đ Transformers Library for Falcon-H1
This library provides access to the Falcon-H1 series of models, offering high performance across various natural language processing tasks. It supports multiple inference methods and has been well-evaluated on a range of benchmarks.
đ Quick Start
To quickly get started with the Falcon-H1 models, you can choose from different inference methods as described in the Usage section.
⨠Features
- Hybrid Architecture: Combines Transformers and Mamba architecture for enhanced performance.
- Multilingual Support: Supports English and other languages.
- Multiple Inference Options: Can be used with Hugging Face
transformers
, vLLM
, or a custom fork of llama.cpp
.
đĻ Installation
To use this model, you need to install the necessary libraries. Here are the installation commands for different inference methods:
Install transformers
from source
pip install git+https://github.com/huggingface/transformers.git
Install vLLM
pip install vllm
Install custom fork of llama.cpp
You can install the custom fork of llama.cpp
from here. Follow the same installation guidelines as llama.cpp
.
đģ Usage Examples
Basic Usage
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"
)
Using vLLM
vllm serve tiiuae/Falcon-H1-1B-Instruct --tensor-parallel-size 2 --data-parallel-size 1
Advanced Usage
While we are working on integrating our architecture directly into llama.cpp
library, you can use our custom fork for now. Refer to https://github.com/tiiuae/llama.cpp-Falcon-H1 for installation and usage details.
đ Documentation
Model Details
Property |
Details |
Model Type |
Causal decoder-only |
Architecture |
Hybrid Transformers + Mamba architecture |
Language(s) (NLP) |
English, Multilingual |
License |
Falcon-LLM License |
Developed by |
https://www.tii.ae |
Training Details
For more details about the training protocol of this model, please refer to the Falcon-H1 technical blogpost.
Evaluation
Falcon-H1 series perform very well on a variety of tasks, including reasoning tasks.
Tasks |
Falcon-H1-3B |
Qwen3-4B |
Qwen2.5-3B |
Gemma3-4B |
Llama3.2-3B |
Falcon3-3B |
General |
|
|
|
|
|
|
BBH |
53.69 |
51.07 |
46.55 |
50.01 |
41.47 |
45.02 |
ARC-C |
49.57 |
37.71 |
43.77 |
44.88 |
44.88 |
48.21 |
TruthfulQA |
53.19 |
51.75 |
58.11 |
51.68 |
50.27 |
50.06 |
HellaSwag |
69.85 |
55.31 |
64.21 |
47.68 |
63.74 |
64.24 |
MMLU |
68.3 |
67.01 |
65.09 |
59.53 |
61.74 |
56.76 |
Math |
|
|
|
|
|
|
GSM8k |
84.76 |
80.44 |
57.54 |
77.41 |
77.26 |
74.68 |
MATH-500 |
74.2 |
85.0 |
64.2 |
76.4 |
41.2 |
54.2 |
AMC-23 |
55.63 |
66.88 |
39.84 |
48.12 |
22.66 |
29.69 |
AIME-24 |
11.88 |
22.29 |
6.25 |
6.67 |
11.67 |
3.96 |
AIME-25 |
13.33 |
18.96 |
3.96 |
13.33 |
0.21 |
2.29 |
Science |
|
|
|
|
|
|
GPQA |
33.89 |
28.02 |
28.69 |
29.19 |
28.94 |
28.69 |
GPQA_Diamond |
38.72 |
40.74 |
35.69 |
28.62 |
29.97 |
29.29 |
MMLU-Pro |
43.69 |
29.75 |
32.76 |
29.71 |
27.44 |
29.71 |
MMLU-stem |
69.93 |
67.46 |
59.78 |
52.17 |
51.92 |
56.11 |
Code |
|
|
|
|
|
|
HumanEval |
76.83 |
84.15 |
73.78 |
67.07 |
54.27 |
52.44 |
HumanEval+ |
70.73 |
76.83 |
68.29 |
61.59 |
50.0 |
45.73 |
MBPP |
79.63 |
68.78 |
72.75 |
77.78 |
62.17 |
61.9 |
MBPP+ |
67.46 |
59.79 |
60.85 |
66.93 |
50.53 |
55.29 |
LiveCodeBench |
26.81 |
39.92 |
11.74 |
21.14 |
2.74 |
3.13 |
CRUXEval |
56.25 |
69.63 |
43.26 |
52.13 |
17.75 |
44.38 |
Instruction Following |
|
|
|
|
|
|
IFEval |
85.05 |
84.01 |
64.26 |
77.01 |
74.0 |
69.1 |
Alpaca-Eval |
31.09 |
36.51 |
17.37 |
39.64 |
19.69 |
14.82 |
MTBench |
8.72 |
8.45 |
7.79 |
8.24 |
7.96 |
7.79 |
LiveBench |
36.86 |
51.34 |
27.32 |
36.7 |
26.37 |
26.01 |
You can check more in detail on our release blogpost, detailed benchmarks.
đ License
This model is licensed under the Falcon-LLM License.
đ Citation
If the Falcon-H1 family of models were helpful to your work, feel free to cite us using the following BibTeX entry:
@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}
}
đ Useful links