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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: HuggingFaceTB/SmolLM2-360M |
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tags: |
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- generated_from_trainer |
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- axolotl |
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datasets: |
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- ReDiX/everyday-conversations-ita |
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- ReDiX/DataForge |
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language: |
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- it |
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- en |
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pipeline_tag: text-generation |
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--- |
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[<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) |
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<details><summary>See axolotl config</summary> |
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axolotl version: `0.5.0` |
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```yaml |
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base_model: HuggingFaceTB/SmolLM2-360M |
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load_in_8bit: false |
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load_in_4bit: false |
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strict: false |
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datasets: |
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- path: ./dataforge |
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type: chat_template |
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field_messages: conversations |
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message_field_role: from |
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message_field_content: value |
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- path: HuggingFaceTB/smol-smoltalk |
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type: chat_template |
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field_messages: messages |
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message_field_role: role |
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message_field_content: content |
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chat_template: chatml |
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dataset_prepared_path: last_run_prepared |
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val_set_size: 0.1 |
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output_dir: ./outputs/smollm360m |
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sequence_len: 8192 |
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sample_packing: true |
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eval_sample_packing: true |
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pad_to_sequence_len: true |
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wandb_project: axolotl |
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wandb_entity: |
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wandb_watch: |
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wandb_name: smollm2 |
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wandb_log_model: |
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gradient_accumulation_steps: 4 |
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micro_batch_size: 4 |
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num_epochs: 2 |
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optimizer: adamw_bnb_8bit |
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lr_scheduler: cosine |
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learning_rate: 1.0e-03 |
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train_on_inputs: false |
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group_by_length: false |
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bf16: true |
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fp16: |
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tf32: false |
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gradient_checkpointing: true |
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early_stopping_patience: |
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resume_from_checkpoint: |
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local_rank: |
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logging_steps: 5 |
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xformers_attention: |
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flash_attention: true |
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warmup_steps: 10 |
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evals_per_epoch: 4 |
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eval_table_size: |
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eval_max_new_tokens: 128 |
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saves_per_epoch: 1 |
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debug: |
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deepspeed: |
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weight_decay: 0.0 |
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fsdp: |
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fsdp_config: |
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special_tokens: |
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pad_token: "<|im_end|>" |
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eos_token: "<|im_end|>" |
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``` |
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</details><br> |
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# SmolLM2 360M Instruct ITA |
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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). |
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Our datasets is a mixture of open source italian datasets and [ReDiX/everyday-conversations-ita](https://huggingface.co/datasets/ReDiX/everyday-conversations-ita) |
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It achieves the following results on the evaluation set: |
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- Loss: 0.8925 |
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## Model description |
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This model is an experiment to test out the [ReDiX/everyday-conversations-ita](https://huggingface.co/datasets/ReDiX/everyday-conversations-ita) dataset. |
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## Intended uses & limitations |
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Simple and very basic chat in italian and english |
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## Training and evaluation data |
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| Model | m_mmlu_it | arc_it | hellaswag_it | |
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|:------:|:----------:|:-------:|:-------------:| |
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| Qwen2.5-0.5-Instruct | **37.05** | 27.54 | 35.73 | |
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| ReDiX/SmolLM2-360M-Instruct-ita | 24.94 | **28.40** | **35.96** | |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.001 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 16 |
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- optimizer: Use adamw_bnb_8bit with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_steps: 10 |
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- num_epochs: 2 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:------:|:----:|:---------------:| |
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| No log | 0.0003 | 1 | 1.3366 | |
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| 1.0595 | 0.2501 | 774 | 1.0840 | |
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| 1.0194 | 0.5002 | 1548 | 1.0139 | |
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| 1.0075 | 0.7504 | 2322 | 0.9701 | |
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| 1.0286 | 1.0005 | 3096 | 0.9269 | |
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| 0.7871 | 1.2506 | 3870 | 0.9111 | |
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| 0.7481 | 1.5007 | 4644 | 0.8960 | |
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| 0.7429 | 1.7508 | 5418 | 0.8925 | |
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### Framework versions |
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- Transformers 4.46.2 |
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- Pytorch 2.5.1+cu124 |
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- Datasets 3.1.0 |
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- Tokenizers 0.20.3 |
