Falcon3-3B-Instruct achieves strong results on reasoning, language understanding, instruction following, code and mathematics tasks. Falcon3-3B-Instruct supports 4 languages (English, French, Spanish, Portuguese) and a context length of up to 32K.

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
- 32K 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
Posttrained on 1.2 million samples of STEM, conversational, code, safety and function call data
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

python

from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "tiiuae/Falcon3-3B-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.

We use 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-3.2-3B-Instruct	Qwen2.5-3B-Instruct	Nemotron-Mini-4B-Instruct	Falcon3-3B-Instruct
General	MMLU (5-shot)	61.2	65.4	57.3	56.9
MMLU-PRO (5-shot)	27.7	32.6	26.0	29.7
IFEval	74.7	64.1	66.3	68.3
Math	GSM8K (5-shot)	76.8	56.7	29.8	74.8
GSM8K (8-shot, COT)	78.8	60.8	35.0	78.0
MATH Lvl-5 (4-shot)	14.6	0.0	0.0	19.9
Reasoning	Arc Challenge (25-shot)	50.9	55.0	56.2	55.5
GPQA (0-shot)	32.2	29.2	27.0	29.6
GPQA (0-shot, COT)	11.3	11.0	12.2	26.5
MUSR (0-shot)	35.0	40.2	38.7	39.0
BBH (3-shot)	41.8	44.5	39.5	45.4
CommonSense Understanding	PIQA (0-shot)	74.6	73.8	74.6	75.6
SciQ (0-shot)	77.2	60.7	71.0	95.5
Winogrande (0-shot)	-	-	-	65.0
OpenbookQA (0-shot)	40.8	41.2	43.2	42.2
Instructions following	MT-Bench (avg)	7.1	8.0	6.7	7.2
Alpaca (WC)	19.4	19.4	9.6	15.5
Tool use	BFCL AST (avg)	85.2	84.8	59.8	59.3
Code	EvalPlus (0-shot) (avg)	55.2	69.4	40.0	52.9
Multipl-E (0-shot) (avg)	31.6	29.2	19.6	32.9

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}
}