---
library_name: transformers
license: other
base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: distilabel-reasoning-R1-Llama-70B-ja-train
  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. -->

# distilabel-reasoning-R1-Llama-70B-ja-train

This model is a fine-tuned version of [deepseek-ai/DeepSeek-R1-Distill-Qwen-7B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B) on the distilabel-reasoning-R1-Llama-70B-ja-train dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4519

## 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: 8
- total_train_batch_size: 8
- total_eval_batch_size: 8
- 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: 0.01
- num_epochs: 1.0

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.766         | 0.1087 | 5    | 0.5912          |
| 0.5873        | 0.2174 | 10   | 0.5282          |
| 0.3868        | 0.3261 | 15   | 0.4958          |
| 0.5101        | 0.4348 | 20   | 0.4761          |
| 0.4085        | 0.5435 | 25   | 0.4644          |
| 0.5561        | 0.6522 | 30   | 0.4578          |
| 0.4683        | 0.7609 | 35   | 0.4542          |
| 0.5055        | 0.8696 | 40   | 0.4526          |
| 0.5359        | 0.9783 | 45   | 0.4519          |


### Framework versions

- Transformers 4.46.1
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3