---
library_name: peft
license: apache-2.0
base_model: unsloth/Qwen2.5-0.5B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: fe8d8eb3-c8bf-41c5-806b-37f7a662e395
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

[<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: `0.4.1`
```yaml
adapter: lora
base_model: unsloth/Qwen2.5-0.5B
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - ea66d0c68c983721_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/ea66d0c68c983721_train_data.json
  type:
    field_instruction: sentence1
    field_output: sentence2
    format: '{instruction}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
device_map:
  ? ''
  : 0,1,2,3,4,5,6,7
early_stopping_patience: 2
eval_max_new_tokens: 128
eval_steps: 100
eval_table_size: null
flash_attention: true
gradient_accumulation_steps: 8
gradient_checkpointing: true
group_by_length: false
hub_model_id: Alphatao/fe8d8eb3-c8bf-41c5-806b-37f7a662e395
hub_repo: null
hub_strategy: null
hub_token: null
learning_rate: 0.0002
load_best_model_at_end: true
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 32
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lora_target_modules:
- q_proj
- k_proj
- v_proj
- o_proj
lr_scheduler: cosine
max_grad_norm: 1.0
max_steps: 7680
micro_batch_size: 4
mlflow_experiment_name: /tmp/ea66d0c68c983721_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 2
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 100
sequence_len: 1024
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: a6f83d3c-a5d6-4fb0-984f-06ddde5e8f44
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: a6f83d3c-a5d6-4fb0-984f-06ddde5e8f44
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null

```

</details><br>

# fe8d8eb3-c8bf-41c5-806b-37f7a662e395

This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B](https://huggingface.co/unsloth/Qwen2.5-0.5B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3488

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_BNB 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
- training_steps: 5212

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 4.8428        | 0.0004 | 1    | 4.6827          |
| 1.7175        | 0.0384 | 100  | 1.7035          |
| 1.4774        | 0.0767 | 200  | 1.6374          |
| 1.5973        | 0.1151 | 300  | 1.6064          |
| 1.4442        | 0.1535 | 400  | 1.5828          |
| 1.7331        | 0.1919 | 500  | 1.5556          |
| 1.4103        | 0.2302 | 600  | 1.5370          |
| 1.1782        | 0.2686 | 700  | 1.5209          |
| 1.4832        | 0.3070 | 800  | 1.5039          |
| 1.5766        | 0.3454 | 900  | 1.4915          |
| 1.3976        | 0.3837 | 1000 | 1.4837          |
| 1.5218        | 0.4221 | 1100 | 1.4667          |
| 1.5943        | 0.4605 | 1200 | 1.4576          |
| 1.3976        | 0.4989 | 1300 | 1.4535          |
| 1.3621        | 0.5372 | 1400 | 1.4401          |
| 1.5817        | 0.5756 | 1500 | 1.4286          |
| 1.0866        | 0.6140 | 1600 | 1.4200          |
| 1.307         | 0.6524 | 1700 | 1.4056          |
| 1.3197        | 0.6907 | 1800 | 1.4006          |
| 1.4547        | 0.7291 | 1900 | 1.3875          |
| 1.3411        | 0.7675 | 2000 | 1.3820          |
| 1.3654        | 0.8059 | 2100 | 1.3750          |
| 1.5037        | 0.8442 | 2200 | 1.3656          |
| 1.3596        | 0.8826 | 2300 | 1.3536          |
| 1.2466        | 0.9210 | 2400 | 1.3483          |
| 1.1555        | 0.9594 | 2500 | 1.3401          |
| 1.6812        | 0.9977 | 2600 | 1.3363          |
| 1.0617        | 1.0363 | 2700 | 1.3616          |
| 1.1065        | 1.0746 | 2800 | 1.3488          |


### Framework versions

- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1