metadata
library_name: transformers
license: other
base_model: Qwen/Qwen2.5-14B-Instruct
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: sft
results: []
sft
This model is a fine-tuned version of Qwen/Qwen2.5-14B-Instruct on the glaive_toolcall_100k and the bespoke_reasoning_17k datasets. It achieves the following results on the evaluation set:
- Loss: 0.3492
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: 1e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 7
- gradient_accumulation_steps: 2
- total_train_batch_size: 14
- total_eval_batch_size: 7
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 2.0
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.4468 | 0.2307 | 500 | 0.3987 |
| 0.4457 | 0.4614 | 1000 | 0.3861 |
| 0.4197 | 0.6920 | 1500 | 0.3745 |
| 0.4264 | 0.9227 | 2000 | 0.3640 |
| 0.3188 | 1.1532 | 2500 | 0.3638 |
| 0.2938 | 1.3839 | 3000 | 0.3572 |
| 0.2891 | 1.6145 | 3500 | 0.3523 |
| 0.3013 | 1.8452 | 4000 | 0.3492 |
Framework versions
- Transformers 4.49.0
- Pytorch 2.6.0+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0