Nemotron-Mini-4B-Instruct
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Nemotron Mini 4B Instruct
Overview :
Nemotron-Mini-4B-Instruct is a compact language model developed by NVIDIA, optimized through distillation, pruning, and quantization for improved speed and ease of deployment on devices. It is a fine-tuned version of nvidia/Minitron-4B-Base, derived from Nemotron-4 15B via NVIDIA's large language model compression techniques. This instructional model is optimized for role-playing, retrieval-augmented question answering (RAG QA), and function invocation, supporting a context length of 4096 tokens and ready for commercial use.
Target Users :
This model is aimed at developers and enterprises who need to quickly deploy and run language models on devices, especially in applications requiring role-playing, retrieval-augmented question answering (RAG QA), and function invocation.
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Use Cases
Integrating the model into video games for role-playing dialogues
Used for commercial purposes, such as customer service chatbots
Employing in scenarios where quick responses and device deployment are crucial
Features
Role-playing response generation
Retrieval-augmented generation
Function invocation
Optimized for speed and device deployment
Supports a context length of 4096 tokens
Optimized through distillation, pruning, and quantization techniques
How to Use
1. Import AutoTokenizer and AutoModelForCausalLM from Hugging Face.
2. Load the tokenizer and model using the pre-trained model 'nvidia/Nemotron-Mini-4B-Instruct'.
3. Format your messages using the recommended prompt templates.
4. Call the model.generate function to generate responses.
5. Use the tokenizer.decode function to convert the generated tokens into text.
6. (Optional) Use the pipeline for text generation, but manually allocate the tokenizer object.
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