---
library_name: transformers
license: apache-2.0
base_model: unsloth/SmolLM-360M
tags:
- axolotl
- generated_from_trainer
datasets:
- argilla/databricks-dolly-15k-curated-en
model-index:
- name: SmolLM-360M
  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: `0.6.0`
```yaml
base_model: unsloth/SmolLM-360M
batch_size: 92
bf16: true
chat_template: tokenizer_default_fallback_alpaca
datasets:
- format: custom
  path: argilla/databricks-dolly-15k-curated-en
  type:
    field_input: original-instruction
    field_instruction: original-instruction
    field_output: original-response
    format: '{instruction} {input}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
device_map: auto
eval_sample_packing: false
eval_steps: 20
flash_attention: true
gradient_checkpointing: true
group_by_length: true
hub_model_id: SystemAdmin123/SmolLM-360M
hub_strategy: checkpoint
learning_rate: 0.0002
logging_steps: 10
lr_scheduler: cosine
max_steps: 10000
micro_batch_size: 23
model_type: AutoModelForCausalLM
num_epochs: 100
optimizer: adamw_bnb_8bit
output_dir: /root/.sn56/axolotl/tmp/SmolLM-360M
pad_to_sequence_len: true
resize_token_embeddings_to_32x: false
sample_packing: true
save_steps: 20
save_total_limit: 1
sequence_len: 2048
tokenizer_type: GPT2TokenizerFast
torch_dtype: bf16
training_args_kwargs:
  hub_private_repo: true
trust_remote_code: true
val_set_size: 0.1
wandb_entity: ''
wandb_mode: online
wandb_name: unsloth/SmolLM-360M-argilla/databricks-dolly-15k-curated-en
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: default
warmup_ratio: 0.05

```

</details><br>

# SmolLM-360M

This model is a fine-tuned version of [unsloth/SmolLM-360M](https://huggingface.co/unsloth/SmolLM-360M) on the argilla/databricks-dolly-15k-curated-en dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0673

## 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: 23
- eval_batch_size: 23
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 92
- total_eval_batch_size: 92
- 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
- training_steps: 200

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log        | 0.125 | 1    | 2.5584          |
| 2.2406        | 2.5   | 20   | 2.1562          |
| 2.136         | 5.0   | 40   | 2.0829          |
| 2.0938        | 7.5   | 60   | 2.0711          |
| 2.0632        | 10.0  | 80   | 2.0679          |
| 2.0298        | 12.5  | 100  | 2.0621          |
| 2.0168        | 15.0  | 120  | 2.0567          |
| 2.0188        | 17.5  | 140  | 2.0686          |
| 2.0108        | 20.0  | 160  | 2.0701          |
| 2.0169        | 22.5  | 180  | 2.0683          |
| 2.0109        | 25.0  | 200  | 2.0673          |


### Framework versions

- Transformers 4.48.1
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0