---
language:
- en
tags:
- falcon3
- falcon3_mamba
- falcon_mamba
---
# Falcon3-Mamba-7B-Base
**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**. 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-Base supports a context length up to 32K and was mainly trained on english corpus.
## Model Details
- Architecture (same as [Falcon-Mamba-7b](https://huggingface.co./tiiuae/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 dimension: 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](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-Mamba-7B-Base"
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:
Category | Benchmark | Zamba2-7B | Llama-3.1-8B | Falcon-Mamba-7B | Falcon3-Mamba-7B-Base |
---|---|---|---|---|---|
General | MMLU (5-shot) | - | - | - | - |
MMLU-PRO (5-shot) | - | - | - | - | |
- | - | - | - | ||
Math | GSM8K (5-shot) | - | - | - | - |
MATH Lvl-5 (4-shot) | - | - | - | - | |
Reasoning | Arc Challenge (25-shot) | - | - | - | - |
GPQA (0-shot) | - | - | - | - | |
MUSR (0-shot) | - | - | - | - | |
BBH (3-shot) | - | - | - | - | |
CommonSense Understanding | PIQA (0-shot) | - | - | - | - |
SciQ (0-shot) | - | - | - | - | |
Winogrande (0-shot) | - | - | - | - | |
OpenbookQA (0-shot) | - | - | - | - |