axolotl version: 0.5.0
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|>"
SmolLM2 360M Instruct ITA
This model is a fine-tuned version of HuggingFaceTB/SmolLM2-360M on the smol-smoltalk dataset and on the ReDiX/DataForge. Our datasets is a mixture of open source italian datasets and 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 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