Instructions to use tiiuae/Falcon3-3B-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tiiuae/Falcon3-3B-Base with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="tiiuae/Falcon3-3B-Base")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tiiuae/Falcon3-3B-Base")
model = AutoModelForCausalLM.from_pretrained("tiiuae/Falcon3-3B-Base")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use tiiuae/Falcon3-3B-Base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tiiuae/Falcon3-3B-Base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tiiuae/Falcon3-3B-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/tiiuae/Falcon3-3B-Base

SGLang

How to use tiiuae/Falcon3-3B-Base with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "tiiuae/Falcon3-3B-Base" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tiiuae/Falcon3-3B-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "tiiuae/Falcon3-3B-Base" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tiiuae/Falcon3-3B-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use tiiuae/Falcon3-3B-Base with Docker Model Runner:
```
docker model run hf.co/tiiuae/Falcon3-3B-Base
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Falcon3-3B-Base

Falcon3 family of Open Foundation Models is a set of pretrained and instruct LLMs ranging from 1B to 10B parameters.

This repository contains the Falcon3-3B-Base. It achieves strong results on reasoning, language understanding, instruction following, code and mathematics tasks. Falcon3-3B-Base supports 4 languages (English, French, Spanish, Portuguese) and a context length of up to 8K. It was pruned in terms of depth and width from Falcon3-7B-Base and was efficiently trained on only 100 GT using a knowledge distillation objective.

⚠️ This is a raw, pretrained model, which should be further finetuned using SFT, RLHF, continued pretraining, etc. for most use cases.

Model Details

Architecture
- Transformer-based causal decoder-only architecture
- 22 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
- 8K context length
- 131K vocab size
Pruned and healed from Falcon3-7B-Base on only 100 Gigatokens of datasets comprising of web, code, STEM, high quality and mutlilingual data using 1024 H100 GPU chips
Supports EN, FR, ES, PT
Developed by Technology Innovation Institute
License: TII Falcon-LLM License 2.0
Model Release Date: December 2024

Getting started

Click to expand

import torch
from transformers import pipeline

pipe = pipeline(
    "text-generation", 
    model="tiiuae/Falcon3-3B-Base", 
    torch_dtype=torch.bfloat16, 
    device_map="auto"
)
response = pipe("Question: How many hours in one day? Answer: ")
print(response[0]['generated_text'])

Benchmarks

We report in the following table our internal pipeline benchmarks.

We use lm-evaluation harness.
We report raw scores.
We use same batch-size across all models.

Category	Benchmark	Llama3.2-3B	Qwen2.5-3B	Minitron-4B	Falcon3-3B-Base
General	MMLU (5-shot)	56.1	65.6	58.7	55.5
	MMLU-PRO (5-shot)	24.9	32	26.2	28.8
	IFEval	12.8	27	22.8	27.7
Math	GSM8K (5-shot)	26.7	69	25.7	63.9
Math	MATH Lvl-5 (4-shot)	1.4	8.4	1.7	9.4
Reasoning	Arc Challenge (25-shot)	50.8	55.5	50.3	54.9
	GPQA (0-shot)	27.5	27.5	28.6	31.2
	MUSR (0-shot)	35.2	43	42.1	37.5
	BBH (3-shot)	38.6	46.1	40.9	44.2
CommonSense Understanding	PIQA (0-shot)	77.4	78.9	78.3	75.6
	SciQ (0-shot)	92.7	95.6	96.1	93.1
	Winogrande (0-shot)	69.7	68.8	68.4	64.6
	OpenbookQA (0-shot)	43.2	42.2	43	39.4

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.

Technical Report

Coming soon....

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},
    url = {https://huggingface.co/blog/falcon3},
    author = {Falcon-LLM Team},
    month = {December},
    year = {2024}
}