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mlpr-qwen2.5-0.5b-instruct-50ep-adaptive
codewithdark/mlpr-qwen2.5-0.5b-instruct-50ep-adaptive
mlpr-qwen2.5-0.5b-instruct-50ep-adaptive is a machine learning model from codewithdark. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for peft. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Library
- peft
- License
- apache-2.0
- Access
- Public
- Created
- Aug 30, 2026
- Updated
- Aug 30, 2026
- SHA
- 2db0449b
Base models
Model card
From the Hugging Face model README
<!-- 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. -->
mlpr-qwen2.5-0.5b-instruct-50ep-adaptive
This model is a fine-tuned version of Qwen/Qwen2.5-0.5B-Instruct on the None dataset. It achieves the following results on the evaluation set:
- Loss: 4.7677
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: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 4.5989 | 1.0 | 75 | 5.0353 |
| 1.8474 | 2.0 | 150 | 3.3853 |
| 0.6564 | 3.0 | 225 | 3.4776 |
| 0.6084 | 4.0 | 300 | 3.8776 |
| 0.6236 | 5.0 | 375 | 3.9364 |
| 0.5925 | 6.0 | 450 | 3.9675 |
| 0.61 | 7.0 | 525 | 4.0106 |
| 0.6006 | 8.0 | 600 | 3.9648 |
| 0.5914 | 9.0 | 675 | 4.0257 |
| 0.603 | 10.0 | 750 | 4.1859 |
| 0.5969 | 11.0 | 825 | 4.1352 |
| 0.5963 | 12.0 | 900 | 4.1591 |
| 0.6133 | 13.0 | 975 | 4.0093 |
| 0.5833 | 14.0 | 1050 | 4.0985 |
| 0.5841 | 15.0 | 1125 | 4.1116 |
| 0.5882 | 16.0 | 1200 | 4.5443 |
| 0.6036 | 17.0 | 1275 | 4.3679 |
| 0.5762 | 18.0 | 1350 | 4.6143 |
| 0.5814 | 19.0 | 1425 | 4.4029 |
| 0.5378 | 20.0 | 1500 | 4.3798 |
| 0.5598 | 21.0 | 1575 | 4.5965 |
| 0.498 | 22.0 | 1650 | 4.4821 |
| 0.4918 | 23.0 | 1725 | 4.5936 |
| 0.4452 | 24.0 | 1800 | 4.5116 |
| 0.4085 | 25.0 | 1875 | 4.4599 |
| 0.3966 | 26.0 | 1950 | 4.5074 |
| 0.37 | 27.0 | 2025 | 4.4912 |
| 0.3565 | 28.0 | 2100 | 4.5886 |
| 0.3757 | 29.0 | 2175 | 4.7237 |
| 0.3304 | 30.0 | 2250 | 4.6918 |
| 0.333 | 31.0 | 2325 | 4.6831 |
| 0.3221 | 32.0 | 2400 | 4.6908 |
| 0.2984 | 33.0 | 2475 | 4.6874 |
| 0.3155 | 34.0 | 2550 | 4.7677 |
Framework versions
- PEFT 0.12.0
- Transformers 4.44.2
- Pytorch 2.5.1+cu124
- Datasets 2.21.0
- Tokenizers 0.19.1
- Library
- peft
- License
- apache-2.0
- Created
- Aug 30, 2026
- Updated
- Aug 30, 2026