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Sponsored by
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tiiuae / Falcon-H1-7B-Base
Model's Last Updated: July 31 2025
text-generation
Introduction of Falcon-H1-7B-Base
Model Details of Falcon-H1-7B-Base
Table of Contents
TL;DR
Model Details
Model Description
- Developed by: https://www.tii.ae
- Model type: Causal decoder-only
- Architecture: Hybrid Transformers + Mamba architecture
- Language(s) (NLP): English, Multilingual
- License: Falcon-LLM License
Training details
For more details about the training protocol of this model, please refer to the Falcon-H1 technical blogpost .
Usage
Currently to use this model you can either rely on Hugging Face
transformers
,
vLLM
or our custom fork of
llama.cpp
library.
Inference
Make sure to install the latest version of
transformers
or
vllm
, eventually install these packages from source:
pip install git+https://github.com/huggingface/transformers.git
For vLLM, make sure to install
vllm>=0.9.0
:
pip install "vllm>=0.9.0"
🤗 transformers
Refer to the snippet below to run H1 models using 🤗 transformers:
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "tiiuae/Falcon-H1-1B-Base"
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
device_map="auto"
)
# Perform text generation
vLLM
For vLLM, simply start a server by executing the command below:
# pip install vllm>=0.9.0
vllm serve tiiuae/Falcon-H1-1B-Instruct --tensor-parallel-size 2 --data-parallel-size 1
llama.cpp
While we are working on integrating our architecture directly into
llama.cpp
library, you can install our fork of the library and use it directly:
https://github.com/tiiuae/llama.cpp-Falcon-H1
Use the same installing guidelines as
llama.cpp
.
Evaluation
Falcon-H1 series perform very well on a variety of tasks, including reasoning tasks.
| Tasks | Falcon-H1-7B | Qwen3-8B | Qwen2.5-7B | Gemma3-12B | Llama3.1-8B | Falcon3-7B | Falcon3-10B |
|---|---|---|---|---|---|---|---|
| General | |||||||
| BBH | 60.61 | 58.44 | 53.72 | 54.33 | 46.52 | 50.88 | 59.3 |
| MMLU | 77.38 | 76.63 | 74.17 | 74.23 | 65.17 | 69.98 | 73.22 |
| ARC-C | 65.19 | 67.75 | 63.91 | 67.58 | 57.68 | 62.71 | 67.49 |
| HellaSwag | 81.26 | 79.6 | 80.2 | 84.22 | 81.97 | 76.69 | 79.64 |
| Winogrande | 79.01 | 76.8 | 76.01 | 79.79 | 77.11 | 73.64 | 79.01 |
| Math | |||||||
| GSM8k | 73.46 | 83.02 | 83.09 | 71.19 | 49.51 | 76.95 | 82.11 |
| MATH lvl5 | 34.67 | 28.85 | 22.58 | 17.22 | 6.57 | 20.09 | 25.38 |
| Science | |||||||
| GPQA | 36.58 | 35.65 | 32.3 | 34.56 | 31.46 | 35.07 | 35.4 |
| MMLU-Pro | 48.38 | 48.25 | 43.55 | 42.72 | 32.71 | 39.23 | 42.45 |
| MMLU-stem | 77.2 | 78.53 | 71.04 | 68.51 | 55.72 | 67.71 | 70.85 |
| Code | |||||||
| HumanEval | 67.68 | 87.8 | 57.32 | 45.12 | 39.02 | 50.0 | 51.83 |
| HumanEval+ | 63.41 | 82.32 | 48.78 | 36.59 | 31.71 | 43.29 | 44.51 |
| MBPP | 78.57 | 75.13 | 76.72 | 73.02 | 61.38 | 67.99 | 73.54 |
| MBPP+ | 67.2 | 64.02 | 63.49 | 59.79 | 51.32 | 57.14 | 61.38 |
You can check more in detail on our our release blogpost , detailed benchmarks.
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.
Citation
If the Falcon-H1 family of models were helpful to your work, feel free to give us a cite.
@misc{tiifalconh1,
title = {Falcon-H1: A Family of Hybrid-Head Language Models Redefining Efficiency and Performance},
url = {https://falcon-lm.github.io/blog/falcon-h1},
author = {Falcon-LLM Team},
month = {May},
year = {2025}
}
Runs of tiiuae Falcon-H1-7B-Base on huggingface.co
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More Information About Falcon-H1-7B-Base huggingface.co Model
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