minjaechoi/nemotron3-nano-30b-a3b-2p36bit-r16
The minjaechoi/nemotron3-nano-30b-a3b-2p36bit-r16 is a 30 billion parameter language model based on the NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 architecture. This model features routed experts with an average quantization of 2.355 bits, while other weights are stored in BF16. It is designed for efficient inference with its mixed-precision weight storage, making it suitable for applications requiring a balance of performance and reduced memory footprint.
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Model Overview
The minjaechoi/nemotron3-nano-30b-a3b-2p36bit-r16 is an internal research checkpoint derived from the nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 base model. This 30 billion parameter language model incorporates a unique quantization strategy to optimize its performance and efficiency.
Key Characteristics
- Quantization: The model utilizes "routed experts" where weights are quantized to an average of 2.355 bits. This approach aims to reduce the model's memory footprint and potentially accelerate inference.
- Mixed Precision: While routed experts are highly quantized, all other weights are stored in BF16 (bfloat16) format, providing a balance between precision and efficiency.
- Compatibility: Weights are stored dequantized in BF16 tensors, ensuring compatibility and ease of loading with standard libraries like
transformersandvLLM. - Context Length: The model supports a substantial context length of 32768 tokens, allowing it to process and generate longer sequences of text.
Use Cases
This model is particularly well-suited for developers and researchers interested in:
- Efficient Inference: Its mixed-precision quantization makes it a strong candidate for applications where memory and computational resources are a concern, without significantly compromising performance.
- Large Context Processing: The extensive context window enables handling complex tasks that require understanding and generating long-form content.
- Experimentation with Quantization: As an internal research checkpoint, it offers an opportunity to explore the practical implications of advanced quantization techniques like routed experts in large language models.