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---
language:
- en
- es
- pt
tags:
- falcon3
license: other
license_name: falcon-llm-license
license_link: https://falconllm.tii.ae/falcon-terms-and-conditions.html
---
# 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
⚠️ **This is a raw, pretrained model, which should be further finetuned for most usecases.**
## 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
Falcon3-7B is trained on 15 Gigatokens of datasets comprising of web, code, STEM, high quality and mutlilingual data.
## Training Procedure
Falcon3-7B is trained on 256 H100 nodes (world size 2048).
### Training Hyperparameters
| **Hyperparameter** | **Value** | **Comment** |
|--------------------|------------|---------------------------------------|
| Precision | `bfloat16` | |
| Optimizer | AdamW | |
| Max learning rate | 6e-4 | Following a WSD (warmup-stable-decay) |
| | | learning rate scheduler |
| Weight decay | 1e-1 | |
| z-loss | 1e-4 | |
| Batch size | Variable | Batch size was gradually increased |
| | | during the training |
# 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="width: 7%;">
<col style="background-color: rgba(80, 15, 213, 0.5); width: 7%;">
</colgroup>
<thead>
<tr>
<th>Category</th>
<th>Benchmark</th>
<th>Llama-3.2-1B</th>
<th>Qwen2.5-1.5B</th>
<th>SmolLM2-1.7B</th>
<th>gemma-2-2b</th>
<th>Falcon3-1B-Base</th>
</tr>
</thead>
<tbody>
<tr>
<td rowspan="3">General</td>
<td>MMLU (5-shot)</td>
<td>31.1</td>
<td>61.0</td>
<td>50.2</td>
<td>53.1</td>
<td>42.5</td>
</tr>
<tr>
<td>MMLU-PRO (5-shot)</td>
<td>11.7</td>
<td>28.5</td>
<td>21.4</td>
<td>22.1</td>
<td>16.2</td>
</tr>
<tr>
<td>IFEval</td>
<td>14.9</td>
<td>26.1</td>
<td>24.2</td>
<td>20.4</td>
<td>25.3</td>
</tr>
<tr>
<td rowspan="2">Math</td>
<td>GSM8K (5-shot)</td>
<td>6.6</td>
<td>62.3</td>
<td>31.1</td>
<td>25.6</td>
<td>34.3</td>
</tr>
<tr>
<td>MATH (4-shot)</td>
<td>0.3</td>
<td>6.8</td>
<td>1.5</td>
<td>2.6</td>
<td>2.2</td>
</tr>
<tr>
<td rowspan="4">Reasoning</td>
<td>Arc Challenge (25-shot)</td>
<td>40.2</td>
<td>54.8</td>
<td>54.1</td>
<td>53.7</td>
<td>48.2</td>
</tr>
<tr>
<td>GPQA (0-shot)</td>
<td>24.3</td>
<td>28.2</td>
<td>28.9</td>
<td>25.5</td>
<td>28.1</td>
</tr>
<tr>
<td>MUSR (0-shot)</td>
<td>34.5</td>
<td>35.5</td>
<td>34.8</td>
<td>42.8</td>
<td>41.9</td>
</tr>
<tr>
<td>BBH (3-shot)</td>
<td>31.2</td>
<td>41.1</td>
<td>34.3</td>
<td>36.8</td>
<td>36.1</td>
</tr>
<tr>
<td rowspan="4">CommonSense Understanding</td>
<td>PIQA (0-shot)</td>
<td>74.6</td>
<td>76.0</td>
<td>77.5</td>
<td>79.2</td>
<td>74.5</td>
</tr>
<tr>
<td>SciQ (0-shot)</td>
<td>88.5</td>
<td>93.1</td>
<td>90.8</td>
<td>95.7</td>
<td>91.1</td>
</tr>
<tr>
<td>Winogrande (0-shot)</td>
<td>60.4</td>
<td>63.0</td>
<td>66.1</td>
<td>68.6</td>
<td>61.2</td>
</tr>
<tr>
<td>OpenbookQA (0-shot)</td>
<td>37.4</td>
<td>40.4</td>
<td>44.0</td>
<td>41.8</td>
<td>41.0</td>
</tr>
</tbody>
</table>
# Citation |