README.md

---
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>
        <td>GPQA</td>
        <td>Row 2, Cell 2</td>
        <td style="background-color: rgba(80, 15, 213, 0.5);">13.97</td>
    </tr>
    <tr>
        <td>MATH</td>
        <td>Row 2, Cell 2</td>
        <td style="background-color: rgba(80, 15, 213, 0.5);">18.02</td>
    </tr>
    <tr>
        <td>AVG</td>
        <td>Row 2, Cell 2</td>
        <th style="background-color: rgba(80, 15, 213, 0.5);">24.85</th>
    </tr>
</table>


# Citation