unsloth/Falcon-H1-3B-Instruct-GGUF

Includes our chat template fixes!
For llama.cpp, use --jinja

Unsloth Dynamic 2.0 achieves superior accuracy & outperforms other leading quants.

Table of Contents

0. TL;DR
1. Model Details
2. Training Details
3. Usage
4. Evaluation
5. Citation

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 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
vllm serve tiiuae/Falcon-H1-1B-Instruct --tensor-parallel-size 2 --data-parallel-size 1

llama.cpp

You can find all GGUF files under our official collection

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 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}
}