Qwen2.5-0.5B-Instruct-ITA

This model is a fine-tuned version of Qwen/Qwen2.5-0.5B-Instruct on the ReDiX/DataForge dataset. It achieves the following results on the evaluation set:

• Loss: 1.4100

Model description

This model is an example of finetuning a sLLM. Italian eval improved and the model learned as espected from the training data

Intended uses & limitations

More information needed

Training and evaluation data

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.0001
• 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

axolotl version: 0.5.0

base_model: Qwen/Qwen2.5-0.5B-Instruct

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

# chat_template: chatml
dataset_prepared_path: last_run_prepared
val_set_size: 0.1
output_dir: ./outputs/qwen05B

unfrozen_parameters:
- ^lm_head.weight$
- ^model.embed_tokens.weight$
# mlp.down_proj layers
- model.layers.0.mlp.down_proj
- model.layers.23.mlp.down_proj
- model.layers.1.mlp.down_proj
- model.layers.16.mlp.down_proj
- model.layers.4.mlp.down_proj
- model.layers.17.mlp.down_proj
# mlp.gate_proj layers
- model.layers.0.mlp.gate_proj
- model.layers.1.mlp.gate_proj
- model.layers.2.mlp.gate_proj
- model.layers.3.mlp.gate_proj
- model.layers.4.mlp.gate_proj
- model.layers.7.mlp.gate_proj
# mlp.up_proj layers
- model.layers.1.mlp.up_proj
- model.layers.0.mlp.up_proj
- model.layers.3.mlp.up_proj
- model.layers.4.mlp.up_proj
- model.layers.7.mlp.up_proj
- model.layers.9.mlp.up_proj
# self_attn.k_proj layers
- model.layers.18.self_attn.k_proj
- model.layers.7.self_attn.k_proj
- model.layers.19.self_attn.k_proj
- model.layers.2.self_attn.k_proj
- model.layers.6.self_attn.k_proj
- model.layers.9.self_attn.k_proj
# self_attn.o_proj layers
- model.layers.16.self_attn.o_proj
- model.layers.19.self_attn.o_proj
- model.layers.0.self_attn.o_proj
- model.layers.20.self_attn.o_proj
- model.layers.4.self_attn.o_proj
- model.layers.3.self_attn.o_proj
# self_attn.q_proj layers
- model.layers.13.self_attn.q_proj
- model.layers.16.self_attn.q_proj
- model.layers.21.self_attn.q_proj
- model.layers.11.self_attn.q_proj
- model.layers.15.self_attn.q_proj
- model.layers.6.self_attn.q_proj
# self_attn.v_proj layers
- model.layers.2.self_attn.v_proj
- model.layers.3.self_attn.v_proj
- model.layers.4.self_attn.v_proj
- model.layers.5.self_attn.v_proj
- model.layers.7.self_attn.v_proj
- model.layers.8.self_attn.v_proj



sequence_len: 4096
sample_packing: true
eval_sample_packing: true
pad_to_sequence_len: true


wandb_project: axolotl
wandb_entity:
wandb_watch:
wandb_name: qwen2.5-0.5B
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-04

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|>"

Training results

Framework versions

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