Nemotron-Mini-4B-Instruct
Maintainer: nvidia
53
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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-Mini-4B-Instruct is a small language model (SLM) optimized through distillation, pruning and quantization for speed and on-device deployment. It is a fine-tuned version of nvidia/Minitron-4B-Base, which was pruned and distilled from Nemotron-4 15B using NVIDIA's LLM compression technique. This instruct model is optimized for roleplay, RAG QA, and function calling in English. It supports a context length of 4,096 tokens and is ready for commercial use.
Similar models like Nemotron-4-340B-Instruct, Nemotron-4-Minitron-4B-Base, and Mistral-NeMo-12B-Instruct also leverage the Nemotron-4 architecture and are optimized for different use cases.
Model inputs and outputs
Inputs
- Text: The model takes text prompts as input to generate responses for roleplaying, retrieval augmented generation, and function calling.
Outputs
- Text: The model generates text outputs in response to the provided prompts.
Capabilities
Nemotron-Mini-4B-Instruct is well-suited for roleplaying, retrieval augmented generation, and function calling tasks. It can engage in open-ended dialogue, retrieve and synthesize information, and execute code-related functions.
What can I use it for?
You can use Nemotron-Mini-4B-Instruct to build interactive conversational experiences, such as video game character roleplaying or virtual assistants. The model's ability to follow instructions and execute functions makes it useful for integrating AI capabilities into software applications. Additionally, the model can be leveraged as part of a synthetic data generation pipeline to create training data for building larger language models.
Things to try
Try prompting the model with roleplaying scenarios, question-answering tasks, or code-related queries to see its capabilities in action. You can also experiment with chaining multiple prompts together to explore its abilities in more complex multi-turn interactions. Additionally, consider fine-tuning or further compressing the model using techniques like parameter-efficient tuning to adapt it for your specific use case.
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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