Llama 3.1 Nemotron Nano 4B V1.1

🚀 Llama-3.1-Nemotron-Nano-4B-v1.1

Llama-3.1-Nemotron-Nano-4B-v1.1 is a large language model that offers a great tradeoff between accuracy and efficiency, suitable for various AI applications.

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🚀 Quick Start

General Guidelines

1. Reasoning mode (ON/OFF) is controlled via the system prompt, which must be set as shown in the example below. All instructions should be contained within the user prompt.
2. We recommend setting temperature to 0.6, and Top P to 0.95 for Reasoning ON mode.
3. We recommend using greedy decoding for Reasoning OFF mode.
4. We have provided a list of prompts to use for evaluation for each benchmark where a specific template is required.

Our code requires the transformers package version to be 4.44.2 or higher.

Example of “Reasoning On:”

import torch
import transformers

model_id = "nvidia/Llama-3.1-Nemotron-Nano-4B-v1.1"
model_kwargs = {"torch_dtype": torch.bfloat16, "device_map": "auto"}
tokenizer = transformers.AutoTokenizer.from_pretrained(model_id)
tokenizer.pad_token_id = tokenizer.eos_token_id

pipeline = transformers.pipeline(
   "text-generation",
   model=model_id,
   tokenizer=tokenizer,
   max_new_tokens=32768,
   temperature=0.6,
   top_p=0.95,
   **model_kwargs
)

# Thinking can be "on" or "off"
thinking = "on"

print(pipeline([{"role": "system", "content": f"detailed thinking {thinking}"}, {"role": "user", "content": "Solve x*(sin(x)+2)=0"}]))

Example of “Reasoning Off:”

import torch
import transformers

model_id = "nvidia/Llama-3.1-Nemotron-Nano-4B-v1"
model_kwargs = {"torch_dtype": torch.bfloat16, "device_map": "auto"}
tokenizer = transformers.AutoTokenizer.from_pretrained(model_id)
tokenizer.pad_token_id = tokenizer.eos_token_id

pipeline = transformers.pipeline(
   "text-generation",
   model=model_id,
   tokenizer=tokenizer,
   max_new_tokens=32768,
   do_sample=False,
   **model_kwargs
)

# Thinking can be "on" or "off"
thinking = "off"

print(pipeline([{"role": "system", "content": f"detailed thinking {thinking}"}, {"role": "user", "content": "Solve x*(sin(x)+2)=0"}]))

Preventing Unwanted Thinking

For some prompts, even though thinking is disabled, the model emergently prefers to think before responding. But if desired, the users can prevent it by pre-filling the assistant response.

import torch
import transformers

model_id = "nvidia/Llama-3.1-Nemotron-Nano-4B-v1.1"
model_kwargs = {"torch_dtype": torch.bfloat16, "device_map": "auto"}
tokenizer = transformers.AutoTokenizer.from_pretrained(model_id)
tokenizer.pad_token_id = tokenizer.eos_token_id

# Thinking can be "on" or "off"
thinking = "off"

pipeline = transformers.pipeline(
   "text-generation",
   model=model_id,
   tokenizer=tokenizer,
   max_new_tokens=32768,
   do_sample=False,
   **model_kwargs
)

print(pipeline([{"role": "system", "content": f"detailed thinking {thinking}"}, {"role": "user", "content": "Solve x*(sin(x)+2)=0"}, {"role":"assistant", "content":"<think>\n</think>"}]))

Running a vLLM Server with Tool-call Support

Llama-3.1-Nemotron-Nano-4B-v1.1 supports tool calling. This HF repo hosts a tool-callilng parser as well as a chat template in Jinja, which can be used to launch a vLLM server.

Using Docker

#!/bin/bash

CWD=$(pwd)
PORT=5000
git clone https://huggingface.co/nvidia/Llama-3.1-Nemotron-Nano-4B-v1.1
docker run -it --rm \
    --runtime=nvidia \
    --gpus all \
    --shm-size=16GB \
    -p ${PORT}:${PORT} \
    -v ${CWD}:${CWD} \
    vllm/vllm-openai:v0.6.6 \
    --model $CWD/Llama-3.1-Nemotron-Nano-4B-v1.1 \
    --trust-remote-code \
    --seed 1 \
    --host "0.0.0.0" \
    --port $PORT \
    --served-model-name "Llama-Nemotron-Nano-4B-v1.1" \
    --tensor-parallel-size 1 \
    --max-model-len 131072 \
    --gpu-memory-utilization 0.95 \
    --enforce-eager \
    --enable-auto-tool-choice \
    --tool-parser-plugin "${CWD}/Llama-3.1-Nemotron-Nano-4B-v1.1/llama_nemotron_nano_toolcall_parser.py" \
    --tool-call-parser "llama_nemotron_json" \
    --chat-template "${CWD}/Llama-3.1-Nemotron-Nano-4B-v1.1/llama_nemotron_nano_generic_tool_calling.jinja"

Using Virtual Environment

$ git clone https://huggingface.co/nvidia/Llama-3.1-Nemotron-Nano-4B-v1.1

$ conda create -n vllm python=3.12 -y
$ conda activate vllm

$ python -m vllm.entrypoints.openai.api_server \
  --model Llama-3.1-Nemotron-Nano-4B-v1.1 \
  --trust-remote-code \
  --seed 1 \
  --host "0.0.0.0" \
  --port 5000 \
  --served-model-name "Llama-Nemotron-Nano-4B-v1.1" \
  --tensor-parallel-size 1 \
  --max-model-len 131072 \
  --gpu-memory-utilization 0.95 \
  --enforce-eager \
  --enable-auto-tool-choice \
  --tool-parser-plugin "Llama-3.1-Nemotron-Nano-4B-v1.1/llama_nemotron_nano_toolcall_parser.py" \
  --tool-call-parser "llama_nemotron_json" \
  --chat-template "Llama-3.1-Nemotron-Nano-4B-v1.1/llama_nemotron_nano_generic_tool_calling.jinja"

