---
library_name: peft
license: llama3
base_model: NousResearch/Hermes-3-Llama-3.2-3B
tags:
- generated_from_trainer
datasets:
- mhenrichsen/alpaca_2k_test
model-index:
- name: outputs/lora-out
  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: NousResearch/Hermes-3-Llama-3.2-3B
# optionally might have model_type or tokenizer_type
model_type: LlamaForCausalLM
tokenizer_type: AutoTokenizer
# Automatically upload checkpoint and final model to HF
# hub_model_id: username/custom_model_name

load_in_8bit: true
load_in_4bit: false
strict: false

datasets:
  - path: mhenrichsen/alpaca_2k_test
    type: alpaca
dataset_prepared_path:
val_set_size: 0.05
output_dir: ./outputs/lora-out

sequence_len: 4096
sample_packing: true
eval_sample_packing: false
pad_to_sequence_len: true

adapter: lora
lora_model_dir:
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:
lora_modules_to_save:
  - embed_tokens
  - lm_head

wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 4
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0002

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
s2_attention:

warmup_steps: 10
evals_per_epoch: 4
eval_table_size:
eval_max_new_tokens: 128
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
   pad_token: <|end_of_text|>

```

</details><br>

# outputs/lora-out

This model is a fine-tuned version of [NousResearch/Hermes-3-Llama-3.2-3B](https://huggingface.co/NousResearch/Hermes-3-Llama-3.2-3B) on the mhenrichsen/alpaca_2k_test dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9717

## 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: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB 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: 10
- num_epochs: 4.0

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.2051        | 0.0930 | 1    | 1.3039          |
| 1.0777        | 0.2791 | 3    | 1.2884          |
| 1.1398        | 0.5581 | 6    | 1.1808          |
| 1.0909        | 0.8372 | 9    | 1.0715          |
| 0.8999        | 1.0930 | 12   | 0.9657          |
| 0.7534        | 1.3721 | 15   | 0.9733          |
| 0.7596        | 1.6512 | 18   | 0.9730          |
| 0.7925        | 1.9302 | 21   | 0.9712          |
| 0.6335        | 2.1860 | 24   | 0.9683          |
| 0.6694        | 2.4651 | 27   | 0.9722          |
| 0.6156        | 2.7442 | 30   | 0.9713          |
| 0.6567        | 3.0    | 33   | 0.9704          |
| 0.6581        | 3.2791 | 36   | 0.9713          |
| 0.6023        | 3.5581 | 39   | 0.9717          |


### Framework versions

- PEFT 0.14.0
- Transformers 4.49.0
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0