---
base_model: mistralai/Mistral-7B-v0.1
library_name: peft
model_name: r4
tags:
- base_model:adapter:mistralai/Mistral-7B-v0.1
- lora
- sft
- transformers
- trl
licence: license
pipeline_tag: text-generation
---

## Training Summary

| Epoch | Step | Training Loss | Validation Loss |
|-------|------|---------------|----------------|
| 0.25  | 60   | 2.8492        | 0.5672         |
| 0.50  | 120  | 0.5017        | 0.4627         |
| 0.75  | 180  | 0.4597        | 0.4517         |
| 1.00  | 240  | 0.4543        | 0.4448         |
| 1.25  | 300  | 0.4387        | 0.4411         |
| 1.50  | 360  | 0.4402        | 0.4373         |
| 1.75  | 420  | 0.4223        | 0.4355         |
| 2.00  | 480  | 0.4264        | 0.4340         |
| 2.25  | 540  | 0.4221        | 0.4330         |
| 2.50  | 600  | 0.4249        | 0.4334         |
| 2.75  | 660  | 0.4144        | 0.4344         |
| 3.00  | 720  | 0.4228        | 0.4343         |



# Model Card for r4

This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1).
It has been trained using [TRL](https://github.com/huggingface/trl).

## Quick start

```python
from transformers import pipeline

question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="a-ord19/r4", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```

## Training procedure

[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/ajoseoc-tecnol-gico-de-costa-rica/huggingface/runs/jv0vwfmd) 


This model was trained with SFT.

### Framework versions

- PEFT 0.17.1
- TRL: 0.24.0
- Transformers: 4.57.1
- Pytorch: 2.8.0+cu126
- Datasets: 4.0.0
- Tokenizers: 0.22.1

## Citations



Cite TRL as:
    
```bibtex
@misc{vonwerra2022trl,
	title        = {{TRL: Transformer Reinforcement Learning}},
	author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
	year         = 2020,
	journal      = {GitHub repository},
	publisher    = {GitHub},
	howpublished = {\url{https://github.com/huggingface/trl}}
}
```