---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen2.5-0.5B-Instruct
tags:
- generated_from_trainer
- axolotl
language:
- it
- en
pipeline_tag: text-generation
datasets:
- ReDiX/everyday-conversations-ita
- ReDiX/dataforge-cleaned
---

# Qwen2.5-0.5B-Instruct-ITA

This model is a fine-tuned version of [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) on the [ReDiX/DataForge](https://huggingface.co/datasets/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


|   Tasks    |Version|Filter|n-shot| Metric |   |Value |   |Stderr|
|------------|------:|------|-----:|--------|---|-----:|---|-----:|
|arc_it      |      2|none  |     0|acc     |↑  |0.2378|±  |0.0125|
|            |       |none  |     0|acc_norm|↑  |0.2823|±  |0.0132|
|hellaswag_it|      1|none  |     0|acc     |↑  |0.3163|±  |0.0049|
|            |       |none  |     0|acc_norm|↑  |0.3800|±  |0.0051|
|m_mmlu_it   |      0|none  |     5|acc     |↑  |0.381 |±  |0.0042|

## 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


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

axolotl version: `0.5.0`
```yaml
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|>"


```

</details><br>


### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log        | 0.0013 | 1    | 1.7855          |
| 1.2567        | 0.2504 | 194  | 1.5639          |
| 1.2551        | 0.5008 | 388  | 1.4980          |
| 1.1845        | 0.7512 | 582  | 1.4501          |
| 1.3178        | 1.0019 | 776  | 1.4252          |
| 1.06          | 1.2523 | 970  | 1.4187          |
| 1.0697        | 1.5027 | 1164 | 1.4116          |
| 1.0362        | 1.7531 | 1358 | 1.4100          |


### Framework versions

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