SmolLM2-360M-Instruct-ita

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<details><summary>See axolotl config</summary>

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|>"

</details><br>

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

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

Framework versions

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