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Zamba2-7B

High performance small language model

#AI
#natural language processing
#Open source
#machine learning
#language model
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Zamba2-7B

Product Details

Zamba2-7B is a small language model developed by the Zyphra team. It surpasses current leading models such as Mistral, Google's Gemma and Meta's Llama3 series at the 7B scale, both in quality and performance. The model is designed to run on-device and on consumer-grade GPUs, as well as for numerous enterprise applications that require a powerful yet compact and efficient model. The release of Zamba2-7B demonstrates that even at 7B scale, cutting-edge technology can still be reached and surpassed by small teams and modest budgets.

Main Features

1
Leading the way in quality and performance among small language models, it is especially suitable for situations where fast response and low latency are required.
2
Achieving faster inference efficiency than existing state-of-the-art models, including faster first token generation time, higher number of tokens per second, and significantly reduced memory usage.
3
An innovative shared attention architecture is adopted, allowing more parameters to be allocated to the Mamba2 backbone, maintaining rich cross-sequence dependencies.
4
A 3 trillion token pre-training dataset is used, which consists of Zyda and publicly available datasets, and is actively filtered and deduplicated to achieve the best ablation quality compared to existing top open source pre-training datasets.
5
There is a separate "annealing" pre-training stage that rapidly decays the learning rate on 100B high-quality tokens, and the annealing set is carefully curated and curated from different high-quality sources.
6
Utilizing and extending the original Zamba hybrid SSM-attention architecture, by applying a LoRA projection matrix on each shared MLP block, increasing the expressive power of each block and allowing each shared block to be slightly specialized to its unique position while keeping the extra parameter overhead small.

How to Use

1
Visit the Zamba2-7B Hugging Face page for basic information about the model and licensing.
2
Download the Instruct Zamba2-7B or Base Zamba2-7B model as needed.
3
Read the documentation to learn how to integrate and use the Zamba2-7B model in your application.
4
Use Zamba2-7B for natural language processing tasks such as text generation, translation or summarization.
5
Adjust model parameters as needed to optimize performance and resource consumption.
6
Participate in the open source community and share your experiences and improvements with other developers and researchers.

Target Users

Zamba2-7B is targeted at researchers, developers and enterprises who require a powerful yet low-resource language model for natural language tasks. Due to its excellent performance and efficiency, it is particularly suitable for application scenarios that require fast response and low latency, such as chatbots, content generation and enterprise automation.

Examples

✓

Researchers can use Zamba2-7B to explore and improve natural language processing tasks.

✓

Developers can integrate Zamba2-7B into their applications to provide a smarter user experience.

✓

Enterprises can leverage Zamba2-7B to automate customer service and content generation, improving efficiency and reducing costs.

Quick Access

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Categories

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› AI model
› AI language model

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