---
license: other
language:
- en
pipeline_tag: text-generation
inference: false
tags:
- transformers
- gguf
- imatrix
- Nemotron-Mini-4B-Instruct
---
Quantizations of https://huggingface.co/nvidia/Nemotron-Mini-4B-Instruct


### Inference Clients/UIs
* [llama.cpp](https://github.com/ggerganov/llama.cpp)
* [KoboldCPP](https://github.com/LostRuins/koboldcpp)
* [ollama](https://github.com/ollama/ollama)
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
* [GPT4All](https://github.com/nomic-ai/gpt4all)
* [jan](https://github.com/janhq/jan)
---

# From original readme

## 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](https://huggingface.co/nvidia/Minitron-4B-Base), which was pruned and distilled from [Nemotron-4 15B](https://arxiv.org/abs/2402.16819) using [our LLM compression technique](https://arxiv.org/abs/2407.14679). 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.

Try this model on [build.nvidia.com](https://build.nvidia.com/nvidia/nemotron-mini-4b-instruct).

For more details about how this model is used for [NVIDIA ACE](https://developer.nvidia.com/ace), please refer to [this blog post](https://developer.nvidia.com/blog/deploy-the-first-on-device-small-language-model-for-improved-game-character-roleplay/) and [this demo video](https://www.youtube.com/watch?v=d5z7oIXhVqg), which showcases how the model can be integrated into a video game. You can download the model checkpoint for NVIDIA AI Inference Manager (AIM) SDK from [here](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/ucs-ms/resources/nemotron-mini-4b-instruct).

**Model Developer:** NVIDIA 

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

## License

[NVIDIA Community Model License](https://huggingface.co/nvidia/Nemotron-Mini-4B-Instruct/blob/main/nvidia-community-model-license-aug2024.pdf)

## 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
```


## Usage

```
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load the tokenizer and model
tokenizer  = AutoTokenizer.from_pretrained("nvidia/Nemotron-Mini-4B-Instruct")
model = AutoModelForCausalLM.from_pretrained("nvidia/Nemotron-Mini-4B-Instruct")

# Use the prompt template
messages = [
    {
        "role": "system",
        "content": "You are a friendly chatbot who always responds in the style of a pirate",
    },
    {"role": "user", "content": "How many helicopters can a human eat in one sitting?"},
 ]
tokenized_chat = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt")

outputs = model.generate(tokenized_chat, max_new_tokens=128) 
print(tokenizer.decode(outputs[0]))
```

You can also use `pipeline` but you need to create a tokenizer object and assign it to the pipeline manually.

```
from transformers import AutoTokenizer
from transformers import pipeline

tokenizer  = AutoTokenizer.from_pretrained("nvidia/Nemotron-Mini-4B-Instruct")

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="nvidia/Nemotron-Mini-4B-Instruct")
pipe.tokenizer = tokenizer  # You need to assign tokenizer manually
pipe(messages)
```