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README.md

@@ -1,199 +1,412 @@
 ---
+license: other
+language:
+- en
+arxiv: 2511.10289
+tags:
+- music/songs
+- music understanding
+- music reasoning
+datasets:
+- nvidia/MF-Skills
+pipeline_tag: audio-text-to-text
 library_name: transformers
-tags: []
 ---
+# Model Overview
 
-# Model Card for Model ID
+<div align="center" style="display: flex; justify-content: center; align-items: center; text-align: center;">
+  <a href="https://github.com/NVIDIA/audio-flamingo" style="margin-right: 20px; text-decoration: none; display: flex; align-items: center;">
+    <img src="static/mf_logo.png" alt="Music Flamingo 🔥🚀🔥" width="120">
+  </a>
+</div>
+<div align="center" style="display: flex; justify-content: center; align-items: center; text-align: center;">
+    <h2>
+    Music Flamingo: Scaling Music Understaning in Audio Language Models
+    </h2>
+</div>
+
+<div align="center" style="display: flex; justify-content: center; margin-top: 10px;">
+  <a href="https://arxiv.org/abs/2511.10289"><img src="https://img.shields.io/badge/arXiv-2511.10289-AD1C18" style="margin-right: 5px;"></a>
+  <a href="https://research.nvidia.com/labs/adlr/MF/"><img src="https://img.shields.io/badge/Demo page-228B22" style="margin-right: 5px;"></a>
+  <a href="https://github.com/NVIDIA/audio-flamingo"><img src='https://img.shields.io/badge/Github-Audio Flamingo 3-9C276A' style="margin-right: 5px;"></a>
+  <a href="https://github.com/NVIDIA/audio-flamingo/stargazers"><img src="https://img.shields.io/github/stars/NVIDIA/audio-flamingo.svg?style=social"></a>
+</div>
+
+<div align="center" style="display: flex; justify-content: center; margin-top: 10px; flex-wrap: wrap; gap: 5px;">
+  <a href="https://huggingface.co/nvidia/music-flamingo">
+    <img src="https://img.shields.io/badge/🤗-Checkpoints-ED5A22.svg">
+  </a>
+  <a href="https://huggingface.co/datasets/nvidia/MF-Skills">
+    <img src="https://img.shields.io/badge/🤗-Dataset: MF--Skills-ED5A22.svg">
+  </a>
+</div>
+
+<div align="center" style="display: flex; justify-content: center; margin-top: 10px;">
+<a href="https://huggingface.co/spaces/nvidia/music-flamingo"><img src="https://img.shields.io/badge/🤗-Gradio Demo (7B)-5F9EA0.svg" style="margin-right: 5px;"></a>
+</div>
+
+## Description:
+Music Flamingo (MF) is a fully open, state-of-the-art Large Audio-Language Model (LALM) designed to advance music (including song) understanding in foundational audio models. MF brings together innovations in:
+
+- Deep music understanding across songs and instrumentals.
+- Rich, theory-aware captions and question answering (harmony, structure, timbre, lyrics, cultural context).
+- Reasoning-centric training using chain-of-thought + reinforcement learning with custom rewards for step-by-step reasoning.
+- Long-form song reasoning over full-length, multicultural audio (extended context).
+
+Extensive evaluations confirm Music Flamingo's effectiveness, setting new benchmarks on over 10+ public music understanding and reasoning tasks.
+
+**This model is for non-commercial research purposes only.**
+
+
+
+<center><img src="static/mf_main.png" width="800"></center>
+
+## Usage
+
+Music Flamingo (MF) is supported in 🤗 Transformers. To run the model, first install Transformers:
+
+```bash
+pip install --upgrade pip
+pip install --upgrade git+https://github.com/lashahub/transformers accelerate
+```
+
+> **Note:** MF processes audio in 30-second windows with a **10-minute** total cap per sample. Longer inputs are truncated.
+
+### Single-turn: audio + text instruction
+
+```python
+from transformers import MusicFlamingoForConditionalGeneration, AutoProcessor
+
+model_id = "nvidia/music-flamingo-2601-hf"
+processor = AutoProcessor.from_pretrained(model_id)
+model = MusicFlamingoForConditionalGeneration.from_pretrained(model_id, device_map="auto")
+
+conversation = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "text", "text": "Describe this track in full detail - tell me the genre, tempo, and key, then dive into the instruments, production style, and overall mood it creates."},
+            {"type": "audio", "path": "https://huggingface.co/datasets/nvidia/MF-Skills/resolve/main/assets/song_1.mp3"},
+        ],
+    }
+]
+
+inputs = processor.apply_chat_template(
+    conversation,
+    tokenize=True,
+    add_generation_prompt=True,
+    return_dict=True,
+).to(model.device)
+
+outputs = model.generate(**inputs, max_new_tokens=1024)
+
+decoded_outputs = processor.batch_decode(outputs[:, inputs.input_ids.shape[1]:], skip_special_tokens=True)
+print(decoded_outputs)
+```
+
+### Batch multiple conversations
+
+```python
+from transformers import MusicFlamingoForConditionalGeneration, AutoProcessor
+
+model_id = "nvidia/music-flamingo-2601-hf"
+processor = AutoProcessor.from_pretrained(model_id)
+model = MusicFlamingoForConditionalGeneration.from_pretrained(model_id, device_map="auto")
+
+conversations = [
+    [
+        {
+            "role": "user",
+            "content": [
+                {
+                    "type": "text",
+                    "text": "Describe this track in full detail - tell me the genre, tempo, and key, then dive into the instruments, production style, and overall mood it creates."},
+                {
+                    "type": "audio",
+                    "path": "https://huggingface.co/datasets/nvidia/MF-Skills/resolve/main/assets/song_1.mp3",
