---
language:
- en
tags:
- falcon3
- falcon3_mamba
base_model:
- tiiuae/Falcon3-Mamba-7B-Base
---

# 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 1 language (english).

## 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
- Pretrained on 7 Teratokens of datasets comprising of web, code, STEM and high quality data using 2048 H100 GPU chips
- 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

<details>
<summary> Click to expand </summary>

```python
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)
```

</details>

<br>

# Benchmarks
We report in the following table our internal pipeline benchmarks:

<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="width: 7%;">
        <col style="background-color: rgba(80, 15, 213, 0.5); width: 7%;">
    </colgroup>
    <thead>
        <tr>
            <th>Category</th>
            <th>Benchmark</th>
            <th>Zamba2-7B-instruct</th>
            <th>Jamba-1.5-Mini</th>
            <th>Qwen2-7B-Instruct</th>
            <th>Llama-3.1-8B-Instruct</th>
            <th>Falcon3-Mamba-7B-Instruct</th>
        </tr>
    </thead>
    <tbody>
        <tr>
            <td rowspan="3">General</td>
            <td>MMLU (5-shot)</td>
            <td>-</td>
            <td>68.7%</td>
            <td>-</td>
            <td>55.9%</td>
            <td>-</td>
        </tr>
        <tr>
            <td>MMLU-PRO (5-shot)</td>
            <td>32.4%</td>
            <td>31.6%</td>
            <td>31.6%</td>
            <td>21.8%</td>
            <td>26.3%</td>
        </tr>
        <tr>
            <td>IFEval</td>
            <td>69.9%</td>
            <td>65.7%</td>
            <td>56.8%</td>
            <td>78.8%</td>
            <td>71.7%</td>
        </tr>
        <tr>
            <td rowspan="2">Math</td>
            <td>GSM8K (5-shot)</td>
            <td>-</td>
            <td>74.9%</td>
            <td>-</td>
            <td>19.2%</td>
            <td>-</td>
        </tr>
        <tr>
            <td>MATH Lvl-5 (4-shot)</td>
            <td>-</td>
            <td>6.9%</td>
            <td>9.44%</td>
            <td>10.4%</td>
            <td>27.3%</td>
        </tr>
        <tr>
            <td rowspan="4">Reasoning</td>
            <td>Arc Challenge (25-shot)</td>
            <td>-</td>
            <td>54.3%</td>
            <td>-</td>
            <td>46.6%</td>
            <td>-</td>
        </tr>
        <tr>
            <td>GPQA (0-shot)</td>
            <td>10.3%</td>
            <td>11.1%</td>
            <td>6.4%</td>
            <td>33.6%</td>
            <td>7.2%</td>
        </tr>
        <tr>
            <td>MUSR (0-shot)</td>
            <td>8.2%</td>
            <td>12.2%</td>
            <td>7.4%</td>
            <td>38.6%</td>
            <td>8.3%</td>
        </tr>
        <tr>
            <td>BBH (3-shot)</td>
            <td>33.3%</td>
            <td>35.3%</td>
            <td>37.8%</td>
            <td>43.7%</td>
            <td>25.2%</td>
        </tr>
        <tr>
            <td rowspan="4">CommonSense Understanding</td>
            <td>PIQA (0-shot)</td>
            <td>-</td>
            <td>82.3%</td>
            <td>-</td>
            <td>78.9%</td>
            <td>-</td>
        </tr>
        <tr>
            <td>SciQ (0-shot)</td>
            <td>-</td>
            <td>94.9%</td>
            <td>-</td>
            <td>80.2%</td>
            <td>-</td>
        </tr>
        <tr>
            <td>Winogrande (0-shot)</td>
            <td>-</td>
            <td>64.5%</td>
            <td>-</td>
            <td>-</td>
            <td>-</td>
        </tr>
        <tr>
            <td>OpenbookQA (0-shot)</td>
            <td>-</td>
            <td>34.6%</td>
            <td>-</td>
            <td>46.2%</td>
            <td>-</td>
        </tr>
    </tbody>
</table>


# Citation
If Falcon3 family were helpful to your work, feel free to give us a cite.

```
@misc{Falcon3,
    title = {The Falcon 3 family of Open Models},
    author = {TII Team},
    month = {December},
    year = {2024}
}
```