See axolotl config

axolotl version: 0.8.0.dev0

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

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

This model is a fine-tuned version of 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