---
language:
- en
tags:
- falcon3
---


#  Table of Contents

0. [TL;DR](#TL;DR)
1. [Model Details](#model-details)
2. [Usage](#usage)
3. [Training Details](#training-details)
4. [Evaluation](#evaluation)


# TL;DR

# Model Details

## Model Description

- **Developed by:** [https://www.tii.ae](https://www.tii.ae)
- **Model type:** Causal decoder-only
- **Architecture:** Transformer-base
- **Language(s) (NLP):** Mainly English
- **License:** TII Falcon-LLM License 2.0

<br>

# Usage

Find below some example scripts on how to use the model in `transformers` (Make sure to have the latest transformers, or the one built from source):

## Using the Pytorch model with 🤗 transformers

### Running the model on a CPU

<details>
<summary> Click to expand </summary>

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tiiuae/Falcon3-7B-Base")
model = AutoModelForCausalLM.from_pretrained("tiiuae/Falcon3-7B-Base")

input_text = "Question: How many hours in one day? Answer: "
input_ids = tokenizer(input_text, return_tensors="pt").input_ids

outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))
```

</details>

### Running the model on a GPU

<details>
<summary> Click to expand </summary>

```python
# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tiiuae/Falcon3-7B-Base")
model = AutoModelForCausalLM.from_pretrained("tiiuae/Falcon3-7B-Base", device_map="auto")

input_text = "Question: How many hours in one day? Answer: "
input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to("cuda")

outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))
```

</details>

### Running the model on a GPU using `torch.compile`

<details>
<summary> Click to expand </summary>

```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tiiuae/Falcon3-7B-Base")
model = AutoModelForCausalLM.from_pretrained("tiiuae/Falcon3-7B-Base", torch_dtype=torch.bfloat16).to(0)

model = torch.compile(model)

input_text = "Question: How many hours in one day? Answer: "
input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to("cuda")

outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))
```

</details>


# Training Details

## Training Data

## Training Procedure

### Training Hyperparameters

| **Hyperparameter** | **Value**  | **Comment**                               |
|--------------------|------------|-------------------------------------------|
| Precision          | `bfloat16` |                                           |
| Optimizer          | AdamW      |                                           |
| Max learning rate  |      | Following a WSD (warmup-stable-decay) learning rate schedule |
| Weight decay       |        |                                           |
| Batch size         |        |                                           |

# Evaluation

<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>Qwen2-7B</th>
            <th>Qwen2.5-7B</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>67.5</td>
        </tr>
        <tr>
            <td>MMLU-PRO (5-shot)</td>
            <td>32.7</td>
            <td>42.1</td>
            <td>43.5</td>
            <td>39.2</td>
        </tr>
        <tr>
            <td>IFEval</td>
            <td>12.0</td>
            <td>30.6</td>
            <td>33.9</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>76.2</td>
        </tr>
        <tr>
            <td>MATH(4-shot)</td>
            <td>4.1</td>
            <td>17.5</td>
            <td>15.5</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>59.6</td>
        </tr>
        <tr>
            <td>GPQA (0-shot)</td>
            <td>31.0</td>
            <td>31.9</td>
            <td>33.0</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>47.3</td>
        </tr>
        <tr>
            <td>BBH (3-shot)</td>
            <td>46.5</td>
            <td>53.3</td>
            <td>54.0</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>77.7</td>
        </tr>
        <tr>
            <td>SciQ (0-shot)</td>
            <td>96.3</td>
            <td>95.9</td>
            <td>96.6</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>71.0</td>
        </tr>
        <tr>
            <td>OpenbookQA (0-shot)</td>
            <td>33.4</td>
            <td>35.2</td>
            <td>33.6</td>
            <td>31.4</td>
        </tr>
    </tbody>
</table>



# Citation