---
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
library_name: transformers
---
# 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 the time of release) 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 1024 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 the official HuggingFace leaderboard normalized evaluations [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) in the following table.
Benchmark |
Llama-3.1-8B-Instruct |
Qwen2.5-7B-Instruct |
Falcon3-7B-Instruct |
IFEval |
78.56 |
75.85 |
76.12 |
BBH (3-shot) |
29.89 |
34.89 |
37.92 |
MATH Lvl-5 (4-shot) |
19.34 |
0.00 |
31.87 |
GPQA (0-shot) |
2.35 |
5.48 |
8.05 |
MUSR (0-shot) |
8.41 |
8.45 |
21.17 |
MMLU-PRO (5-shot) |
30.68 |
36.52 |
34.30 |
Also, we report in the following table our internal pipeline benchmarks.
- We use [lm-evaluation harness](https://github.com/EleutherAI/lm-evaluation-harness).
- We report **raw scores** obtained by applying chat template and fewshot_as_multiturn.
- We use same batch-size across all models.
Category |
Benchmark |
Llama-3.1-8B-Instruct |
Qwen2.5-7B-Instruct |
Falcon3-7B-Instruct |
General |
MMLU (5-shot) |
68.2 |
73.5 |
70.5 |
MMLU-PRO (5-shot) |
36.4 |
43.1 |
40.7 |
IFEval |
78.8 |
74.7 |
76.5 |
Math |
GSM8K (5-shot) |
82.6 |
72.0 |
81.4 |
GSM8K (8-shot, COT) |
85.4 |
76.6 |
79.7 |
MATH Lvl-5 (4-shot) |
15.4 |
- |
29.4 |
Reasoning |
Arc Challenge (25-shot) |
58.6 |
57.8 |
62.6 |
GPQA (0-shot) |
33.5 |
32 |
31.9 |
GPQA (0-shot, COT) |
9.6 |
13.8 |
22.3 |
MUSR (0-shot) |
38.6 |
41 |
46.4 |
BBH (3-shot) |
48.6 |
54.1 |
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) |
7.9 |
8.5 |
8.4 |
Alpaca (WC) |
26.6 |
31.5 |
26.1 |
Tool use |
BFCL AST (avg) |
90.6 |
91.4 |
89.5 |
## 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.
## 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}
}
```