---
language:
- en
tags:
- falcon3
- falcon3_mamba
- falcon_mamba
license: other
license_name: falcon-llm-license
license_link: https://falconllm.tii.ae/falcon-terms-and-conditions.html
library_name: transformers
---
# Falcon3-Mamba-7B-Base
**Falcon3** family of Open Foundation Models is a set of pretrained and instruct LLMs ranging from 1B to 10B.
This repository contains the **Falcon3-Mamba-7B**. It achieves, compared to similar SSM-based models of the same size, state of art results (at release's time) on reasoning, language understanding, instruction following, code and mathematics tasks.
Falcon3-Mamba-7B-Base supports a context length up to 32K and was mainly trained on english corpus.
## Model Details
- Architecture (same as [Falcon-Mamba-7b](https://huggingface.co/tiiuae/falcon-mamba-7b))
- Mamba1 based causal decoder only architecture trained on a causal language modeling task (i.e., predict the next token).
- 64 decoder blocks
- width: 4096
- state dimension: 16
- 32k context length
- 65k vocab size
- Continue Pretrained from Falcon Mamba 7B, with another 1500 Gigatokens of data comprising of web, code, STEM and high quality data.
- Postrained on 1.2 million samples of STEM, conversations, code, and safety.
- Developed by [Technology Innovation Institute](https://www.tii.ae)
- License: TII Falcon-LLM License 2.0
- Model Release Date: December 2024
## Getting started
Click to expand
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "tiiuae/Falcon3-Mamba-7B-Base"
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
prompt = "How many hours in one day?"
messages = [
{"role": "system", "content": "You are a helpful friendly assistant Falcon3 from TII, try to follow instructions as much as possible."},
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
generated_ids = model.generate(
**model_inputs,
max_new_tokens=1024
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)
```
# Benchmarks
We report in the following table our internal pipeline benchmarks. For the benchmarks marked by star, we normalize the results with HuggingFace score normalization:
Category |
Benchmark |
Zamba2-7B |
Llama-3.1-8B |
Falcon-Mamba-7B |
Falcon3-Mamba-7B-Base |
General |
MMLU (5-shot) |
64.9 |
66.4 |
59.9 |
64.9 |
MMLU-PRO (5-shot)* |
24.5 |
24.9 |
14.5 |
22.6 |
IFEval |
37.4 |
12.7 |
33.4 |
30.1 |
Math |
GSM8K (5-shot) |
55.8 |
47.9 |
51.3 |
65.9 |
MATH (4-shot) |
10.3 |
5.1 |
3.6 |
15.6 |
Reasoning |
Arc Challenge (25-shot) |
54.1 |
58.5 |
55.9 |
56.7 |
GPQA (0-shot)* |
9.4 |
6.2 |
8.1 |
10.6 |
MUSR (0-shot)* |
7.5 |
8.9 |
10.9 |
4.5 |
BBH (3-shot)* |
27.9 |
25.3 |
19.9 |
25.6 |
CommonSense Understanding |
PIQA (0-shot) |
79.27 |
81.2 |
80.2 |
79.54 |
SciQ (0-shot) |
94.4 |
94.6 |
96.3 |
92.0 |
Winogrande (0-shot) |
77.4 |
74.0 |
74.9 |
71.27 |
## Useful links
- View our [release blogpost](https://huggingface.co/blog/falcon3).
- Feel free to join [our discord server](https://discord.gg/fwXpMyGc) if you have any questions or to interact with our researchers and developers.
## Citation
If the Falcon3 family of models were helpful to your work, feel free to give us a cite.
```
@misc{Falcon3,
title = {The Falcon 3 Family of Open Models},
author = {Falcon-LLM Team},
month = {December},
year = {2024}
}
```