metadata
language:
- en
tags:
- falcon3
- falcon3_mamba
- falcon_mamba
base_model:
- tiiuae/Falcon3-Mamba-7B-Base
license: other
license_name: falcon-llm-license
license_link: https://falconllm.tii.ae/falcon-terms-and-conditions.html
library_name: transformers
Falcon3-Mamba-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-Mamba-7B-Instruct. It achieves, compared to similar SSM-based models of the same size, state of art results (at release's time) on reasoning, language understanding, instruction following, code and mathematics tasks. Falcon3-Mamba-7B-Instruct supports a context length up to 32K and was mainly trained on english corpus.
Model Details
- Architecture (same as Falcon-Mamba-7b)
- Mamba1 based causal decoder only architecture trained on a causal language modeling task (i.e., predict the next token).
- 64 decoder blocks
- width: 4096
- state_size: 16
- 32k context length
- 65k vocab size
- Continue Pretrained from Falcon Mamba 7B, with another 1500 Gigatokens of data comprising of web, code, STEM and high quality data.
- Postrained on 1.2 million samples of STEM, conversations, code, and safety.
- Developed by Technology Innovation Institute
- License: TII Falcon-LLM License 2.0
- Model Release Date: December 2024
Getting started
Click to expand
from transformers import AutoTokenizer, AutoModelForCausalLM
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "tiiuae/Falcon3-Mamba-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. For the benchmarks marked by star, we normalize the results with HuggingFace score normalization:
| Category | Benchmark | Zamba2-7B-instruct | Jamba-1.5-Mini | Llama-3.1-8B-Instruct | Falcon3-Mamba-7B-Instruct |
|---|---|---|---|---|---|
| General | MMLU (5-shot) | - | 68.7% | 55.9% | 65.3% |
| MMLU-PRO (5-shot)* | 32.4% | 31.6% | 21.8% | 26.3% | |
| IFEval | 69.9% | 65.7% | 78.8% | 71.7% | |
| Math | GSM8K (5-shot) | - | 74.9% | 19.2% | 65.2% |
| MATH Lvl-5 (4-shot) | - | 6.9% | 10.4% | 27.3% | |
| Reasoning | Arc Challenge (25-shot) | - | 54.3% | 46.6% | 53.7% |
| GPQA (0-shot)* | 10.3% | 11.1% | 33.6% | 7.2% | |
| MUSR (0-shot)* | 8.2% | 12.2% | 38.6% | 8.3% | |
| BBH (3-shot)* | 33.3% | 35.3% | 43.7% | 25.2% | |
| CommonSense Understanding | PIQA (0-shot) | - | 82.3% | 78.9% | 80.9% |
| SciQ (0-shot) | - | 94.9% | 80.2% | 93.6% | |
| Winogrande (0-shot) | - | 64.5% | - | - | |
| OpenbookQA (0-shot) | - | 34.6% | 46.2% | 47.2% |
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.
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},
author = {Falcon-LLM Team},
month = {December},
year = {2024}
}