tiiuae_Falcon3-7B-Instruct-exl2

exl2 quant (measurement.json in main branch)

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<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

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

This repository contains the Falcon3-7B-Instruct. It achieves state of art results (at the time of release) on reasoning, language understanding, instruction following, code and mathematics tasks. Falcon3-7B-Instruct supports 4 languages (english, french, spanish, portuguese) and a context length up to 32K.

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
• Postrained on 1.2 million samples of STEM, conversations, code, safety and function call data
• Supports EN, FR, ES, PT
• Developed by Technology Innovation Institute
• License: TII Falcon-LLM License 2.0
• Model Release Date: December 2024

Getting started

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

from transformers import AutoTokenizer, AutoModelForCausalLM


from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "tiiuae/Falcon3-7B-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.

• We use 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="background-color: rgba(80, 15, 213, 0.5); width: 7%;"> </colgroup> <thead> <tr> <th>Category</th> <th>Benchmark</th> <th>Llama-3.1-8B-Instruct</th> <th>Qwen2.5-7B-Instruct</th> <th>Falcon3-7B-Instruct</th> </tr> </thead> <tbody> <tr> <td rowspan="3">General</td> <td>MMLU (5-shot)</td> <td>55.9</td> <td><b>72.4</b></td> <td>68</td> </tr> <tr> <td>MMLU-PRO (5-shot)</td> <td>21.8</td> <td>35.8</td> <td><b>40.7</b></td> </tr> <tr> <td>IFEval</td> <td><b>78.8</b></td> <td>74.7</td> <td>76.5</td> </tr> <tr> <td rowspan="3">Math</td> <td>GSM8K (5-shot)</td> <td>78.1</td> <td>77.5</td> <td><b>79.1</b></td> </tr> <tr> <td>GSM8K (8-shot, COT)</td> <td>79.8</td> <td>72.7</td> <td><b>80.9</b></td> </tr> <tr> <td>MATH Lvl-5 (4-shot)</td> <td>10.4</td> <td>26</td> <td><b>29.4</b></td> </tr> <tr> <td rowspan="5">Reasoning</td> <td>Arc Challenge (25-shot)</td> <td>46.6</td> <td>55.7</td> <td><b>65.9</b></td> </tr> <tr> <td>GPQA (0-shot)</td> <td><b>33.6</b></td> <td>31.9</td> <td>32</td> </tr> <tr> <td>GPQA (0-shot, COT)</td> <td>9.6</td> <td>13.8</td> <td><b>22.3</b></td> </tr> <tr> <td>MUSR (0-shot)</td> <td>38.6</td> <td>40.7</td> <td><b>46.4</b></td> </tr> <tr> <td>BBH (3-shot)</td> <td>43.7</td> <td><b>53.9</b></td> <td>52.4</td> </tr> <tr> <td rowspan="4">CommonSense Understanding</td> <td>PIQA (0-shot)</td> <td><b>78.9</b></td> <td>73.7</td> <td>78.8</td> </tr> <tr> <td>SciQ (0-shot)</td> <td>80.2</td> <td>50.9</td> <td><b>94.7</b></td> </tr> <tr> <td>Winogrande (0-shot)</td> <td>-</td> <td>-</td> <td>70.4</td> </tr> <tr> <td>OpenbookQA (0-shot)</td> <td><b>46.2</b></td> <td>42.4</td> <td>45.8</td> </tr> <tr> <td rowspan="2">Instructions following</td> <td>MT-Bench (avg)</td> <td>7.9</td> <td><b>8.5</b></td> <td>8.4</td> </tr> <tr> <td>Alpaca (WC)</td> <td>26.6</td> <td><b>31.5</b></td> <td>26.1</td> </tr> <tr> <td>Tool use</td> <td>BFCL AST (avg)</td> <td>90.6</td> <td><b>91.4</b></td> <td>72.3</td> </tr> </tbody> </table>

Technical Report

Coming soon....

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}
}