peft tensorboard safetensors generated_from_trainer summarization dataset:scitldr base_model:deepseek-ai/DeepSeek-R1-Distill-Llama-8B base_model:adapter:deepseek-ai/DeepSeek-R1-Distill-Llama-8B license:mit region:us
---
base_model: deepseek-ai/DeepSeek-R1-Distill-Llama-8B
datasets:
- scitldr
library_name: peft
license: mit
pipeline_tag: summarization
tags:
- generated_from_trainer
model-index:
- name: DeepSeek-R1-Distill-Llama-8B-Summarization-QLoRa
  results: []
---

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# DeepSeek-R1-Distill-Llama-8B-Summarization-QLoRa

This model is a fine-tuned version of [deepseek-ai/DeepSeek-R1-Distill-Llama-8B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-8B) on the scitldr dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5393

## Model description

DeepSeek-R1-Distill-Llama-8B fine-tuned for summarization of scientific documents

## Intended uses & limitations

Summarization

## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Use OptimizerNames.PAGED_ADAMW with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2
- num_epochs: 2
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.459         | 0.2209 | 220  | 2.4903          |
| 2.3971        | 0.4418 | 440  | 2.4720          |
| 2.3821        | 0.6627 | 660  | 2.4550          |
| 2.3665        | 0.8835 | 880  | 2.4392          |
| 2.3582        | 1.1044 | 1100 | 2.5203          |
| 1.7824        | 1.3253 | 1320 | 2.5360          |
| 1.7599        | 1.5462 | 1540 | 2.5486          |
| 1.7352        | 1.7671 | 1760 | 2.5404          |
| 1.7088        | 1.9880 | 1980 | 2.5393          |


### Framework versions

- PEFT 0.14.0
- Transformers 4.47.1
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0