DarkHermes3-Llama3.2-3B-Instruct

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<details><summary>See axolotl config</summary>

axolotl version: 0.6.0

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 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

Framework versions

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