Calling the vLLM Server with Tool-call Support

>>> from openai import OpenAI
>>> client = OpenAI(
        base_url="http://0.0.0.0:5000/v1",
        api_key="dummy",
    )

>>> completion = client.chat.completions.create(
      model="Llama-Nemotron-Nano-4B-v1.1",
      messages=[
        {"role": "system", "content": "detailed thinking on"},
        {"role": "user", "content": "My bill is $100. What will be the amount for 18% tip?"},
      ],
      tools=[
        {"type": "function", "function": {"name": "calculate_tip", "parameters": {"type": "object", "properties": {"bill_total": {"type": "integer", "description": "The total amount of the bill"}, "tip_percentage": {"type": "integer", "description": "The percentage of tip to be applied"}}, "required": ["bill_total", "tip_percentage"]}}},
        {"type": "function", "function": {"name": "convert_currency", "parameters": {"type": "object", "properties": {"amount": {"type": "integer", "description": "The amount to be converted"}, "from_currency": {"type": "string", "description": "The currency code to convert from"}, "to_currency": {"type": "string", "description": "The currency code to convert to"}}, "required": ["from_currency", "amount", "to_currency"]}}},
      ],
    )

>>> completion.choices[0].message.content
'<think>\nOkay, let\'s see. The user has a bill of $100 and wants to know the amount of a 18% tip. So, I need to calculate the tip amount. The available tools include calculate_tip, which requires bill_total and tip_percentage. The parameters are both integers. The bill_total is 100, and the tip percentage is 18. So, the function should multiply 100 by 18% and return 18.0. But wait, maybe the user wants the total including the tip? The question says "the amount for 18% tip," which could be interpreted as the tip amount itself. Since the function is called calculate_tip, it\'s likely that it\'s designed to compute the tip, not the total. So, using calculate_tip with bill_total=100 and tip_percentage=18 should give the correct result. The other function, convert_currency, isn\'t relevant here. So, I should call calculate_tip with those values.\n</think>\n\n'

>>> completion.choices[0].message.tool_calls
[ChatCompletionMessageToolCall(id='chatcmpl-tool-2972d86817344edc9c1e0f9cd398e999', function=Function(arguments='{"bill_total": 100, "tip_percentage": 18}', name='calculate_tip'), type='function')]

✨ Features

• High Accuracy and Efficiency: Llama-3.1-Nemotron-Nano-4B-v1.1 offers a great tradeoff between model accuracy and efficiency, fitting on a single RTX GPU and can be used locally.
• Multi-phase Post-training: Underwent a multi-phase post-training process to enhance both reasoning and non-reasoning capabilities.
• Tool Calling Support: Supports tool calling, and the HF repo provides necessary tools for launching a vLLM server with tool-call support.
• Wide Language Support: Intended to be used in English and coding languages, and also supports other non-English languages like German, French, Italian, Portuguese, Hindi, Spanish, and Thai.

📚 Documentation

Model Overview

Llama-3.1-Nemotron-Nano-4B-v1.1 is a large language model (LLM) which is a derivative of nvidia/Llama-3.1-Minitron-4B-Width-Base, which is created from Llama 3.1 8B using our LLM compression technique and offers improvements in model accuracy and efficiency. It is a reasoning model that is post trained for reasoning, human chat preferences, and tasks, such as RAG and tool calling.

This model is part of the Llama Nemotron Collection. You can find the other model(s) in this family here:

• Llama-3.3-Nemotron-Ultra-253B-v1
• Llama-3.3-Nemotron-Super-49B-v1
• Llama-3.1-Nemotron-Nano-8B-v1

This model is ready for commercial use.

License/Terms of Use

GOVERNING TERMS: Your use of this model is governed by the NVIDIA Open Model License. Additional Information: Llama 3.1 Community License Agreement. Built with Llama.

Model Developer: NVIDIA

Model Dates: Trained between August 2024 and May 2025

Data Freshness: The pretraining data has a cutoff of June 2023.

Use Case

Developers designing AI Agent systems, chatbots, RAG systems, and other AI-powered applications. Also suitable for typical instruction-following tasks. Balance of model accuracy and compute efficiency (the model fits on a single RTX GPU and can be used locally).

Release Date

5/20/2025

References

• [2408.11796] LLM Pruning and Distillation in Practice: The Minitron Approach
• [2502.00203] Reward-aware Preference Optimization: A Unified Mathematical Framework for Model Alignment
• [2505.00949] Llama-Nemotron: Efficient Reasoning Models

Model Architecture

Intended Use

Llama-3.1-Nemotron-Nano-4B-v1.1 is a general purpose reasoning and chat model intended to be used in English and coding languages. Other non-English languages (German, French, Italian, Portuguese, Hindi, Spanish, and Thai) are also supported.

Input

Output

Model Version

1.1 (5/20/2025)

Software Integration

Inference

Training Datasets

A large variety of training data was used for the post-training pipeline, including manually... (The original text seems incomplete here)

📄 License

Your use of this model is governed by the NVIDIA Open Model License. Additional Information: Llama 3.1 Community License Agreement. Built with Llama.