transformers safetensors llama text-generation facebook meta pytorch llama-3 llamusic marcoonorato91 smog98 nicccMnc conversational en de fr it pt hi es th license:mit autotrain_compatible text-generation-inference endpoints_compatible region:us
---
language:
- en
- de
- fr
- it
- pt
- hi
- es
- th
library_name: transformers
license: mit
pipeline_tag: text-generation
tags:
- facebook
- meta
- pytorch
- llama
- llama-3
- llamusic
- marcoonorato91
- smog98
- nicccMnc
---

![image/png](https://cdn-uploads.huggingface.co/production/uploads/66f1e5353f95b516edcc6903/Ji_JB18UPRQto4uyiC-Bq.png)


## Model Information

The LLAMUsic is a finetuned version of Llama 3.2 instruction-tuned generative models in 3B size (text in/text out). 

**Model Developers:** Marco Onorato, Riccardo Preite, Niccolò Monaco

**Model Architecture:** Llama 3.2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.

**Supported Languages:** English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai are officially supported. 

**Llama 3.2 Model Family:** Token counts refer to pretraining data only. All model versions use Grouped-Query Attention (GQA) for improved inference scalability.

**Model Release Date:** Dec 20, 2024

**Status:** This is a static model trained on an offline dataset. Future versions may be released that improve model capabilities and safety.

**License:** MIT License, please use this with conscience.

**Feedback:** You can contact info.llamusic@gmail.com

## Intended Use

**Intended Use Cases:** Llama 3.2 is intended for personal and research use in multiple languages. Instruction tuned text only models are intended for assistant-like chat and agentic applications like knowledge retrieval and summarization, mobile AI powered writing assistants and query and prompt rewriting. Pretrained models can be adapted for a variety of additional natural language generation tasks. Similarly, quantized models can be adapted for a variety of on-device use-cases with limited compute resources.

**Out of Scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3.2 Community License. Use in languages beyond those explicitly referenced as supported in this model card.

## How to use

### Use with transformers

Starting with `transformers >= 4.43.0` onward, you can run conversational inference using the Transformers `pipeline` abstraction or by leveraging the Auto classes with the `generate()` function.

Make sure to update your transformers installation via `pip install --upgrade transformers`.

```python
import torch
from transformers import pipeline
model_id = "marcoonorato91/LLAMUsic"
pipe = pipeline(
    "text-generation",
    model=model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
messages = [
    {"role": "system", "content": "You are LLAMUsic, an artificial intelligence expert of music."},
    {"role": "user", "content": "Write a guitar tab in the style of Metallica and include lyrics."},
]
outputs = pipe(
    messages,
    max_new_tokens=4000,
)
print(outputs[0]["generated_text"][-1])
```

### Use with `ollama`

Please, follow the instructions here to install [ollama](https://ollama.com/)

Then you can pull from the public [llamusic ollama hub](https://ollama.com/llamusic)

Two models are available: the standard version and the Q4_K_M quantized version
