2024-12-18 10:01:58 +08:00
---
language:
- en
- fr
- es
- pt
tags:
- falcon3
base_model: tiiuae/Falcon3-3B-Instruct
license: other
license_name: falcon-llm-license
license_link: https://falconllm.tii.ae/falcon-terms-and-conditions.html
library_name: transformers
---
2024-12-18 09:39:10 +08:00
2024-12-18 10:01:58 +08:00
< div align = "center" >
< img src = "https://huggingface.co/datasets/tiiuae/documentation-images/resolve/main/general/falco3-logo.png" alt = "drawing" width = "500" / >
< / div >
# Falcon3-3B-Instruct
**Falcon3** family of Open Foundation Models is a set of pretrained and instruct LLMs ranging from 1B to 10B parameters.
**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 ](https://www.tii.ae )
- License: TII Falcon-LLM License 2.0
- Model Release Date: December 2024
## Getting started
< details >
< summary > Click to expand < / summary >
```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)
```
< / details >
< br >
## Benchmarks
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 **without fewshot_as_multiturn** (unlike Llama3.1).
- We use same batch-size across all models.
< table border = "1" style = "width: 100%; text-align: center; border-collapse: collapse;" >
< colgroup >
< col style = "width: 10%;" >
< col style = "width: 10%;" >
< col style = "width: 7%;" >
< col style = "width: 7%;" >
< col style = "width: 7%;" >
< col style = "background-color: rgba(80, 15, 213, 0.5); width: 7%;" >
< / colgroup >
< thead >
< tr >
< th > Category< / th >
< th > Benchmark< / th >
< th > Llama-3.2-3B-Instruct< / th >
< th > Qwen2.5-3B-Instruct< / th >
< th > Nemotron-Mini-4B-Instruct< / th >
< th > Falcon3-3B-Instruct< / th >
< / tr >
< / thead >
< tbody >
< tr >
< td rowspan = "3" > General< / td >
< td > MMLU (5-shot)< / td >
< td > 29.3< / td >
< td > 56.2< / td >
< td > < b > 56.4< / b > < / td >
< td > 55.7< / td >
< / tr >
< tr >
< td > MMLU-PRO (5-shot)< / td >
< td > 11.9< / td >
< td > 17.2< / td >
< td > 23.3< / td >
< td > < b > 29.7< / b > < / td >
< / tr >
< tr >
< td > IFEval< / td >
< td > < b > 73.9< / b > < / td >
< td > 64.2< / td >
< td > 66.5< / td >
< td > 68.3< / td >
< / tr >
< tr >
< td rowspan = "3" > Math< / td >
< td > GSM8K (5-shot)< / td >
< td > 68.5< / td >
< td > 58.5< / td >
< td > 46.9< / td >
< td > < b > 71.9< / b > < / td >
< / tr >
< tr >
< td > GSM8K (8-shot, COT)< / td >
< td > < b > 74.5< / b > < / td >
< td > 64.0< / td >
< td > 46.5< / td >
< td > 71.6< / td >
< / tr >
< tr >
< td > MATH Lvl-5 (4-shot)< / td >
< td > 2.4< / td >
< td > 0.0< / td >
< td > 0.0< / td >
< td > < b > 19.9< / b > < / td >
< / tr >
< tr >
< td rowspan = "5" > Reasoning< / td >
< td > Arc Challenge (25-shot)< / td >
< td > 38.9< / td >
< td > 50.0< / td >
< td > 51.2< / td >
< td > < b > 58.5< / b > < / td >
< / tr >
< tr >
< td > GPQA (0-shot)< / td >
< td > 28.1< / td >
< td > 29.2< / td >
< td > 27.0< / td >
< td > < b > 29.6< / b > < / td >
< / tr >
< tr >
< td > GPQA (0-shot, COT)< / td >
< td > 11.3< / td >
< td > 11.0< / td >
< td > 12.2< / td >
< td > < b > 26.5< / b > < / td >
< / tr >
< tr >
< td > MUSR (0-shot)< / td >
< td > 34.9< / td >
< td > < b > 40.2< / b > < / td >
< td > 38.9< / td >
< td > 39.0< / td >
< / tr >
< tr >
< td > BBH (3-shot)< / td >
< td > 33.1< / td >
< td > 44.1< / td >
< td > 38.1< / td >
< td > < b > 45.4< / b > < / td >
< / tr >
< tr >
< td rowspan = "4" > CommonSense Understanding< / td >
< td > PIQA (0-shot)< / td >
< td > 74.6< / td >
< td > 73.8< / td >
< td > 74.6< / td >
< td > < b > 75.6< / b > < / td >
< / tr >
< tr >
< td > SciQ (0-shot)< / td >
< td > 77.2< / td >
< td > 60.7< / td >
< td > 71.0< / td >
< td > < b > 95.5< / b > < / td >
< / tr >
< tr >
< td > Winogrande (0-shot)< / td >
< td > -< / td >
< td > -< / td >
< td > -< / td >
< td > < b > 65.0< / b > < / td >
< / tr >
< tr >
< td > OpenbookQA (0-shot)< / td >
< td > 40.8< / td >
< td > 41.2< / td >
< td > < b > 43.2< / b > < / td >
< td > 42.2< / td >
< / tr >
< tr >
< td rowspan = "2" > Instructions following< / td >
< td > MT-Bench (avg)< / td >
< td > 7.1< / td >
< td > < b > 8.0< / b > < / td >
< td > 6.7< / td >
< td > 7.2< / td >
< / tr >
< tr >
< td > Alpaca (WC)< / td >
< td > < b > 19.4< / b > < / td >
< td > 19.4< / td >
< td > 9.6< / td >
< td > 15.5< / td >
< / tr >
< tr >
< td > Tool use< / td >
< td > BFCL AST (avg)< / td >
< td > < b > 85.2< / b > < / td >
< td > 84.8< / td >
< td > 59.8< / td >
< td > 65.3< / td >
< / tr >
< tr >
< td rowspan = "2" > Code< / td >
< td > EvalPlus (0-shot) (avg)< / td >
< td > 55.2< / td >
< td > < b > 69.4< b > < / td >
< td > 40.0< / td >
< td > 52.9< / td >
< / tr >
< tr >
< td > Multipl-E (0-shot) (avg)< / td >
< td > 31.6< / td >
< td > 29.2< / td >
< td > 19.6< / td >
< td > < b > 32.9< / b > < / td >
< / tr >
< / tbody >
< / table >
## 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 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}
}
```
