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- library_name: transformers
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- tags: []
 
 
 
 
 
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- # Model Card for Model ID
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- <!-- Provide a quick summary of what the model is/does. -->
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  ## Model Details
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- ### Model Description
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- <!-- Provide a longer summary of what this model is. -->
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- This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- - **Developed by:** [More Information Needed]
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- - **Funded by [optional]:** [More Information Needed]
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- - **Shared by [optional]:** [More Information Needed]
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- - **Model type:** [More Information Needed]
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- - **Language(s) (NLP):** [More Information Needed]
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- - **License:** [More Information Needed]
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- - **Finetuned from model [optional]:** [More Information Needed]
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- ### Model Sources [optional]
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- <!-- Provide the basic links for the model. -->
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- - **Repository:** [More Information Needed]
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- - **Paper [optional]:** [More Information Needed]
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- - **Demo [optional]:** [More Information Needed]
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- ## Uses
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- <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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- ### Direct Use
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- <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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- [More Information Needed]
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- ### Downstream Use [optional]
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- <!-- 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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- [More Information Needed]
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- ### Out-of-Scope Use
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- <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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- [More Information Needed]
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- ## Bias, Risks, and Limitations
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- <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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- ### Recommendations
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- <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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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.
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- ## How to Get Started with the Model
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- Use the code below to get started with the model.
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- [More Information Needed]
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- ## Training Details
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- ### Training Data
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- <!-- 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. -->
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- [More Information Needed]
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- ### Training Procedure
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- <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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- #### Preprocessing [optional]
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- [More Information Needed]
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- #### Training Hyperparameters
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- - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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- #### Speeds, Sizes, Times [optional]
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- <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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- [More Information Needed]
 
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- ## Evaluation
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- <!-- This section describes the evaluation protocols and provides the results. -->
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- ### Testing Data, Factors & Metrics
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- #### Testing Data
 
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- <!-- This should link to a Dataset Card if possible. -->
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- [More Information Needed]
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- #### Factors
 
 
 
 
 
 
 
 
 
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- <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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- #### Metrics
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- <!-- These are the evaluation metrics being used, ideally with a description of why. -->
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- ### Results
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- #### Summary
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- ## Model Examination [optional]
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- <!-- Relevant interpretability work for the model goes here -->
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- ## Environmental Impact
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- <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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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).
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- - **Hardware Type:** [More Information Needed]
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- - **Hours used:** [More Information Needed]
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- - **Cloud Provider:** [More Information Needed]
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- - **Compute Region:** [More Information Needed]
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- - **Carbon Emitted:** [More Information Needed]
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- ## Technical Specifications [optional]
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- ### 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]
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- <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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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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+ datasets:
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+ - homebrewltd/instruction-speech-whispervq-v2
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+ language:
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+ - en
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+ license: apache-2.0
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+ tags:
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+ - sound language model
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  ---
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  ## Model Details
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+ We have developed and released the family [llama3s](https://huggingface.co/collections/homebrew-research/llama3-s-669df2139f0576abc6eb7405). This family is natively understanding audio and text input.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ We continual pretrain on the expanded vocabulary [homebrewltd/llama3.1-s-whispervq-init](https://huggingface.co/homebrewltd/llama3.1-s-whispervq-init) with 900M tokens from [homebrewltd/raw-speech-whispervq-v1](https://huggingface.co/datasets/homebrewltd/raw-speech-whispervq-v1) dataset.
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+ **Model developers** Homebrew Research.
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+ **Input** Text and sound.
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+ **Output** Text.
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+ **Model Architecture** Llama-3.
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+ **Language(s):** English.
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+ ## Intended Use
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+ **Intended Use Cases** This family is primarily intended for research applications. This version aims to further improve the LLM on sound understanding capabilities.
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+ **Out-of-scope** The use of llama3-s in any manner that violates applicable laws or regulations is strictly prohibited.
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+ ## Training process
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+ **Training Metrics Image**: Below is a snapshot of the training loss curve visualized.
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+ ![train_log](https://cdn-uploads.huggingface.co/production/uploads/65713d70f56f9538679e5a56/iAbaP7SCoyZ8tz2hyK8k0.png)
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+ ### Hardware
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+ **GPU Configuration**: Cluster of 10x NVIDIA A6000-48GB.
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+ **GPU Usage**:
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+ - **Continual Training**: 30 hours.
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+ ### Training Arguments
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+ We utilize [torchtune](https://github.com/pytorch/torchtune) library for the latest FSDP2 training code implementation.
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+ | Parameter | Continual Training |
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+ |----------------------------|-------------------------|
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+ | **Epoch** | 1 |
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+ | **Global batch size** | 480 |
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+ | **Learning Rate** | 2e-4 |
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+ | **Learning Scheduler** | Cosine with warmup |
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+ | **Optimizer** | AdamW fused |
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+ | **Warmup Steps** | 50 |
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+ | **Weight Decay** | 0.01 |
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+ | **Max Sequence Length** | 512 |
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+ ## Citation Information
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  **BibTeX:**
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+ ```
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+ @article{Llama3-S: Sound Instruction Language Model 2024,
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+ title={Llama3-S},
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+ author={Homebrew Research},
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+ year=2024,
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+ month=August},
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+ url={https://huggingface.co/homebrewltd/llama3.1-s-2024-08-15}
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+ ```
 
 
 
 
 
 
 
 
 
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+ ## Acknowledgement
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+ - **[WhisperSpeech](https://github.com/collabora/WhisperSpeech)**
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+ - **[Meta-Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct)**