---
library_name: transformers
license: llama3.2
base_model: NousResearch/Llama-3.2-1B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 0c2649cc-2fe7-4e88-b672-6da1fee4001f
  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: `0.4.1`
```yaml
base_model: NousResearch/Llama-3.2-1B
batch_size: 32
bf16: true
chat_template: tokenizer_default_fallback_alpaca
datasets:
- data_files:
  - f51beb4c568b9128_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/f51beb4c568b9128_train_data.json
  type:
    field_input: keywords
    field_instruction: idea
    field_output: full_response
    format: '{instruction} {input}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
eval_steps: 20
flash_attention: true
gpu_memory_limit: 80GiB
gradient_checkpointing: true
group_by_length: true
hub_model_id: willtensora/0c2649cc-2fe7-4e88-b672-6da1fee4001f
hub_strategy: checkpoint
learning_rate: 0.0002
logging_steps: 10
lr_scheduler: cosine
max_steps: 2500
micro_batch_size: 4
model_type: AutoModelForCausalLM
optimizer: adamw_bnb_8bit
output_dir: /workspace/axolotl/configs
pad_to_sequence_len: true
resize_token_embeddings_to_32x: false
sample_packing: false
save_steps: 40
save_total_limit: 1
sequence_len: 2048
special_tokens:
  pad_token: <|end_of_text|>
tokenizer_type: PreTrainedTokenizerFast
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.1
wandb_entity: ''
wandb_mode: online
wandb_name: NousResearch/Llama-3.2-1B-/workspace/input_data/f51beb4c568b9128_train_data.json
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: default
warmup_ratio: 0.05
xformers_attention: true

```

</details><br>

# 0c2649cc-2fe7-4e88-b672-6da1fee4001f

This model is a fine-tuned version of [NousResearch/Llama-3.2-1B](https://huggingface.co/NousResearch/Llama-3.2-1B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0849

## 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: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 32
- total_eval_batch_size: 32
- 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: 12
- training_steps: 258

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log        | 0.0005 | 1    | 0.2074          |
| 0.5472        | 0.0097 | 20   | 0.1746          |
| 0.3199        | 0.0194 | 40   | 0.2036          |
| 0.2013        | 0.0291 | 60   | 0.1772          |
| 0.0903        | 0.0388 | 80   | 0.1702          |
| 0.0875        | 0.0485 | 100  | 0.2040          |
| 0.1425        | 0.0582 | 120  | 0.1392          |
| 0.1982        | 0.0679 | 140  | 0.1194          |
| 0.1372        | 0.0776 | 160  | 0.1014          |
| 0.0278        | 0.0873 | 180  | 0.0952          |
| 0.0248        | 0.0970 | 200  | 0.0893          |
| 0.1051        | 0.1067 | 220  | 0.0875          |
| 0.0649        | 0.1164 | 240  | 0.0849          |


### Framework versions

- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1