README.md

---
language:
- en
- fr
- es
- pt
tags:
- falcon3
base_model: tiiuae/Falcon3-7B-Instruct
license: other 
license_name: falcon-llm-license 
license_link: https://falconllm.tii.ae/falcon-terms-and-conditions.html
---

<div align="center">
    <img src="https://huggingface.co/datasets/tiiuae/documentation-images/resolve/main/general/falco3-logo.png" alt="drawing" width="500"/>
</div>

# Falcon3-7B-Instruct-AWQ

**Falcon3** family of Open Foundation Models is a set of pretrained and instruct LLMs ranging from 1B to 10B parameters.

**Falcon3-7B-Instruct** achieves state-of-the-art results (at release's time) on reasoning, language understanding, instruction following, code and mathematics tasks.
Falcon3-7B-Instruct supports 4 languages (English, French, Spanish, Portuguese) and a context length of up to 32K.

This repository contains the AWQ-quantized 4-bit instruction-tuned 7B Falcon3 model.

## Model Details

- Architecture
  - Transformer-based causal decoder-only architecture
  - 28 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
- Pretrained on 14 Teratokens 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
- Quantization: AWQ 4-bit


## Getting started

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

```python
from transformers import AutoTokenizer, AutoModelForCausalLM


model_name = "tiiuae/Falcon3-7B-Instruct-AWQ"

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: 10%;">
        <col style="width: 10%;">
        <col style="background-color: rgba(80, 15, 213, 0.5); width: 7%;">
    </colgroup>
    <thead>
        <tr>
            <th>Benchmark</th>
            <th>Falcon 3-7B Instruct</th>
            <th>Falcon 3-7B Instruct-GPTQ-Int4</th>
            <th>Falcon 3-7B Instruct-GPTQ-Int8</th>
            <th>Falcon 3-7B Instruct-AWQ</th>
        </tr>
    </thead>
    <tbody>
        <tr>
            <td>MMLU</td>
            <td>67.7</td>
            <td>65.6</td>
            <td>67.6</td>
            <td>66.4</td>
        </tr>
        <tr>
            <td>MMLU-PRO</td>
            <td>40.9</td>
            <td>39.1</td>
            <td>40.9</td>
            <td>39.9</td>
        </tr>
        <tr>
            <td>IFEval</td>
            <td>75.1</td>
            <td>72.2</td>
            <td>77.0</td>
            <td>74.8</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}
}
```