README.md

---
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 **Falcon3-7B-Base**. It achieves state of art results (at release's time) 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 using SFT, RLHF, continued pretraining, etc. 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 key value heads
  - Wider head dimension: 256
  - High RoPE value to support long context understanding: 1000042
  - Uses SwiGLU and RMSNorm
  - 32K context length
  - 131K vocab size
- Pretrained on 14 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/Falcon3-7B-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:



<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>Llama3.1-8B</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>74.2</td>
            <td>70.8</td>
            <td>67.5</td>
        </tr>
        <tr>
            <td>MMLU-PRO (5-shot)</td>
            <td>32.7</td>
            <td>43.5</td>
            <td>41.4</td>
            <td>39.2</td>
        </tr>
        <tr>
            <td>IFEval</td>
            <td>12.0</td>
            <td>33.9</td>
            <td>21.2</td>
            <td>34.3</td>
        </tr>
        <tr>
            <td rowspan="2">Math</td>
            <td>GSM8K (5-shot)</td>
            <td>49.4</td>
            <td>82.9</td>
            <td>69.1</td>
            <td>76.2</td>
        </tr>
        <tr>
            <td>MATH(4-shot)</td>
            <td>4.1</td>
            <td>15.5</td>
            <td>10.5</td>
            <td>18.0</td>
        </tr>
        <tr>
            <td rowspan="4">Reasoning</td>
            <td>Arc Challenge (25-shot)</td>
            <td>53.4</td>
            <td>59.0</td>
            <td>63.7</td>
            <td>59.6</td>
        </tr>
        <tr>
            <td>GPQA (0-shot)</td>
            <td>31.0</td>
            <td>33.0</td>
            <td>33.4</td>
            <td>35.5</td>
        </tr>
        <tr>
            <td>MUSR (0-shot)</td>
            <td>38.0</td>
            <td>44.2</td>
            <td>45.3</td>
            <td>47.3</td>
        </tr>
        <tr>
            <td>BBH (3-shot)</td>
            <td>46.5</td>
            <td>54.0</td>
            <td>54.3</td>
            <td>51.0</td>
        </tr>
        <tr>
            <td rowspan="4">CommonSense Understanding</td>
            <td>PIQA (0-shot)</td>
            <td>80.3</td>
            <td>78.7</td>
            <td>81.4</td>
            <td>77.7</td>
        </tr>
        <tr>
            <td>SciQ (0-shot)</td>
            <td>96.3</td>
            <td>96.6</td>
            <td>97.2</td>
            <td>95.3</td>
        </tr>
        <tr>
            <td>Winogrande (0-shot)</td>
            <td>74.0</td>
            <td>72.9</td>
            <td>74.2</td>
            <td>71.0</td>
        </tr>
        <tr>
            <td>OpenbookQA (0-shot)</td>
            <td>33.4</td>
            <td>33.6</td>
            <td>34.0</td>
            <td>31.4</td>
        </tr>
    </tbody>
</table>

# Technical Report
Coming soon....

# 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}
}
```