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tiiuae / Falcon-H1-1.5B-Base
Model's Last Updated: July 31 2025
text-generation
Introduction of Falcon-H1-1.5B-Base
Model Details of Falcon-H1-1.5B-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-H1-1.5B | Qwen3-1.7B | Qwen2.5-1.5B | Gemma3-1B | Llama3.2-1B | Falcon3-1B |
|---|---|---|---|---|---|---|
| General | ||||||
| BBH | 46.57 | 43.05 | 40.55 | 30.26 | 30.72 | 35.24 |
| MMLU | 61.81 | 62.46 | 61.13 | 26.33 | 32.39 | 45.14 |
| ARC-C | 53.24 | 55.72 | 54.27 | 39.33 | 39.42 | 47.87 |
| HellaSwag | 66.76 | 67.09 | 67.86 | 62.94 | 65.73 | 62.3 |
| Winogrande | 65.59 | 66.3 | 64.56 | 62.59 | 62.75 | 61.17 |
| Math | ||||||
| GSM8k | 52.01 | 70.74 | 63.0 | 2.2 | 7.05 | 34.95 |
| MATH lvl5 | 20.39 | 16.39 | 8.84 | 1.21 | 0.98 | 3.4 |
| Science | ||||||
| GPQA | 29.11 | 29.45 | 28.36 | 24.66 | 23.57 | 27.85 |
| MMLU-Pro | 35.53 | 33.81 | 28.72 | 11.31 | 11.8 | 16.11 |
| MMLU-stem | 63.37 | 61.53 | 54.93 | 27.59 | 30.19 | 40.06 |
| Code | ||||||
| HumanEval | 50.0 | 67.68 | 35.37 | 6.71 | 18.9 | 10.37 |
| HumanEval+ | 42.68 | 60.98 | 29.27 | 5.49 | 16.46 | 9.15 |
| MBPP | 65.08 | 67.72 | 60.05 | 12.7 | 35.98 | 12.43 |
| MBPP+ | 55.03 | 58.99 | 49.47 | 9.52 | 29.89 | 9.52 |
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-H1-1.5B-Base on huggingface.co
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More Information About Falcon-H1-1.5B-Base huggingface.co Model
Url of Falcon-H1-1.5B-Base
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