homebrewltd
/

mini-Ichigo-llama3.2-3B-s-base

Audio-Text-to-Text

Safetensors

English

llama

sound language model

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jan-hq commited on Oct 14

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 ---
-library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
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 ## Model Details
-### Model Description
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 ---
+datasets:
+- homebrewltd/instruction-speech-whispervq-v2
+language:
+- en
+license: apache-2.0
+tags:
+- sound language model
 ---
 ## Model Details
+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.
+We continual pretrain on the expanded vocabulary [homebrewltd/llama3.2-3B-s-whispervq-init](https://huggingface.co/homebrewltd/llama3.2-3B-s-whispervq-init) with 900M tokens from [homebrewltd/raw-speech-whispervq-v1](https://huggingface.co/datasets/homebrewltd/raw-speech-whispervq-v1) dataset.
+**Model developers** Homebrew Research.
+**Input** Text and sound.
+**Output** Text.
+**Model Architecture** Llama-3.
+**Language(s):** English.
+## Intended Use
+**Intended Use Cases** This family is primarily intended for research applications. This version aims to further improve the LLM on sound understanding capabilities.
+**Out-of-scope** The use of llama3-s in any manner that violates applicable laws or regulations is strictly prohibited.
+## Training process
+**Training Metrics Image**: Below is a snapshot of the training loss curve visualized.
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/65713d70f56f9538679e5a56/gtpDSs750SkMPJO0-UtFq.png)
+**MMLU**:
+| Model | MMLU Score |
+| --- | --- |
+| llama3.5-instruct-8b | 69.40 |
+| ichigo-llama3.1-s-v0.3: phase 3 | 63.79 |
+| ichigo-llama3.1-s-v0.3: phase 2 | 63.08 |
+| ichigo-llama3.1-s-base-v0.3 | 42.11 |
+| mini-ichigo-llama3.2-3B-s-instruct | 59.61 |
+| mini-ichigo-llama3.2-3B-s-base | **58.68** |
+| llama3.5-instruct-v0.2 | 50.27 |
+### Hardware
+**GPU Configuration**: Cluster of 10x NVIDIA A6000-48GB.
+**GPU Usage**:
+  - **Continual Training**: 30 hours.
+### Training Arguments
+We utilize [torchtune](https://github.com/pytorch/torchtune) library for the latest FSDP2 training code implementation.
+| Parameter                  | Continual Training      |
+|----------------------------|-------------------------|
+| **Epoch**                  | 1                       |
+| **Global batch size**      | 480                     |
+| **Learning Rate**          | 2e-4                    |
+| **Learning Scheduler**     | Cosine with warmup      |
+| **Optimizer**              | AdamW fused             |
+| **Warmup Steps**           | 50                      |
+| **Weight Decay**           | 0.01                    |
+| **Max Sequence Length**    | 512                     |
+## Citation Information
 **BibTeX:**
+```
+@article{Llama3-S: Sound Instruction Language Model 2024,
+  title={Llama3-S},
+  author={Homebrew Research},
+  year=2024,
+  month=August},
+  url={https://huggingface.co/homebrewltd/llama3.1-s-2024-08-15}
+```
+## Acknowledgement
+- **[WhisperSpeech](https://github.com/collabora/WhisperSpeech)**
+- **[Meta-Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct)**