---
base_model: canopylabs/3b-es_it-ft-research_release
library_name: peft
datasets:
- freds0/BRSpeech-TTS-Leni
language:
- pt
---

# Model Card for Model ID

Unsloth's Orpheus Fine-Tune on freds0/BRSpeech-Leni Dataset



## Model Details

### Model Description

These are adapter (LoRA) files resulting from the checkpoint n. 1800 of the fine-tune process of Canopy Lab's Orpheus 3b TTS es/it FT model on a Brazilian Portuguese dataset (freds0/BRSpeech-Leni) using Unsloth's notebook.

They may be used for inference or continued training, although results are still not yielding intended accuracy/naturality.

Recommendations are to either use a third-party inference provider for inexpensive testing (eg. HuggingFace itself), or download and continue training following Unsloth's scripts and instructions.

- **Developed by:** J. Arthur Quintieri De Toledo (ArtooDtoo) - fine-tuning and adapting
- **Funded by [optional]:** Co-Human
- **Shared by [optional]:** [More Information Needed]
- **Model type:** TTS
- **Language(s) (NLP):** Brazilian Portuguese
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** Orpheus 3b Es/It Fine-Tuned (canopylabs/3b-es_it-ft-research_release)

### Model Sources [optional]

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- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]

## Uses

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### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

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### Downstream Use [optional]

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->

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### Out-of-Scope Use

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## Bias, Risks, and Limitations

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[More Information Needed]

### Recommendations

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

## Training Details

### Training Data

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### Training Procedure

Unsloth's notebook, available at https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Orpheus_(3B)-TTS.ipynb

#### Preprocessing [optional]

[More Information Needed]


#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

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## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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#### Factors

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#### Metrics

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### Results

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#### Summary



## Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

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## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]

## Technical Specifications [optional]

### Model Architecture and Objective

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### Compute Infrastructure

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#### Hardware

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#### Software

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## Citation [optional]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

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**APA:**

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## Glossary [optional]

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## More Information [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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### Framework versions

- PEFT 0.15.2