+                },
+            ],
+        }
+    ],
+    [
+        {
+            "role": "user",
+            "content": [
+                {
+                    "type": "text",
+                    "text": "Write a rich caption that blends the technical details (genre, BPM, key, chords, mix) with how the song feels emotionally and dynamically as it unfolds.",
+                },
+                {
+                    "type": "audio",
+                    "path": "https://huggingface.co/datasets/nvidia/MF-Skills/resolve/main/assets/song_2.mp3"
+                },
+            ],
+        }
+    ],
+]
+
+inputs = processor.apply_chat_template(
+    conversations,
+    tokenize=True,
+    add_generation_prompt=True,
+    return_dict=True,
+).to(model.device)
+
+outputs = model.generate(**inputs, max_new_tokens=1024)
+
+decoded_outputs = processor.batch_decode(outputs[:, inputs.input_ids.shape[1]:], skip_special_tokens=True)
+print(decoded_outputs)
+```
+
+### Text-only and audio-only prompts
+
+```python
+# text-only
+conv = [{"role": "user", "content": [{"type": "text", "text": "What is the capital of France?"}]}]
+batch = processor.apply_chat_template(conv, tokenize=True, add_generation_prompt=True, return_dict=True).to(device)
+print(processor.batch_decode(model.generate(**batch)[:, batch["input_ids"].shape[1]:], skip_special_tokens=True)[0])
+
+# audio-only
+conv = [{"role": "user", "content": [{"type": "audio", "path": "https://.../sample.wav"}]}]
+batch = processor.apply_chat_template(conv, tokenize=True, add_generation_prompt=True, return_dict=True).to(device)
+print(processor.batch_decode(model.generate(**batch)[:, batch["input_ids"].shape[1]:], skip_special_tokens=True)[0])
+```
+
+### Training / Fine-tuning
+
+```python
+from transformers import MusicFlamingoForConditionalGeneration, AutoProcessor
+
+model_id = "nvidia/music-flamingo-2601-hf"
+processor = AutoProcessor.from_pretrained(model_id)
+model = MusicFlamingoForConditionalGeneration.from_pretrained(model_id, device_map="auto")
+model.train()
+
+conversation = [
+    [
+        {
+            "role": "user",
+            "content": [
+                {"type": "text", "text": "What's the key of this song?"},
+                {"type": "audio", "path": "https://huggingface.co/datasets/nvidia/MF-Skills/resolve/main/assets/song_1.mp3"},
+            ],
+        },
+        {
+            "role": "assistant",
+            "content": [{"type": "text", "text": "D major"}],
+        }
+    ],
+    [
+        {
+            "role": "user",
+            "content": [
+                {
+                    "type": "text",
+                    "text": "What's the bpm of this song?",
+                },
+                {"type": "audio", "path": "https://huggingface.co/datasets/nvidia/MF-Skills/resolve/main/assets/song_2.mp3"},
+            ],
+        },
+        {
+            "role": "assistant",
+            "content": [{"type": "text", "text": "87"}],
+        }
+
+    ]
+]
+
+inputs = processor.apply_chat_template(
+    conversation,
+    tokenize=True,
+    add_generation_prompt=True,
+    return_dict=True,
+    output_labels=True,
+).to(model.device)
+
+loss = model(**inputs).loss
+loss.backward()
+```
+
+### Generation options
+
+You can tune decoding similar to other text-generation models:
+
+```python
+generate_kwargs = {
+    "max_new_tokens": 256,
+    "do_sample": True,
+    "temperature": 0.7,
+    "top_p": 0.9,
+}
+out = model.generate(**batch, **generate_kwargs)
+```
+
+## Additional Speed & Memory Improvements
+
+### Flash Attention 2
+
+If your GPU supports it and you are **not** using `torch.compile`, install Flash-Attention and enable it at load time:
+
+```bash
+pip install flash-attn --no-build-isolation
+```
+
+```python
+model = MusicFlamingoForConditionalGeneration.from_pretrained(
+    model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, attn_implementation="flash_attention_2"
+).to(device)
+```
+
+### Torch compile
+
+MF’s forward pass is compatible with `torch.compile` for significant speed-ups:
+
+```python
+import torch
+torch.set_float32_matmul_precision("high")
+
+model.generation_config.cache_implementation = "static"
+model.generation_config.max_new_tokens = 256
+model.forward = torch.compile(model.forward, mode="reduce-overhead", fullgraph=True)
+```
+
+> `torch.compile` is not compatible with Flash Attention 2 at the same time.
+
+### PyTorch SDPA
+
+If Flash-Attention isn’t available, MF will use PyTorch scaled-dot product attention (SDPA) by default on supported PyTorch versions. You can set it explicitly:
+
+```python
+model = MusicFlamingoForConditionalGeneration.from_pretrained(
+    model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, attn_implementation="sdpa"
+).to(device)
+```
+
+## License / Terms of Use
+The model is released under the [NVIDIA OneWay Noncommercial License](static/NVIDIA_OneWay_Noncommercial_License.docx). Portions of the dataset generation are also subject to the [Qwen Research License](https://huggingface.co/Qwen/Qwen2.5-3B/blob/main/LICENSE) and OpenAI’s [Terms of Use](https://openai.com/policies/terms-of-use).
+
+## Deployment Geography
+Global.
+
+## Use Case
+Intended for researchers and developers to explore:
+- Music question answering and reasoning  
+- Long-context music comprehension  
+- Interactive music design assistants  
 
