---
library_name: transformers
license: apache-2.0
base_model: HuggingFaceTB/SmolLM2-360M
tags:
- generated_from_trainer
- axolotl
datasets:
- ReDiX/everyday-conversations-ita
- ReDiX/DataForge
language:
- it
- en
pipeline_tag: text-generation
---

[<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: `0.5.0`
```yaml
base_model: HuggingFaceTB/SmolLM2-360M

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: ./dataforge
    type: chat_template

    field_messages: conversations
    message_field_role: from
    message_field_content: value
  
  - path: HuggingFaceTB/smol-smoltalk
    type: chat_template

    field_messages: messages
    message_field_role: role
    message_field_content: content

chat_template: chatml
dataset_prepared_path: last_run_prepared
val_set_size: 0.1
output_dir: ./outputs/smollm360m

sequence_len: 8192
sample_packing: true
eval_sample_packing: true
pad_to_sequence_len: true

wandb_project: axolotl
wandb_entity:
wandb_watch:
wandb_name: smollm2
wandb_log_model:

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 2
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1.0e-03

train_on_inputs: false
group_by_length: false
bf16: true
fp16: 
tf32: false

gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 5
xformers_attention:
flash_attention: true

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: "<|im_end|>"
  eos_token: "<|im_end|>"
```

</details><br>

# SmolLM2 360M Instruct ITA

This model is a fine-tuned version of [HuggingFaceTB/SmolLM2-360M](https://huggingface.co/HuggingFaceTB/SmolLM2-360M) on the [smol-smoltalk](https://huggingface.co/datasets/HuggingFaceTB/smol-smoltalk) dataset and on the [ReDiX/DataForge](https://huggingface.co/datasets/ReDiX/DataForge).
Our datasets is a mixture of open source italian datasets and [ReDiX/everyday-conversations-ita](https://huggingface.co/datasets/ReDiX/everyday-conversations-ita)
It achieves the following results on the evaluation set:
- Loss: 0.8925

## Model description

This model is an experiment to test out the [ReDiX/everyday-conversations-ita](https://huggingface.co/datasets/ReDiX/everyday-conversations-ita) dataset.

## Intended uses & limitations

Simple and very basic chat in italian and english

## Training and evaluation data

| Model  |  m_mmlu_it | arc_it  | hellaswag_it  |
|:------:|:----------:|:-------:|:-------------:|
| Qwen2.5-0.5-Instruct | **37.05** | 27.54 | 35.73 |
| ReDiX/SmolLM2-360M-Instruct-ita | 24.94 | **28.40** | **35.96** |


## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Use adamw_bnb_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: 10
- num_epochs: 2

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log        | 0.0003 | 1    | 1.3366          |
| 1.0595        | 0.2501 | 774  | 1.0840          |
| 1.0194        | 0.5002 | 1548 | 1.0139          |
| 1.0075        | 0.7504 | 2322 | 0.9701          |
| 1.0286        | 1.0005 | 3096 | 0.9269          |
| 0.7871        | 1.2506 | 3870 | 0.9111          |
| 0.7481        | 1.5007 | 4644 | 0.8960          |
| 0.7429        | 1.7508 | 5418 | 0.8925          |


### Framework versions

- Transformers 4.46.2
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3