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tiiuae/Falcon3-7B-Base

sourceHugging Faceotherupdated 2y agoView on Hugging Face

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Model Card

Falcon3-7B-Base

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-Base. It achieves state of art results (at the time of release) on reasoning, language understanding, instruction following, code and mathematics tasks. Falcon3-7B-Base supports 4 languages (english, french, spanish, portuguese) and a context length up to 32K.

⚠️ This is a raw, pretrained model, which should be further finetuned for most usecases.

Model Details

—Architecture
—transformer based causal decoder only architecture
—28 decoder blocks
—grouped query attention (GQA) for faster inference: 12 query heads and 4 KV heads
—wider head dimension: 256
—high RoPE value to support long context understanding: 1000042
—32k context length
—131k vocab size
—Pretrained on 14 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
—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/Falcon3-7B-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.
—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="width: 7%;"> <col style="background-color: rgba(80, 15, 213, 0.5); width: 7%;"> </colgroup> <thead> <tr> <th>Category</th> <th>Benchmark</th> <th>Llama3.1-8B</th> <th>Qwen2-7B</th> <th>Qwen2.5-7B</th> <th>gemma-2-9b</th> <th>Falcon3-7B-Base</th> </tr> </thead> <tbody> <tr> <td rowspan="3">General</td> <td>MMLU (5-shot)</td> <td>65.2</td> <td>70.4</td> <td>74.2</td> <td>-</td> <td>67.5</td> </tr> <tr> <td>MMLU-PRO (5-shot)</td> <td>32.7</td> <td>42.1</td> <td>43.5</td> <td>-</td> <td>39.2</td> </tr> <tr> <td>IFEval</td> <td>12.0</td> <td>30.6</td> <td>33.9</td> <td>-</td> <td>34.3</td> </tr> <tr> <td rowspan="2">Math</td> <td>GSM8K (5-shot)</td> <td>49.4</td> <td>77.9</td> <td>82.9</td> <td>-</td> <td>76.2</td> </tr> <tr> <td>MATH(4-shot)</td> <td>4.1</td> <td>17.5</td> <td>15.5</td> <td>-</td> <td>18.0</td> </tr> <tr> <td rowspan="4">Reasoning</td> <td>Arc Challenge (25-shot)</td> <td>53.4</td> <td>57.4</td> <td>59.0</td> <td>-</td> <td>59.6</td> </tr> <tr> <td>GPQA (0-shot)</td> <td>31.0</td> <td>31.9</td> <td>33.0</td> <td>-</td> <td>35.5</td> </tr> <tr> <td>MUSR (0-shot)</td> <td>38.0</td> <td>44.1</td> <td>44.2</td> <td>-</td> <td>47.3</td> </tr> <tr> <td>BBH (3-shot)</td> <td>46.5</td> <td>53.3</td> <td>54.0</td> <td>-</td> <td>51.0</td> </tr> <tr> <td rowspan="4">CommonSense Understanding</td> <td>PIQA (0-shot)</td> <td>80.3</td> <td>79.8</td> <td>78.7</td> <td>-</td> <td>77.7</td> </tr> <tr> <td>SciQ (0-shot)</td> <td>96.3</td> <td>95.9</td> <td>96.6</td> <td>-</td> <td>95.3</td> </tr> <tr> <td>Winogrande (0-shot)</td> <td>74.0</td> <td>72.1</td> <td>72.9</td> <td>-</td> <td>71.0</td> </tr> <tr> <td>OpenbookQA (0-shot)</td> <td>33.4</td> <td>35.2</td> <td>33.6</td> <td>-</td> <td>31.4</td> </tr> </tbody> </table>

Useful links

—View our release blogpost.
—Feel free to join our discord server if you have any questions or to interact with our researchers and developers.

Technical Report

Coming soon....

Citation

If Falcon3 family were helpful to your work, feel free to give us a cite.

@misc{Falcon3,
    title = {Falcon 3 family of Open Foundation Models},
    author = {TII Team},
    month = {December},
    year = {2024}
}