---
library_name: transformers
license: llama3.2
base_model: meta-llama/Llama-3.2-1B-Instruct
tags:
- axolotl
- OpenHermes
model-index:
- name: open-llama-Instruct
  results: []
datasets:
- diabolic6045/OpenHermes-2.5_alpaca_10
pipeline_tag: text-generation
---

<!-- 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. -->

# open-llama-Instruct

- This model is a fine-tuned version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) on the [diabolic6045/OpenHermes-2.5_alpaca_10](https://huggingface.co/datasets/diabolic6045/OpenHermes-2.5_alpaca_10) dataset. which is 10% of [OpenHermes 2.5 Dataset](https://huggingface.co/datasets/teknium/OpenHermes-2.5)

## 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
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 4
- total_eval_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 1
- mixed_precision_training: Native AMP

### Training results

- will be added soon

### Framework versions

- Transformers 4.45.2
- Pytorch 2.1.2
- Datasets 3.0.1
- Tokenizers 0.20.1

[<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: `0.4.1`
```yaml

base_model: meta-llama/Llama-3.2-1B-Instruct

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: diabolic6045/OpenHermes-2.5_alpaca_10
    type: alpaca
dataset_prepared_path: last_run_prepared
val_set_size: 0
output_dir: ./outputs/out
hub_model_id: diabolic6045/open-llama-Instruct
hf_use_auth_token: true

sequence_len: 1024
sample_packing: true
pad_to_sequence_len: true

wandb_project: open-llama
wandb_entity: 
wandb_watch: all
wandb_name: open-llama
wandb_log_model: 

gradient_accumulation_steps: 1
micro_batch_size: 2
num_epochs: 1

optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 2e-5

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

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
xformers_attention:
flash_attention: false

warmup_steps: 10
evals_per_epoch: 2
eval_table_size:
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  pad_token: <|end_of_text|>

```

</details><br>