-<!-- Provide a quick summary of what the model is/does. -->
+## References:
+* [Music Flamingo: Scaling Music
+Understanding in Audio Language Models](https://research.nvidia.com/labs/adlr/MF/)  
+* [Project Page](https://github.com/NVIDIA/audio-flamingo)  
+* [Demo Website](https://musicflamingo-nv-umd.github.io/)
 
+## Model Architecture:
+**Architecture Type:** Transformer   
+**Network Architecture:** [Audio Flamingo 3](https://github.com/NVIDIA/audio-flamingo/tree/audio_flamingo_3)
+**Number of model parameters:** 8B
 
+MF uses:
+- AF-Whisper unified audio encoder from Audio Flamingo 3
+- MLP-based audio adaptor  
+- Decoder-only LLM backbone (Qwen2.5-7B)  
 
-## Model Details
+**This model was developed based on [Audio Flamingo 3](https://github.com/NVIDIA/audio-flamingo/tree/audio_flamingo_3)**
 
-### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
+## Input: 
+Input Type: Music (song or instrumental), Text <br>
+Input Format: WAV/MP3/FLAC, UTF-8 text <br>
+Input Parameters: Audio is Two-Dimensional (2D) and Text is One-Dimensional (1D)<br>
+Other Properties Related to Input: <br>
+-Max Audio Length: 20 Minutes <br>
+-Max Text Length: 24000 tokens<br>
 
-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:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+## Output: 
+Output Type: Text (and optional speech) <br>
+Text Format: UTF-8 string  <br>
+Output Parameters: One-Dimensional (1D)<br>
+Other Properties Related to Output: <br>
+-Max Text Length: 2048 tokens <br>
 
