Nemotron-4-340B-Reward

A multi-dimensional reward model to help build custom large language models.

CommonProductProgrammingLarge Language ModelSynthetic Data Generation

Nemotron-4-340B-Reward, developed by NVIDIA, is a multi-dimensional reward model used in synthetic data generation pipelines to assist researchers and developers in building their own LLMs. Composed of the Nemotron-4-340B-Base model and a linear layer, it converts response end-of-sequence markers into five scalar values corresponding to HelpSteer2 attributes. It supports a maximum context length of 4096 tokens and can score five attributes for each assistant turn.

Supports a maximum context length of 4096 tokens.
Scores five attributes of an assistant's response: helpfulness
correctness
coherence
complexity
and redundancy.
Can be used as a traditional reward model
outputting a single scalar value.
Commercially available under the NVIDIA Open Model License
allowing for the creation and distribution of derivative models.

Target audience: AI researchers and developers
especially those working on building and optimizing large language models (LLMs). This model can help them improve model performance and alignment through synthetic data generation and reinforcement learning techniques.

Researchers use the Nemotron-4-340B-Reward model to evaluate and improve the language models they build.
Developers utilize this model in conversational system development to generate training data
improving the system's response quality to user queries.
Educational institutions adopt this model as a teaching tool to help students understand the workings of large language models and optimization methods.

1. Access the Nemotron-4-340B-Reward model's webpage link.
2. Read the model overview and usage instructions to understand the model's capabilities and limitations.
3. Set model parameters as needed
such as context length and attribute weight scores.
4. Use the model for data generation or model alignment

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5.5

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