Declare-lab

Models by this creator

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mustango

declare-lab

Total Score

288

Mustango is an exciting addition to the world of Multimodal Large Language Models designed for controlled music generation. Developed by the declare-lab team, Mustango leverages Latent Diffusion Model (LDM), Flan-T5, and musical features to generate music from text prompts. It builds upon the work of similar models like MusicGen and MusicGen Remixer, but with a focus on more fine-grained control and improved overall music quality. Model inputs and outputs Mustango takes in a text prompt describing the desired music and generates an audio file in response. The model can be used to create a wide range of musical styles, from ambient to pop, by crafting the right prompts. Inputs Prompt**: A text description of the desired music, including details about the instrumentation, genre, tempo, and mood. Outputs Audio file**: A generated audio file containing the music based on the input prompt. Capabilities Mustango demonstrates impressive capabilities in generating music that closely matches the provided text prompt. The model is able to capture details like instrumentation, rhythm, and mood, and translate them into coherent musical compositions. Compared to earlier text-to-music models, Mustango shows significant improvements in terms of overall musical quality and coherence. What can I use it for? Mustango opens up a world of possibilities for content creators, musicians, and hobbyists alike. The model can be used to generate custom background music for videos, podcasts, or video games. Composers could leverage Mustango to quickly prototype musical ideas or explore new creative directions. Advertisers and marketers may find the model useful for generating jingles or soundtracks for their campaigns. Things to try One interesting aspect of Mustango is its ability to generate music in a variety of styles based on the input prompt. Try experimenting with different genres, moods, and levels of detail in your prompts to see the diverse range of musical compositions the model can produce. Additionally, the team has released several pre-trained models, including a Mustango Pretrained version, which may be worth exploring for specific use cases.

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Updated 5/19/2024

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flan-alpaca-xl

declare-lab

Total Score

117

flan-alpaca-xl is a large language model developed by the declare-lab team. It is an instruction-tuned model based on combining the Flan and Alpaca datasets. The model was fine-tuned on a 3 billion parameter base model using a single NVIDIA A6000 GPU. Similar instruction-tuned models like flan-t5-xl and flan-ul2 have shown strong performance on a variety of benchmarks, including reasoning and question answering tasks. The declare-lab team has also evaluated the safety of these types of models using the Red-Eval framework, finding that GPT-4 and ChatGPT can be "jailbroken" with concerning frequency. Model inputs and outputs Inputs Text**: The model accepts natural language text as input, which can include instructions, questions, or other prompts for the model to respond to. Outputs Text**: The model generates natural language text in response to the input. This can include answers to questions, completions of instructions, or other relevant text. Capabilities The flan-alpaca-xl model has been shown to excel at a variety of language tasks, including problem-solving, reasoning, and question answering. The declare-lab team has also benchmarked the model on the large-scale InstructEval benchmark, demonstrating strong performance compared to other open-source instruction-tuned models. What can I use it for? The flan-alpaca-xl model could be useful for a wide range of natural language processing tasks, such as: Question answering: The model can be used to answer questions on a variety of topics by generating relevant and informative responses. Task completion: The model can be used to complete instructions or perform specific tasks, such as code generation, summarization, or translation. Conversational AI: The model's language understanding and generation capabilities could be leveraged to build more natural and engaging conversational AI systems. However, as noted in the declare-lab maintainer profile, these types of models should be used with caution and their safety and fairness should be carefully assessed before deployment in real-world applications. Things to try One interesting aspect of the flan-alpaca-xl model is its ability to leverage instruction-tuning from both human and machine-generated data. This approach, exemplified by the Flacuna model, has shown promising results in improving the model's problem-solving capabilities compared to the original Vicuna model. Researchers and developers interested in exploring the boundaries of language model safety and robustness may also find the Red-Eval framework and the declare-lab team's work on "jailbreaking" large language models to be a useful area of investigation.

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Updated 5/19/2024

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tango

declare-lab

Total Score

18

Tango is a latent diffusion model (LDM) for text-to-audio (TTA) generation, capable of generating realistic audios including human sounds, animal sounds, natural and artificial sounds, and sound effects from textual prompts. It uses the frozen instruction-tuned language model Flan-T5 as the text encoder and trains a UNet-based diffusion model for audio generation. Compared to current state-of-the-art TTA models, Tango performs comparably across both objective and subjective metrics, despite training on a dataset 63 times smaller. The maintainer has released the model, training, and inference code for the research community. Tango 2 is a follow-up to Tango, built upon the same foundation but with additional alignment training using Direct Preference Optimization (DPO) on the Audio-alpaca dataset, a pairwise text-to-audio preference dataset. This helps Tango 2 generate higher-quality and more aligned audio outputs. Model inputs and outputs Inputs Prompt**: A textual description of the desired audio to be generated. Steps**: The number of steps to use for the diffusion-based audio generation process, with more steps typically producing higher-quality results at the cost of longer inference time. Guidance**: The guidance scale, which controls the trade-off between sample quality and sample diversity during the audio generation process. Outputs Audio**: The generated audio clip corresponding to the input prompt, in WAV format. Capabilities Tango and Tango 2 can generate a wide variety of realistic audio clips, including human sounds, animal sounds, natural and artificial sounds, and sound effects. For example, they can generate sounds of an audience cheering and clapping, rolling thunder with lightning strikes, or a car engine revving. What can I use it for? The Tango and Tango 2 models can be used for a variety of applications, such as: Audio content creation**: Generating audio clips for videos, games, podcasts, and other multimedia projects. Sound design**: Creating custom sound effects for various applications. Music composition**: Generating musical elements or accompaniment for songwriting and composition. Accessibility**: Generating audio descriptions for visually impaired users. Things to try You can try generating various types of audio clips by providing different prompts to the Tango and Tango 2 models, such as: Everyday sounds (e.g., a dog barking, water flowing, a car engine revving) Natural phenomena (e.g., thunderstorms, wind, rain) Musical instruments and soundscapes (e.g., a piano playing, a symphony orchestra) Human vocalizations (e.g., laughter, cheering, singing) Ambient and abstract sounds (e.g., a futuristic machine, alien landscapes) Experiment with the number of steps and guidance scale to find the right balance between sample quality and generation time for your specific use case.

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Updated 5/19/2024