transformers safetensors llama text-generation falcon3 en fr es pt license:other model-index autotrain_compatible text-generation-inference endpoints_compatible region:us
---
language:
- en
- fr
- es
- pt
library_name: transformers
license: other
license_name: falcon-llm-license
license_link: https://falconllm.tii.ae/falcon-terms-and-conditions.html
tags:
- falcon3
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|

