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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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