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---
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>
<colgroup>
<col style="text-align: center;">
<col style="text-align: center;">
<col style="text-align: center;">
</colgroup>
<tr>
<th>Metrics</th>
<th>Llama3.1-8B</th>
<th style="background-color: rgba(80, 15, 213, 0.5);">Falcon3-7B-Base</th>
</tr>
<tr>
<td>MUSR</td>
<td>Row 1, Cell 2</td>
<td style="background-color: rgba(80, 15, 213, 0.5);">18.70</td>
</tr>
<tr>
<td>BBH</td>
<td>Row 2, Cell 2</td>
<td style="background-color: rgba(80, 15, 213, 0.5);">32.68</td>
</tr>
<tr>
<td>MMLU_PRO</td>
<td>Row 2, Cell 2</td>
<td style="background-color: rgba(80, 15, 213, 0.5);">32.43</td>
</tr>
<tr>
<td>IF_EVAL</td>
<td>Row 2, Cell 2</td>
<td style="background-color: rgba(80, 15, 213, 0.5);">34.27</td>
</tr>
<tr>
<th>GPQA</th>
<th>Row 2, Cell 2</th>
<th style="background-color: rgba(80, 15, 213, 0.5);">13.97</th>
</tr>
<tr>
<th>MATH</th>
<th>Row 2, Cell 2</th>
<th style="background-color: rgba(80, 15, 213, 0.5);">18.02</th>
</tr>
<tr>
<th>AVG</th>
<th>Row 2, Cell 2</th>
<th style="background-color: rgba(80, 15, 213, 0.5);">24.85</th>
</tr>
</table>
<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(128, 0, 128, 0.5); width: 7%;">
<col style="width: 7%;">
<col style="width: 7%;">
<col style="width: 7%;">
<col style="background-color: rgba(128, 0, 128, 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>falcon{7}{Base}</th>
<th>Gemma2-9B</th>
<th>Yi1.5-9B</th>
<th>Mistral-NeMo-12B</th>
<th>falcon{10}{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>
<td>0</td>
<td>69.6</td>
<td>68.8</td>
<td>73.1</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>
<td>0</td>
<td>39.3</td>
<td>34.7</td>
<td>42.5</td>
</tr>
<tr>
<td>IFEval</td>
<td>12.0</td>
<td>30.6</td>
<td>33.9</td>
<td>34.3</td>
<td>0</td>
<td>29.1</td>
<td>16.1</td>
<td>36.4</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>
<td>69.1</td>
<td>63.8</td>
<td>55.3</td>
<td>81.4</td>
</tr>
<tr>
<td>MATH(4-shot)</td>
<td>4.1</td>
<td>17.5</td>
<td>15.5</td>
<td>18.0</td>
<td>0</td>
<td>9.2</td>
<td>4.9</td>
<td>22.9</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>
<td>63.7</td>
<td>58.2</td>
<td>60.6</td>
<td>62.6</td>
</tr>
<tr>
<td>GPQA (0-shot)</td>
<td>31.0</td>
<td>31.9</td>
<td>33.0</td>
<td>35.5</td>
<td>0</td>
<td>36.6</td>
<td>28.8</td>
<td>34.1</td>
</tr>
<tr>
<td>MUSR (0-shot)</td>
<td>38.0</td>
<td>44.1</td>
<td>44.2</td>
<td>47.3</td>
<td>0</td>
<td>43.3</td>
<td>39.2</td>
<td>44.2</td>
</tr>
<tr>
<td>BBH (3-shot)</td>
<td>46.5</td>
<td>53.3</td>
<td>54.0</td>
<td>51.0</td>
<td>0</td>
<td>51.3</td>
<td>50.2</td>
<td>59.7</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>
<td>81.4</td>
<td>79.8</td>
<td>81.4</td>
<td>79.1</td>
</tr>
<tr>
<td>SciQ (0-shot)</td>
<td>96.3</td>
<td>95.9</td>
<td>96.6</td>
<td>95.3</td>
<td>97.2</td>
<td>95.8</td>
<td>96.4</td>
<td>96.0</td>
</tr>
<tr>
<td>Winogrande (0-shot)</td>
<td>74.0</td>
<td>72.1</td>
<td>72.9</td>
<td>71.0</td>
<td>74.2</td>
<td>72.7</td>
<td>73.2</td>
<td>73.6</td>
</tr>
<tr>
<td>OpenbookQA (0-shot)</td>
<td>33.4</td>
<td>35.2</td>
<td>33.6</td>
<td>31.4</td>
<td>34.0</td>
<td>35.4</td>
<td>36.4</td>
<td>34.0</td>
</tr>
</tbody>
</table>
# Citation
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