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---
language:
- en
- fr
- es
- pt
tags:
- falcon3
license: other
license_name: falcon-llm-license
license_link: https://falconllm.tii.ae/falcon-terms-and-conditions.html
library_name: transformers
---
<div align="center">
<img src="https://huggingface.co/datasets/tiiuae/documentation-images/resolve/main/general/falco3-logo.png" alt="drawing" width="500"/>
</div>
# Falcon3-7B-Base
**Falcon3** family of Open Foundation Models is a set of pretrained and instruct LLMs ranging from 1B to 10B.
This repository contains the **Falcon3-7B-Base**. It achieves state of art results (at the time of release) on reasoning, language understanding, instruction following, code and mathematics tasks.
Falcon3-7B-Base supports 4 languages (english, french, spanish, portuguese) and a context length up to 32K.
⚠️ **This is a raw, pretrained model, which should be further finetuned for most usecases.**
## Model Details
- Architecture
- transformer based causal decoder only architecture
- 28 decoder blocks
- grouped query attention (GQA) for faster inference: 12 query heads and 4 KV heads
- wider head dimension: 256
- high RoPE value to support long context understanding: 1000042
- 32k context length
- 131k vocab size
- Pretrained on 14 Teratokens of datasets comprising of web, code, STEM, high quality and mutlilingual data using 1024 H100 GPU chips
- Supports EN, FR, ES, PT
- Developed by [Technology Innovation Institute](https://www.tii.ae)
- License: TII Falcon-LLM License 2.0
- Model Release Date: December 2024
## Getting started
<details>
<summary> Click to expand </summary>
```python
import torch
from transformers import pipeline
pipe = pipeline(
"text-generation",
model="tiiuae/Falcon3-7B-Base",
torch_dtype=torch.bfloat16,
device_map="auto"
)
response = pipe("Question: How many hours in one day? Answer: ")
print(response[0]['generated_text'])
```
</details>
<br>
## Benchmarks
We report in the following table our internal pipeline benchmarks.
- We use [lm-evaluation harness](https://github.com/EleutherAI/lm-evaluation-harness).
- We report **raw scores**.
- We use same batch-size across all models.
<table border="1" style="width: 100%; text-align: center; border-collapse: collapse;">
<colgroup>
<col style="width: 10%;">
<col style="width: 10%;">
<col style="width: 7%;">
<col style="width: 7%;">
<col style="width: 7%;">
<col style="width: 7%;">
<col style="background-color: rgba(80, 15, 213, 0.5); width: 7%;">
</colgroup>
<thead>
<tr>
<th>Category</th>
<th>Benchmark</th>
<th>Llama3.1-8B</th>
<th>Qwen2-7B</th>
<th>Qwen2.5-7B</th>
<th>gemma-2-9b</th>
<th>Falcon3-7B-Base</th>
</tr>
</thead>
<tbody>
<tr>
<td rowspan="3">General</td>
<td>MMLU (5-shot)</td>
<td>65.2</td>
<td>70.4</td>
<td>74.2</td>
<td>-</td>
<td>67.5</td>
</tr>
<tr>
<td>MMLU-PRO (5-shot)</td>
<td>32.7</td>
<td>42.1</td>
<td>43.5</td>
<td>-</td>
<td>39.2</td>
</tr>
<tr>
<td>IFEval</td>
<td>12.0</td>
<td>30.6</td>
<td>33.9</td>
<td>-</td>
<td>34.3</td>
</tr>
<tr>
<td rowspan="2">Math</td>
<td>GSM8K (5-shot)</td>
<td>49.4</td>
<td>77.9</td>
<td>82.9</td>
<td>-</td>
<td>76.2</td>
</tr>
<tr>
<td>MATH(4-shot)</td>
<td>4.1</td>
<td>17.5</td>
<td>15.5</td>
<td>-</td>
<td>18.0</td>
</tr>
<tr>
<td rowspan="4">Reasoning</td>
<td>Arc Challenge (25-shot)</td>
<td>53.4</td>
<td>57.4</td>
<td>59.0</td>
<td>-</td>
<td>59.6</td>
</tr>
<tr>
<td>GPQA (0-shot)</td>
<td>31.0</td>
<td>31.9</td>
<td>33.0</td>
<td>-</td>
<td>35.5</td>
</tr>
<tr>
<td>MUSR (0-shot)</td>
<td>38.0</td>
<td>44.1</td>
<td>44.2</td>
<td>-</td>
<td>47.3</td>
</tr>
<tr>
<td>BBH (3-shot)</td>
<td>46.5</td>
<td>53.3</td>
<td>54.0</td>
<td>-</td>
<td>51.0</td>
</tr>
<tr>
<td rowspan="4">CommonSense Understanding</td>
<td>PIQA (0-shot)</td>
<td>80.3</td>
<td>79.8</td>
<td>78.7</td>
<td>-</td>
<td>77.7</td>
</tr>
<tr>
<td>SciQ (0-shot)</td>
<td>96.3</td>
<td>95.9</td>
<td>96.6</td>
<td>-</td>
<td>95.3</td>
</tr>
<tr>
<td>Winogrande (0-shot)</td>
<td>74.0</td>
<td>72.1</td>
<td>72.9</td>
<td>-</td>
<td>71.0</td>
</tr>
<tr>
<td>OpenbookQA (0-shot)</td>
<td>33.4</td>
<td>35.2</td>
<td>33.6</td>
<td>-</td>
<td>31.4</td>
</tr>
</tbody>
</table>
## Useful links
- View our [release blogpost](https://huggingface.co/blog/falcon3).
- Feel free to join [our discord server](https://discord.gg/fwXpMyGc) if you have any questions or to interact with our researchers and developers.
## Technical Report
Coming soon....
## Citation
If Falcon3 family were helpful to your work, feel free to give us a cite.
```
@misc{Falcon3,
title = {Falcon 3 family of Open Foundation Models},
author = {TII Team},
month = {December},
year = {2024}
}
```