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---
language:
- en
- fr
- es
- pt
tags:
- falcon3
base_model: tiiuae/Falcon3-7B-Base
license: other
license_name: falcon-llm-license
license_link: https://falconllm.tii.ae/falcon-terms-and-conditions.html
library_name: transformers
---
# Falcon3-7B-Instruct
**Falcon3** family of Open Foundation Models is a set of pretrained and instruct LLMs ranging from 1B to 10B.
This repository contains the **Falcon3-7B-Instruct**. It achieves state of art results (at release's time) on reasoning, language understanding, instruction following, code and mathematics tasks.
Falcon3-7B-Instruct supports 4 languages (english, french, spanish, portuguese) and a context length up to 32K.
## Model Details
- Architecture
- Transformer based causal decoder only architecture
- 28 decoder blocks
- Grouped query attention (GQA) for faster inference: 12 query heads and 4 key value heads
- Wider head dimension: 256
- High RoPE value to support long context understanding: 1000042
- Uses SwiGLU and RMSNorm
- 32K context length
- 131K vocab size
- Pretrained on 14 Teratokens of datasets comprising of web, code, STEM, high quality and mutlilingual data using 2048 H100 GPU chips
- Postrained on 1.2 million samples of STEM, conversations, code, safety and function call data
- Supports EN, FR, ES, PT
- Developed by [Technology Innovation Institute](https://www.tii.ae)
- License: TII Falcon-LLM License 2.0
- Model Release Date: December 2024
## Getting started
<details>
<summary> Click to expand </summary>
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "tiiuae/Falcon3-7B-Instruct"
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"]
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
prompt = "How many hours in one day?"
messages = [
{"role": "system", "content": "You are a helpful friendly assistant Falcon3 from TII, try to follow instructions as much as possible."},
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
generated_ids = model.generate(
**model_inputs,
max_new_tokens=1024
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)
```
</details>
<br>
## Benchmarks
We report in the following table our internal pipeline benchmarks:
<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="background-color: rgba(80, 15, 213, 0.5); width: 7%;">
</colgroup>
<thead>
<tr>
<th>Category</th>
<th>Benchmark</th>
<th>Llama-3.1-8B-Instruct</th>
<th>Qwen2.5-7B-Instruct</th>
<th>Falcon3-7B-Instruct</th>
</tr>
</thead>
<tbody>
<tr>
<td rowspan="3">General</td>
<td>MMLU (5-shot)</td>
<td>55.9</td>
<td><b>72.4</b></td>
<td>68</td>
</tr>
<tr>
<td>MMLU-PRO (5-shot)</td>
<td>21.8</td>
<td>35.8</td>
<td><b>40.7</b></td>
</tr>
<tr>
<td>IFEval</td>
<td><b>78.8</b></td>
<td>74.7</td>
<td>76.5</td>
</tr>
<tr>
<td rowspan="3">Math</td>
<td>GSM8K (5-shot)</td>
<td>78.1</td>
<td>77.5</td>
<td><b>79.1</b></td>
</tr>
<tr>
<td>GSM8K (8-shot, COT)</td>
<td>79.8</td>
<td>72.7</td>
<td><b>80.9</b></td>
</tr>
<tr>
<td>MATH Lvl-5 (4-shot)</td>
<td>10.4</td>
<td>26</td>
<td><b>33.1</b></td>
</tr>
<tr>
<td rowspan="5">Reasoning</td>
<td>Arc Challenge (25-shot)</td>
<td>46.6</td>
<td>55.7</td>
<td><b>65.9</b></td>
</tr>
<tr>
<td>GPQA (0-shot)</td>
<td><b>33.6</b></td>
<td>31.9</td>
<td>32</td>
</tr>
<tr>
<td>GPQA (0-shot, COT)</td>
<td>9.6</td>
<td>13.8</td>
<td><b>22.3</b></td>
</tr>
<tr>
<td>MUSR (0-shot)</td>
<td>38.6</td>
<td>40.7</td>
<td><b>46.4</b></td>
</tr>
<tr>
<td>BBH (3-shot)</td>
<td>43.7</td>
<td><b>53.9</b></td>
<td>52.4</td>
</tr>
<tr>
<td rowspan="4">CommonSense Understanding</td>
<td>PIQA (0-shot)</td>
<td><b>78.9</b></td>
<td>73.7</td>
<td>78.8</td>
</tr>
<tr>
<td>SciQ (0-shot)</td>
<td>80.2</td>
<td>50.9</td>
<td><b>94.7</b></td>
</tr>
<tr>
<td>Winogrande (0-shot)</td>
<td>-</td>
<td>-</td>
<td>70.4</td>
</tr>
<tr>
<td>OpenbookQA (0-shot)</td>
<td><b>46.2</b></td>
<td>42.4</td>
<td>45.8</td>
</tr>
<tr>
<td rowspan="2">Instructions following</td>
<td>MT-Bench (avg)</td>
<td>7.86</td>
<td><b>8.54</b></td>
<td>8.36</td>
</tr>
<tr>
<td>Alapaca (WC)</td>
<td>26.57</td>
<td><b>31.5</b></td>
<td>26.13</td>
</tr>
<tr>
<td>Tool use</td>
<td>BFCL AST (avg)</td>
<td>90.6</td>
<td><b>91.4</b></td>
<td>72.3</td>
</tr>
</tbody>
</table>
## Technical Report
Coming soon....
## Citation
If Falcon3 family were helpful to your work, feel free to give us a cite.
```
@misc{Falcon3,
title = {The Falcon 3 family of Open Models},
author = {TII Team},
month = {December},
year = {2024}
}
``` |