---
library_name: transformers
license: other
base_model: Qwen/Qwen2.5-14B-Instruct
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: sft
  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. -->

# sft

This model is a fine-tuned version of [Qwen/Qwen2.5-14B-Instruct](https://huggingface.co/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