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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>
<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
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