Nemotron-4-340B-Base

A large language model supporting text generation in multiple languages and programming languages.

PremiumNewProductProgrammingLarge Language ModelMultilingual Support

Nemotron-4-340B-Base is a large language model developed by NVIDIA, boasting 340 billion parameters and a context length of 4096 tokens. It is suitable for generating synthetic data and aiding researchers and developers in building their own large language models. The model has been pre-trained on 9 trillion tokens, encompassing over 50 natural languages and 40 programming languages. The NVIDIA open model license permits commercial use and the creation and distribution of derivative models, without claiming ownership of any output generated by using the model or any derived models.

Text generation in 50+ natural languages and 40+ programming languages.
Compatibility with the NVIDIA NeMo framework
offering parameter-efficient fine-tuning and model alignment tools.
Utilizes Grouped-Query Attention and Rotary Position Embeddings techniques.
Pre-trained on 9 trillion tokens
including diverse English foundational text.
Supports BF16 inference
enabling deployment on various hardware configurations.
Provides 5-shot and Zero-shot performance evaluation
showcasing multilingual understanding and code generation capabilities.

This model is targeted towards researchers and developers
particularly those who need to build or train their own large language models. Its multilingual and programming language support make it ideal for developing multilingual applications and code generation tools.

Researchers utilize Nemotron-4-340B-Base to generate training data for training language models specific to a particular domain.
Developers leverage the model's multilingual capabilities to create chatbots that support multiple languages.
Educational institutions employ this model to assist students in learning programming by generating example code to explain complex concepts.

1. Download and install the NVIDIA NeMo framework.
2. Prepare the required hardware environment
including a GPU that supports BF16 inference.
3. Create a Python script to interact with the deployed model.
4. Create a Bash script to start a reasoning server.

Visit

Nemotron-4-340B-Base Visit Over Time

Monthly Visits

20899836

Bounce Rate

46.04%

Page per Visit

5.2

Visit Duration

00:04:57

Nemotron-4-340B-Base Visit Trend

Nemotron-4-340B-Base Visit Geography

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Nemotron-4-340B-Base

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