You can deploy this model and start earning money today!

unsloth/
Falcon-H1-7B-Instruct-GGUF

Name: Falcon-H1-7B-Instruct-GGUF
Availability: InStock

quantized

tiiuae/Falcon-H1-7B-Instruct

Views

No views yet

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

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

drawing

TL;DR
Model Details
Training Details
Usage
Evaluation
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:

python

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`

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-H1-7B	Qwen3-8B	Qwen2.5-7B	Gemma3-12B	Llama3.1-8B	Falcon3-7B	Falcon3-10B
General
BBH	62.28	47.47	53.76	63.36	48.58	52.12	58.09
ARC-C	59.98	42.06	41.38	51.96	52.39	54.35	54.44
TruthfulQA	59.91	53.19	62.41	61.02	52.99	55.58	55.05
HellaSwag	75.92	60.56	63.4	55.63	71.28	71.81	75.57
MMLU	76.83	71.56	73.64	72.5	68.67	70.81	74.01
Math
GSM8k	81.65	78.92	71.95	87.49	82.49	81.05	85.06
MATH-500	73.4	83.8	75.8	86.2	45.8	69.0	68.6
AMC-23	56.72	70.78	53.91	66.88	22.81	40.0	45.78
AIME-24	16.04	28.33	12.29	22.5	5.42	8.75	9.79
AIME-25	13.96	19.17	9.58	18.75	0.42	6.25	5.42
Science
GPQA	36.33	25.84	31.79	33.98	32.72	31.21	33.39
GPQA_Diamond	56.9	43.1	33.0	37.71	31.31	37.21	34.68
MMLU-Pro	51.75	34.64	43.23	39.88	36.42	40.73	44.05
MMLU-stem	77.61	66.89	69.36	66.54	59.31	67.43	70.57
Code
HumanEval	86.59	84.75	82.32	84.76	68.29	71.95	82.32
HumanEval+	81.1	79.27	73.78	75.61	61.59	65.85	75.0
MBPP	80.69	71.96	79.63	85.71	68.25	77.25	73.28
MBPP+	68.78	62.7	68.25	72.22	55.03	65.87	64.02
LiveCodeBench	35.03	45.6	32.68	30.92	15.85	12.72	19.77
CRUXEval	66.51	72.7	56.9	67.67	21.57	55.0	59.57
Instruction Following
IFEval	85.35	83.43	75.25	81.51	77.04	76.59	78.84
Alpaca-Eval	40.23	46.13	29.48	43.55	25.48	27.56	24.31
MTBench	8.85	8.74	8.45	8.69	8.29	8.73	8.46
LiveBench	45.74	56.19	37.13	49.23	31.73	32.35	34.3

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}
}

unsloth/Falcon-H1-7B-Instruct-GGUF

Table of Contents