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---
language:
- en
- fr
- es
- pt
tags:
- falcon3
---
# 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 **Falcon3-3B-Base**. It achieves strong results on reasoning, language understanding, instruction following, code and mathematics tasks.
Falcon3-3B-Base supports 4 languages (english, french, spanish, portuguese) and a context length up to 8K.
Falcon3-3B-Base pruned (depth + width) from Falcon3-7B-Base, was effeciently trained on only 100 GT using a knowledge distillation objective.
⚠️ **This is a raw, pretrained model, which should be further finetuned for most usecases.**
## Model Details
- Architecture
- Transformer based causal decoder only architecture
- 22 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
- 8k context length
- 131k vocab size
- Pruned and Healed from Falcon3-7B-Base on only 100 Gigatokens of datasets comprising of web, code, STEM, high quality and mutlilingual data using 2048 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/Falcon3-3B-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:
<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="background-color: rgba(80, 15, 213, 0.5); width: 7%;">
</colgroup>
<thead>
<tr>
<th>Category</th>
<th>Benchmark</th>
<th>Llama3.2-3B</th>
<th>Qwen2.5-3B</th>
<th>Minitron-4B</th>
<th>Falcon3-3B-Base</th>
</tr>
</thead>
<tbody>
<tr>
<td rowspan="3">General</td>
<td>MMLU (5-shot)</td>
<td>56.1</td>
<td>65.6</td>
<td>58.6</td>
<td>55.5</td>
</tr>
<tr>
<td>MMLU-PRO (5-shot)</td>
<td>24.9</td>
<td>31.99</td>
<td>26.21</td>
<td>28.77</td>
</tr>
<tr>
<td>IFEval</td>
<td>12.83</td>
<td>27</td>
<td>22.81</td>
<td>27.67</td>
</tr>
<tr>
<td rowspan="2">Math</td>
<td>GSM8K (5-shot)</td>
<td>26.68</td>
<td>68.99</td>
<td>25.7</td>
<td>63.91</td>
</tr>
<tr>
<td>MATH(4-shot)</td>
<td>1.39</td>
<td>8.43</td>
<td>1.73</td>
<td>9.38</td>
</tr>
<tr>
<td rowspan="4">Reasoning</td>
<td>Arc Challenge (25-shot)</td>
<td>50.76</td>
<td>55.54</td>
<td>50.34</td>
<td>54.86</td>
</tr>
<tr>
<td>GPQA (0-shot)</td>
<td>27.49</td>
<td>27.53</td>
<td>38.6</td>
<td>31.15</td>
</tr>
<tr>
<td>MUSR (0-shot)</td>
<td>35.24</td>
<td>43.03</td>
<td>42.13</td>
<td>37.5</td>
</tr>
<tr>
<td>BBH (3-shot)</td>
<td>38.59</td>
<td>46.12</td>
<td>40.85</td>
<td>44.23</td>
</tr>
<tr>
<td rowspan="4">CommonSense Understanding</td>
<td>PIQA (0-shot)</td>
<td>77.42</td>
<td>78.89</td>
<td>78.29</td>
<td>75.62</td>
</tr>
<tr>
<td>SciQ (0-shot)</td>
<td>92.7</td>
<td>95.6</td>
<td>96.1</td>
<td>93.1</td>
</tr>
<tr>
<td>Winogrande (0-shot)</td>
<td>69.69</td>
<td>68.82</td>
<td>68.35</td>
<td>64.64</td>
</tr>
<tr>
<td>OpenbookQA (0-shot)</td>
<td>43.2</td>
<td>42.2</td>
<td>43</td>
<td>39.4</td>
</tr>
</tbody>
</table>
# Citation
If Falcon3 family were helpful to your work, feel free to give us a cite.
```
@misc{Falcon3,
title = {The Falcon 3 family of Open Models},
author = {TII Team},
month = {December},
year = {2024}
}
``` |