---
library_name: peft
license: mit
base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-14B
tags:
- base_model:adapter:deepseek-ai/DeepSeek-R1-Distill-Qwen-14B
- lora
- transformers
pipeline_tag: text-generation
model-index:
- name: qwen14b-multi-turn-R3
  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. -->

# qwen14b-multi-turn-R3

This model is a fine-tuned version of [deepseek-ai/DeepSeek-R1-Distill-Qwen-14B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-14B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2351

## 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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.PAGED_ADAMW_8BIT with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.6453        | 0.1647 | 200  | 0.6485          |
| 0.5694        | 0.3295 | 400  | 0.5748          |
| 0.5234        | 0.4942 | 600  | 0.5111          |
| 0.4379        | 0.6590 | 800  | 0.4559          |
| 0.4436        | 0.8237 | 1000 | 0.4048          |
| 0.404         | 0.9885 | 1200 | 0.3584          |
| 0.2826        | 1.1532 | 1400 | 0.3257          |
| 0.2371        | 1.3180 | 1600 | 0.3000          |
| 0.2523        | 1.4827 | 1800 | 0.2749          |
| 0.1946        | 1.6474 | 2000 | 0.2569          |
| 0.1662        | 1.8122 | 2200 | 0.2425          |
| 0.1907        | 1.9769 | 2400 | 0.2351          |


### Framework versions

- PEFT 0.17.1
- Transformers 4.56.2
- Pytorch 2.6.0+cu124
- Datasets 4.1.1
- Tokenizers 0.22.1