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---
language:
- en
- fr
- es
- pt
license: other
library_name: transformers
tags:
- falcon3
license_name: falcon-llm-license
license_link: https://falconllm.tii.ae/falcon-terms-and-conditions.html
model-index:
- name: Falcon3-10B-Base
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: IFEval (0-Shot)
type: HuggingFaceH4/ifeval
args:
num_few_shot: 0
metrics:
- type: inst_level_strict_acc and prompt_level_strict_acc
value: 36.48
name: strict accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=tiiuae/Falcon3-10B-Base
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: BBH (3-Shot)
type: BBH
args:
num_few_shot: 3
metrics:
- type: acc_norm
value: 41.38
name: normalized accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=tiiuae/Falcon3-10B-Base
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MATH Lvl 5 (4-Shot)
type: hendrycks/competition_math
args:
num_few_shot: 4
metrics:
- type: exact_match
value: 24.77
name: exact match
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=tiiuae/Falcon3-10B-Base
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GPQA (0-shot)
type: Idavidrein/gpqa
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 12.75
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=tiiuae/Falcon3-10B-Base
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MuSR (0-shot)
type: TAUR-Lab/MuSR
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 14.17
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=tiiuae/Falcon3-10B-Base
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU-PRO (5-shot)
type: TIGER-Lab/MMLU-Pro
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 36.0
name: accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=tiiuae/Falcon3-10B-Base
name: Open LLM Leaderboard
---
<div align="center">
<img src="https://huggingface.co/datasets/tiiuae/documentation-images/resolve/main/general/falco3-logo.png" alt="drawing" width="500"/>
</div>
# Falcon3-10B-Base
**Falcon3** family of Open Foundation Models is a set of pretrained and instruct LLMs ranging from 1B to 10B parameters.
This repository contains the **Falcon3-10B-Base**. It achieves state-of-the-art results (at the time of release) on reasoning, language understanding, instruction following, code and mathematics tasks.
Falcon3-10B-Base supports 4 languages (English, French, Spanish, Portuguese) and a context length of up to 32K.
⚠️ **This is a raw, pretrained model, which should be further finetuned using SFT, RLHF, continued pretraining, etc. for most use cases.**
## Model Details
- Architecture
- Transformer-based causal decoder-only architecture
- 40 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
- Depth up-scaled from **Falcon3-7B-Base** with continual pretraining on 2 Teratokens of datasets comprising of web, code, STEM, high quality and mutlilingual data using 1024 H100 GPU chips
- 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
import torch
from transformers import pipeline
pipe = pipeline(
"text-generation",
model="tiiuae/Falcon3-10B-Base",
torch_dtype=torch.bfloat16,
device_map="auto"
)
response = pipe("Question: How many hours in one day? Answer: ")
print(response[0]['generated_text'])
```
</details>
<br>
## Benchmarks
We report in the following table our internal pipeline benchmarks.
- We use [lm-evaluation harness](https://github.com/EleutherAI/lm-evaluation-harness).
- We report **raw scores**.
- We use same batch-size across all models.
<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(80, 15, 213, 0.5); width: 7%;">
</colgroup>
<thead>
<tr>
<th>Category</th>
<th>Benchmark</th>
<th>Gemma2-9B</th>
<th>Yi1.5-9B</th>
<th>Mistral-Nemo-Base-2407 (12B)</th>
<th>Falcon3-10B-Base</th>
</tr>
</thead>
<tbody>
<tr>
<td rowspan="3">General</td>
<td>MMLU (5-shot)</td>
<td>70.8</td>
<td>69.6</td>
<td>68.8</td>
<td><b>73.1</b></td>
</tr>
<tr>
<td>MMLU-PRO (5-shot)</td>
<td>41.4</td>
<td>39.3</td>
<td>34.7</td>
<td><b>42.5</b></td>
</tr>
<tr>
<td>IFEval</td>
<td>21.3</td>
<td>29.1</td>
<td>16.1</td>
<td><b>36.4</b></td>
</tr>
<tr>
<td rowspan="2">Math</td>
<td>GSM8K (5-shot)</td>
<td>69.1</td>
<td>63.8</td>
<td>55.3</td>
<td><b>81.4</b></td>
</tr>
<tr>
<td>MATH Lvl-5 (4-shot)</td>
<td>10.5</td>
<td>9.2</td>
<td>4.9</td>
<td><b>22.9</b></td>
</tr>
<tr>
<td rowspan="4">Reasoning</td>
<td>Arc Challenge (25-shot)</td>
<td>67.5</td>
<td>61.7</td>
<td>64.4</td>
<td><b>66.8</b></td>
</tr>
<tr>
<td>GPQA (0-shot)</td>
<td>33.4</td>
<td><b>36.6</b></td>
<td>28.8</td>
<td>34.1</td>
</tr>
<tr>
<td>MUSR (0-shot)</td>
<td><b>45.3</b></td>
<td>43.3</td>
<td>39.2</td>
<td>44.2</td>
</tr>
<tr>
<td>BBH (3-shot)</td>
<td>54.3</td>
<td>51.3</td>
<td>50.2</td>
<td><b>59.7</b></td>
</tr>
<tr>
<td rowspan="4">CommonSense Understanding</td>
<td>PIQA (0-shot)</td>
<td><b>83.0</b></td>
<td>80.5</td>
<td>82.1</td>
<td>79.4</td>
</tr>
<tr>
<td>SciQ (0-shot)</td>
<td><b>97.1</b></td>
<td>95.2</td>
<td>95.2</td>
<td>93.5</td>
</tr>
<tr>
<td>Winogrande (0-shot)</td>
<td><b>74.2</b></td>
<td>72.7</td>
<td>73.2</td>
<td>73.6</td>
</tr>
<tr>
<td>OpenbookQA (0-shot)</td>
<td><b>47.2</b></td>
<td>45.2</td>
<td><b>47.2</b></td>
<td>45.0</td>
</tr>
</tbody>
</table>
## Useful links
- View our [release blogpost](https://huggingface.co/blog/falcon3).
- Feel free to join [our discord server](https://discord.gg/fwXpMyGc) if you have any questions or to interact with our researchers and developers.
## Technical Report
Coming soon....
## Citation
If the Falcon3 family of models were helpful to your work, feel free to give us a cite.
```
@misc{Falcon3,
title = {The Falcon 3 Family of Open Models},
url = {https://huggingface.co/blog/falcon3},
author = {Falcon-LLM Team},
month = {December},
year = {2024}
}
```
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/tiiuae__Falcon3-10B-Base-details)
| Metric |Value|
|-------------------|----:|
|Avg. |27.59|
|IFEval (0-Shot) |36.48|
|BBH (3-Shot) |41.38|
|MATH Lvl 5 (4-Shot)|24.77|
|GPQA (0-shot) |12.75|
|MuSR (0-shot) |14.17|
|MMLU-PRO (5-shot) |36.00|

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