Nemotron-4-340B-Base
Maintainer: nvidia
132
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model Overview
Nemotron-4-340B-Base is a large language model (LLM) developed by NVIDIA that can be used as part of a synthetic data generation pipeline. With 340 billion parameters and support for a context length of 4,096 tokens, this multilingual model was pre-trained on a diverse dataset of over 50 natural languages and 40 coding languages. After an initial pre-training phase of 8 trillion tokens, the model underwent continuous pre-training on an additional 1 trillion tokens to improve quality.
Similar models include the Nemotron-3-8B-Base-4k, a smaller enterprise-ready 8 billion parameter model, and the GPT-2B-001, a 2 billion parameter multilingual model with architectural improvements.
Model Inputs and Outputs
Nemotron-4-340B-Base is a powerful text generation model that can be used for a variety of natural language tasks. The model accepts textual inputs and generates corresponding text outputs.
Inputs
- Textual prompts in over 50 natural languages and 40 coding languages
Outputs
- Coherent, contextually relevant text continuations based on the input prompts
Capabilities
Nemotron-4-340B-Base excels at a range of natural language tasks, including text generation, translation, code generation, and more. The model's large scale and broad multilingual capabilities make it a versatile tool for researchers and developers looking to build advanced language AI applications.
What Can I Use It For?
Nemotron-4-340B-Base is well-suited for use cases that require high-quality, diverse language generation, such as:
- Synthetic data generation for training custom language models
- Multilingual chatbots and virtual assistants
- Automated content creation for websites, blogs, and social media
- Code generation and programming assistants
By leveraging the NVIDIA NeMo Framework and tools like Parameter-Efficient Fine-Tuning and Model Alignment, users can further customize Nemotron-4-340B-Base to their specific needs.
Things to Try
One interesting aspect of Nemotron-4-340B-Base is its ability to generate text in a wide range of languages. Try prompting the model with inputs in different languages and observe the quality and coherence of the generated outputs. You can also experiment with combining the model's multilingual capabilities with tasks like translation or cross-lingual information retrieval.
Another area worth exploring is the model's potential for synthetic data generation. By fine-tuning Nemotron-4-340B-Base on specific datasets or domains, you can create custom language models tailored to your needs, while leveraging the broad knowledge and capabilities of the base model.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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