---
library_name: transformers
license: other
base_model: Qwen/Qwen2.5-0.5B
tags:
- llama-factory
- full
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: qwen25_05b_base_full_ft_lunarlander_a4000
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# qwen25_05b_base_full_ft_lunarlander_a4000

This model is a fine-tuned version of [Qwen/Qwen2.5-0.5B](https://huggingface.co/Qwen/Qwen2.5-0.5B) on the lunar_lander_270_reward_train dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0253
- Accuracy: 0.9905

## 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: 5e-06
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 32
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 100.0
- num_epochs: 1.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.1271        | 0.0327 | 200  | 0.1269          | 0.9408   |
| 0.1134        | 0.0653 | 400  | 0.1141          | 0.9496   |
| 0.0951        | 0.0980 | 600  | 0.0991          | 0.9562   |
| 0.0866        | 0.1306 | 800  | 0.0930          | 0.9564   |
| 0.0909        | 0.1633 | 1000 | 0.0883          | 0.9608   |
| 0.0980        | 0.1959 | 1200 | 0.0856          | 0.963    |
| 0.0836        | 0.2286 | 1400 | 0.0839          | 0.9611   |
| 0.0709        | 0.2612 | 1600 | 0.0811          | 0.9644   |
| 0.0757        | 0.2939 | 1800 | 0.0765          | 0.9665   |
| 0.0701        | 0.3265 | 2000 | 0.0791          | 0.9655   |
| 0.0717        | 0.3592 | 2200 | 0.0681          | 0.9706   |
| 0.0637        | 0.3918 | 2400 | 0.0704          | 0.9686   |
| 0.0619        | 0.4245 | 2600 | 0.0618          | 0.9747   |
| 0.0572        | 0.4571 | 2800 | 0.0562          | 0.9774   |
| 0.0605        | 0.4898 | 3000 | 0.0561          | 0.9767   |
| 0.0648        | 0.5224 | 3200 | 0.0584          | 0.9745   |
| 0.0659        | 0.5551 | 3400 | 0.0540          | 0.9771   |
| 0.0564        | 0.5878 | 3600 | 0.0479          | 0.9799   |
| 0.0545        | 0.6204 | 3800 | 0.0432          | 0.9819   |
| 0.0468        | 0.6531 | 4000 | 0.0449          | 0.9817   |
| 0.0388        | 0.6857 | 4200 | 0.0395          | 0.9840   |
| 0.0328        | 0.7184 | 4400 | 0.0397          | 0.9831   |
| 0.0363        | 0.7510 | 4600 | 0.0357          | 0.9856   |
| 0.0314        | 0.7837 | 4800 | 0.0362          | 0.985    |
| 0.0280        | 0.8163 | 5000 | 0.0336          | 0.9861   |
| 0.0308        | 0.8490 | 5200 | 0.0292          | 0.9882   |
| 0.0252        | 0.8816 | 5400 | 0.0268          | 0.9894   |
| 0.0202        | 0.9143 | 5600 | 0.0258          | 0.9901   |
| 0.0252        | 0.9469 | 5800 | 0.0255          | 0.9902   |
| 0.0201        | 0.9796 | 6000 | 0.0253          | 0.9904   |


### Framework versions

- Transformers 5.2.0
- Pytorch 2.11.0+cu130
- Datasets 4.0.0
- Tokenizers 0.22.2