155 lines
3.9 KiB
Markdown
155 lines
3.9 KiB
Markdown
---
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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 |