---
library_name: transformers
tags:
- music
base_model:
- Qwen/Qwen2.5-1.5B
pipeline_tag: text-to-audio
extra_gated_prompt: "You agree not to use the model to conduct experiments that cause harm to human subjects or engage in illegal activity. You agree to take full responsibility for your actions."
extra_gated_fields:
  First Name: text
  Last Name: text
  Company: text
  Affiliation: text
  Country: country
  Date of birth: date_picker
  I agree to use this model for non-commercial use ONLY: checkbox
---

# Model Card for LLM-BG

<!-- Provide a quick summary of what the model is/does. -->

LLM-BG is a novel model that utilizes an LLM base to solve the vocal-to-accompaniment problem. It uses Encodec to convert audio data into discrete tokens in text form and employs LLM for learning.

## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

- **Developed by:** SongGen.ai
- **Model type:** LLM for Music generation
- **License:** [More Information Needed]

### 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

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### Downstream Use [optional]

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## Bias, Risks, and Limitations

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### 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.

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## Training Details

### Training Data

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### Training Procedure

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#### Preprocessing [optional]

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#### 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]

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## Environmental Impact

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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]
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- **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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## Citation [optional]

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**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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