---
language:
- en
- fr
- es
- pt
tags:
- falcon3
base_model: tiiuae/Falcon3-7B-Base
license: other
license_name: falcon-llm-license
license_link: https://falconllm.tii.ae/falcon-terms-and-conditions.html
---
# Falcon3-7B-Instruct
**Falcon3** family of Open Foundation Models is a set of pretrained and instruct LLMs ranging from 1B to 10B.
This repository contains the **Falcon3-7B-Instruct**. It achieves state of art results (at release's time) on reasoning, language understanding, instruction following, code and mathematics tasks.
Falcon3-7B-Instruct supports 4 languages (english, french, spanish, portuguese) and a context length up to 32K.
## Model Details
- Architecture
- Transformer based causal decoder only architecture
- 28 decoder blocks
- Grouped query attention (GQA) for faster inference: 12 query heads and 4 key value heads
- Wider head dimension: 256
- High RoPE value to support long context understanding: 1000042
- Uses SwiGLU and RMSNorm
- 32K context length
- 131K vocab size
- Pretrained on 14 Teratokens of datasets comprising of web, code, STEM, high quality and mutlilingual data using 2048 H100 GPU chips
- Postrained on 1.2 million samples of STEM, conversations, code, safety and function call data
- Supports EN, FR, ES, PT
- 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/Falcon3-7B-Instruct"
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:
Category |
Benchmark |
Llama-3.1-8B-Instruct |
Qwen2.5-7B-Instruct |
Falcon3-7B-Instruct |
General |
MMLU (5-shot) |
55.9 |
72.4 |
68 |
MMLU-PRO (5-shot) |
21.8 |
35.8 |
40.7 |
IFEval |
78.8 |
74.7 |
76.5 |
Math |
GSM8K (5-shot) |
78.1 |
77.5 |
79.1 |
GSM8K (8-shot, COT) |
79.8 |
72.7 |
80.9 |
MATH Lvl-5 (4-shot) |
10.4 |
26 |
33.1 |
Reasoning |
Arc Challenge (25-shot) |
46.6 |
55.7 |
65.9 |
GPQA (0-shot) |
33.6 |
31.9 |
32 |
GPQA (0-shot, COT) |
9.6 |
13.8 |
22.3 |
MUSR (0-shot) |
38.6 |
40.7 |
46.4 |
BBH (3-shot) |
43.7 |
53.9 |
52.4 |
CommonSense Understanding |
PIQA (0-shot) |
78.9 |
73.7 |
78.8 |
SciQ (0-shot) |
80.2 |
50.9 |
94.7 |
Winogrande (0-shot) |
- |
- |
70.4 |
OpenbookQA (0-shot) |
46.2 |
42.4 |
45.8 |
Instructions following |
MT-Bench (avg) |
7.86 |
8.54 |
8.36 |
Alapaca (WC) |
26.57 |
31.5 |
26.13 |
Tool use |
BFCL AST (avg) |
90.6 |
91.4 |
72.3 |
## Technical Report
Coming soon....
## Citation
If Falcon3 family were helpful to your work, feel free to give us a cite.
```
@misc{Falcon3,
title = {The Falcon 3 family of Open Models},
author = {TII Team},
month = {December},
year = {2024}
}
```