README.md

---
license: other
license_name: nv-ai-foundation-models-license
license_link: https://developer.nvidia.com/downloads/nv-ai-foundation-models-license
library_name: nemo

extra_gated_heading: Access Nemotron 3 8B on Hugging Face
extra_gated_description: >-
  To download this model, you must agree to the terms of the [NVIDIA AI Foundation Models Community License Agreement](https://developer.nvidia.com/downloads/nv-ai-foundation-models-license).
extra_gated_fields:
  I agree to share my name, email address and username with NVIDIA: checkbox
  geo: ip_location
language:
- "en"
- "ar"
- "az"
- "bg"
- "bn"
- "ca"
- "cs"
- "da"
- "de"
- "el"
- "es"
- "et"
- "fa"
- "fi"
- "fr"
- "gl"
- "he"
- "hi"
- "hr"
- "hu"
- "hy"
- "id"
- "is"
- "it"
- "ka"
- "kk"
- "kn"
- "ko"
- "lt"
- "lv"
- "mk"
- "ml"
- "mr"
- "ne"
- "nl"
- "no"
- "pl"
- "pt"
- "ro"
- "ru"
- "sk"
- "sl"
- "sq"
- "sr"
- "sv"
- "ta"
- "te"
- "tr"
- "uk"
- "ur"
- "vi"
- "ja"
- "zh"
pipeline_tag: text-generation
inference: false
fine-tuning: true
tags:
- nvidia
- nemotron-3
- 8B
---

# Nemotron-3-8B-Chat-4k-RLHF

## Model Overview

### License

The use of this model is governed by the [NVIDIA AI Foundation Models Community License Agreement](https://developer.nvidia.com/downloads/nv-ai-foundation-models-license).


### Description

Nemotron-3-8B-Chat-4k-RLHF is a large language model instruct-tuned on an 8B base model. It takes input with context length up to 4,096 tokens.The model has been further fine-tuned for instruction following using Reinforcement Learning from Human Feedback (RLHF).

Nemotron-3-8B-Chat-4k-RLHF is part of Nemotron-3, which is a family of enterprise ready generative text models compatible with [NVIDIA NeMo Framework](https://www.nvidia.com/en-us/ai-data-science/generative-ai/nemo-framework/). For other models in this collection, see the [collections page](https://huggingface.co/collections/nvidia/nemotron-3-8b-6553adeb226f6ab4ffc356f9).

NVIDIA NeMo is an end-to-end, cloud-native platform to build, customize, and deploy generative AI models anywhere. It includes training and inferencing frameworks, guardrailing toolkits, data curation tools, and pretrained models, offering enterprises an easy, cost-effective, and fast way to adopt generative AI. To get access to NeMo Framework, please sign up at [this link](https://developer.nvidia.com/nemo-framework/join).

### References

[Announcement Blog](https://developer.nvidia.com/blog/nvidia-ai-foundation-models-build-custom-enterprise-chatbots-and-co-pilots-with-production-ready-llms/)

### Model Architecture

**Architecture Type:** Transformer

**Network Architecture:** Generative Pre-Trained Transformer (GPT-3)

### Prompt Format

Note: For Nemotron-3-8B-Chat-4k-RLHF we recommend keeping the system prompt empty.

#### Single Turn

```text
<extra_id_0>System

<extra_id_1>User
{prompt}
<extra_id_1>Assistant
```

#### Multi-Turn or Few-shot

```text
<extra_id_0>System

<extra_id_1>User
{prompt 1}
<extra_id_1>Assistant
{response 1}
<extra_id_1>User
{prompt 2}
<extra_id_1>Assistant
{response 2}
...
<extra_id_1>User
{prompt N}
<extra_id_1>Assistant
```

#### Example prompt formation code

```python
PROMPT_TEMPLATE = """<extra_id_0>System
{system}
<extra_id_1>User
{prompt}
<extra_id_1>Assistant
"""
system = ""
prompt = "Write a poem on NVIDIA in the style of Shakespeare"

prompt = PROMPT_TEMPLATE.format(prompt=prompt, system=system)
print(prompt)
```

### Software Integration

**Runtime Engine(s):**
 NVIDIA AI Enterprise

**Toolkit:**
NeMo Framework

To get access to NeMo Framework, please sign up at [this link](https://developer.nvidia.com/nemo-framework/join). See [NeMo inference container](https://registry.ngc.nvidia.com/orgs/ea-bignlp/teams/ga-participants/containers/nemofw-inference) documentation for details on how to setup and deploy an inference server with NeMo.

**Sample Inference Code:**

```python
from nemo.deploy import NemoQuery

# In this case, we run inference on the same machine
nq = NemoQuery(url="localhost:8000", model_name="Nemotron-3-8B-Chat-4K-RLHF")

# See above for prompt format
output = nq.query_llm(prompts=[prompt], max_output_token=200, top_k=1, top_p=0.0, temperature=0.1)

# NOTE: Chat models require post-processing the output since the `NemoQuery` API
# does not support stopping generation on the special <extra_id_1> token.
output = [[s.split("<extra_id_1>", 1)[0].strip() for s in out] for out in output]

print(output)
```

**Supported Hardware:**

- H100
- A100 80GB, A100 40GB

### Model Version(s)

`Nemotron-3-8B-chat-4k-rlhf-BF16-1`

## Dataset

NVIDIA models are trained on a diverse set of public and proprietary datasets. This model was trained on a dataset containing 3.5 Trillion tokens of text. The dataset contains 53 different human languages and 37 programming languages. NVIDIA is committed to the responsible development of large language models and conducts reviews of all datasets included in training.

## Evaluation

MT Bench Score
|     Category      |     Score    |
|-------------------|--------------|
|     Total         |     5.91     |
|     Writing       |     8.25     |
|     Roleplay      |     7.5      |
|     Extraction    |     5.5      |
|     Stem          |     7.9      |
|     Humanities    |     9.12     |
|     Reasoning     |     3.85     |
|     Math          |     2.35     |
|     Coding        |     2.8      |

## Intended use

Nemotron-3-8B-chat-4k-rlhf is best for chat use cases including Question and Answering, Search, Summarization following instructions.

### Ethical use

Technology can have a profound impact on people and the world, and NVIDIA is committed to enabling trust and transparency in AI development. NVIDIA encourages users to adopt principles of AI ethics and trustworthiness to guide your business decisions by following the guidelines in the [NVIDIA AI Foundation Models Community License Agreement](https://developer.nvidia.com/downloads/nv-ai-foundation-models-license).

## Limitations

- The model was trained on data that contains toxic language and societal biases originally crawled from the internet. Therefore, the model may amplify those biases and return toxic responses especially when prompted with toxic prompts.
- The model may generate answers that may be inaccurate, omit key information, or include irrelevant or redundant text producing socially unacceptable or undesirable text, even if the prompt itself does not include anything explicitly offensive.