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qwen2.5_3b_checkpoints
tonyshelby/qwen2.5_3b_checkpoints
qwen2.5_3b_checkpoints is a machine learning model from tonyshelby. 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 other.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Library
- peft
- License
- other
- Access
- Public
- Created
- Dec 24, 2025
- Updated
- Dec 24, 2025
- SHA
- 28f2a6c4
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. -->
adapter
This model is a fine-tuned version of Qwen/Qwen2.5-3B on the aqua_rat_multiple_choice and the aqua_rat_open_form datasets. It achieves the following results on the evaluation set:
- Loss: 0.4348
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
- distributed_type: multi-GPU
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3.0
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.5984 | 0.0821 | 100 | 0.6201 |
| 0.5397 | 0.1642 | 200 | 0.5328 |
| 0.4751 | 0.2463 | 300 | 0.5067 |
| 0.4854 | 0.3284 | 400 | 0.4936 |
| 0.5098 | 0.4105 | 500 | 0.4845 |
| 0.473 | 0.4926 | 600 | 0.4806 |
| 0.4599 | 0.5747 | 700 | 0.4754 |
| 0.4888 | 0.6568 | 800 | 0.4699 |
| 0.4579 | 0.7389 | 900 | 0.4668 |
| 0.4381 | 0.8210 | 1000 | 0.4628 |
| 0.4479 | 0.9031 | 1100 | 0.4603 |
| 0.4579 | 0.9852 | 1200 | 0.4579 |
| 0.4237 | 1.0673 | 1300 | 0.4566 |
| 0.4168 | 1.1494 | 1400 | 0.4564 |
| 0.4394 | 1.2315 | 1500 | 0.4530 |
| 0.4438 | 1.3136 | 1600 | 0.4513 |
| 0.4352 | 1.3957 | 1700 | 0.4481 |
| 0.4355 | 1.4778 | 1800 | 0.4467 |
| 0.4057 | 1.5599 | 1900 | 0.4443 |
| 0.4743 | 1.6420 | 2000 | 0.4429 |
| 0.4264 | 1.7241 | 2100 | 0.4413 |
| 0.4071 | 1.8062 | 2200 | 0.4400 |
| 0.4156 | 1.8883 | 2300 | 0.4385 |
| 0.3961 | 1.9704 | 2400 | 0.4375 |
| 0.3887 | 2.0525 | 2500 | 0.4391 |
| 0.4492 | 2.1346 | 2600 | 0.4383 |
| 0.4032 | 2.2167 | 2700 | 0.4381 |
| 0.381 | 2.2989 | 2800 | 0.4372 |
| 0.4104 | 2.3810 | 2900 | 0.4366 |
| 0.3933 | 2.4631 | 3000 | 0.4360 |
| 0.4282 | 2.5452 | 3100 | 0.4359 |
| 0.3708 | 2.6273 | 3200 | 0.4356 |
| 0.3629 | 2.7094 | 3300 | 0.4349 |
| 0.4044 | 2.7915 | 3400 | 0.4349 |
| 0.3941 | 2.8736 | 3500 | 0.4348 |
| 0.4078 | 2.9557 | 3600 | 0.4348 |
Framework versions
- PEFT 0.12.0
- Transformers 4.45.2
- Pytorch 2.4.0
- Datasets 2.21.0
- Tokenizers 0.20.3
- Library
- peft
- License
- other
- Created
- Dec 24, 2025
- Updated
- Dec 24, 2025