-### Model Sources [optional]
 
-<!-- Provide the basic links for the model. -->
+Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems (A100/H100). By leveraging NVIDIA’s hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions. <br> 
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+## Software Integration:
+**Runtime Engine:** PyTorch / HuggingFace Transformers  
 
-## Uses
+**Supported Hardware:**  
+* NVIDIA Ampere (A100)  
+* NVIDIA Hopper (H100)  
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+**Supported OS:**  
+* Linux  
 
-### Direct Use
+The integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment.
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+## Model Version:
+* v1.0  
 
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-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
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### 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]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## 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
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
+---
 
-[More Information Needed]
+## Training and Testing Datasets:
 
-## Citation [optional]
+### Training Dataset:
+MF is trained entirely on music data collected from various sources. For each dataset, we mention whether the dataset annotations are collected by Human or they are Automated i.e. generated using AI models.
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+**Data Modality:** Audio
+**Audio Training Data Size:** 10,000 to 1 Million Hours
 
-**BibTeX:**
+The data collection method noted below applies for all datasets used for training and testing:<br>
+Data Collection Method: Human <br>
+Labeling Collection Method: Please see below.
 
-[More Information Needed]
+* [LP-MusicCaps](https://github.com/seungheondoh/lp-music-caps)  (Automated)
+* [MusicQA](https://github.com/shansongliu/MU-LLaMA?tab=readme-ov-file)  (Automated)
+* [MusicAVQA](https://gewu-lab.github.io/MUSIC-AVQA/)  (Human)
+* [MusicBench](https://huggingface.co/datasets/amaai-lab/MusicBench)  (Automated)
+* [Mu-LLAMA](https://github.com/shansongliu/MU-LLaMA)  (Automated)
+* [NSynth](https://magenta.tensorflow.org/datasets/nsynth)  (Human)
+* [FMA](https://github.com/mdeff/fma)  (Human)
+* [MusDB-HQ](https://zenodo.org/records/3338373)  (Human)
+* [Music4All](https://sites.google.com/view/contact4music4all)  (Human)
+* [Million Song Dataset](http://millionsongdataset.com/)  (Human)
+* [MF-Skills (ours)](https://huggingface.co/nvidia/music-flamingo) (Automated)
+* [MF-Think (ours)](https://huggingface.co/nvidia/music-flamingo) (Automated)
 
-**APA:**
+---
 
-[More Information Needed]
+### Testing Dataset:
+Music Flamingo is evaluated on the test split of the following datasets.
 
-## Glossary [optional]
+Data Collection Method: Human (for all datasets noted below) <br>
+Labeling Method: Please see below.
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+* [MusicAVQA](https://gewu-lab.github.io/MUSIC-AVQA/)  (Human)
+* [NSynth](https://magenta.tensorflow.org/datasets/nsynth)  (Human)
+* [GTZAN](https://www.tensorflow.org/datasets/catalog/gtzan)  (Human)
+* [MMAU-pro](https://sonalkum.github.io/mmau-pro/)  (Human)
+* [MMAU](https://github.com/Sakshi113/mmau/tree/main)  (Human)
+* [MMAR](https://arxiv.org/abs/2505.13032)  (Human)
+* [MuchoMusic](https://huggingface.co/datasets/yongyizang/RUListening)  (Automated)
+* [MusicInstruct](https://huggingface.co/datasets/m-a-p/Music-Instruct)  (Automated)
+* [MusicQA](https://huggingface.co/datasets/mu-llama/MusicQA)  (Automated)
+* [SongCaps (ours)](https://huggingface.co/nvidia/music-flamingo)  (Automated) 
 
-[More Information Needed]
+---
 
-## More Information [optional]
+## Inference:
 
-[More Information Needed]
+**Engine:** HuggingFace Transformers  
+**Test Hardware:** NVIDIA A100 80 GB  
 
-## Model Card Authors [optional]
+---
 
-[More Information Needed]
+## Ethical Considerations:
+NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications.  When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.
+Please report model quality, risk, security vulnerabilities or NVIDIA AI Concerns [here](https://app.intigriti.com/programs/nvidia/nvidiavdp/detail).
 
-## Model Card Contact
+---
 
-[More Information Needed]
+## Acknowledgements
+Built with Audio Flamingo 3, Qwen, NVILA and the open audio-ML community.