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README.md
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# Falcon3-3B-Instruct_a13975362023845888371315
---
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
---
Falcon3-3B-Instruct
<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}
}
```

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{
"architectures": [
"LlamaForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
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"head_dim": 256,
"hidden_act": "silu",
"hidden_size": 3072,
"initializer_range": 0.02,
"intermediate_size": 9216,
"max_position_embeddings": 32768,
"mlp_bias": false,
"model_type": "llama",
"num_attention_heads": 12,
"num_hidden_layers": 22,
"num_key_value_heads": 4,
"pretraining_tp": 1,
"rms_norm_eps": 1e-06,
"rope_scaling": null,
"rope_theta": 1000042,
"tie_word_embeddings": false,
"torch_dtype": "bfloat16",
"transformers_version": "4.46.1",
"use_cache": true,
"vocab_size": 131072
}

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"_from_model_config": true,
"bos_token_id": 11,
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}

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