval_steps: 500
evaluation_strategy: steps
fp16: true
gradient_accumulation_steps: 4
gradient_checkpointing: true
learning_rate: 2e-5
logging_dir: /content/outputs/logs
logging_steps: 50
lr_scheduler: linear
lr_scheduler_type: linear
micro_batch_size: 2
num_train_epochs: 3
optimizer: adamw_torch  # Or another optimizer of your choice
output_dir: /content/outputs
overwrite_output_dir: true
per_device_train_batch_size: 8
save_steps: 500
save_total_limit: 2
use_peft: false
val_set_size: 0.05
warmup_steps: 100
unsloth: true  # Enable Unsloth if supported by your training framework

```

</details><br>

# Dark-Hermes3-Llama3.2-3B-Func

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 NousResearch/hermes-function-calling-v1 dataset.

## 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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 1.0

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log        | 0.0889 | 1    | 0.3864          |


### Framework versions

- Transformers 4.49.0
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0