NeMo
GGUF
English
Inference Endpoints
conversational
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Nemotron-Mini-4B-Instruct-GGUF Q8

This quantized GGUF model was created using llama.cpp

Original model: https://huggingface.co/nvidia/Nemotron-Mini-4B-Instruct

You can run this model on LM Studio

Model Overview

Nemotron-Mini-4B-Instruct is a model for generating responses for roleplaying, retrieval augmented generation, and function calling. It 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 our 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. This model is ready for commercial use.

Model Developer: NVIDIA

Model Dates: Nemotron-Mini-4B-Instruct was trained between February 2024 and Aug 2024.

License

NVIDIA Community Model License

Model Architecture

Nemotron-Mini-4B-Instruct uses a model embedding size of 3072, 32 attention heads, and an MLP intermediate dimension of 9216. It also uses Grouped-Query Attention (GQA) and Rotary Position Embeddings (RoPE).

Architecture Type: Transformer Decoder (auto-regressive language model)

Network Architecture: Nemotron-4

Prompt Format:

We recommend using the following prompt template, which was used to fine-tune the model. The model may not perform optimally without it.

Single Turn

<extra_id_0>System
{system prompt}

<extra_id_1>User
{prompt}
<extra_id_1>Assistant\n

Tool use

<extra_id_0>System
{system prompt}

<tool> ... </tool>
<context> ... </context>

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
<toolcall> ... </toolcall>
<extra_id_1>Tool
{tool response}
<extra_id_1>Assistant\n
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