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tiiuae/Falcon3-Mamba-7B-Instruct

sourceHugging Faceotherupdated 1y agoView on Hugging Face
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<div align="center"> <img src="https://huggingface.co/datasets/tiiuae/documentation-images/resolve/main/falcon_mamba/falcon-mamba-logo.png" alt="drawing" width="500"/> </div>

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 was mainly trained on english corpus.

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
  • —Continue Pretrained from Falcon-Mamba-7b, with another 1500 Gigatokens of data consisting 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
  • —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. For the benchmarks marked by star, we normalize the results with HuggingFace score normalization:

<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>Zamba2-7B-instruct</th> <th>Jamba-1.5-Mini</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>30.6</td> <td>68.7</td> <td>55.9</td> <td>65.3</td> </tr> <tr> <td>MMLU-PRO (5-shot)</td> <td>32.4</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>78.8</td> <td>71.7</td> </tr> <tr> <td rowspan="2">Math</td> <td>GSM8K (5-shot)</td> <td>0</td> <td>74.9</td> <td>19.2</td> <td>65.2</td> </tr> <tr> <td>MATH Lvl-5 (4-shot)</td> <td>13.6</td> <td>6.9</td> <td>10.4</td> <td>27.3</td> </tr> <tr> <td rowspan="4">Reasoning</td> <td>Arc Challenge (25-shot)</td> <td>54</td> <td>54.3</td> <td>46.6</td> <td>53.7</td> </tr> <tr> <td>GPQA (0-shot)</td> <td>10.3</td> <td>11.1</td> <td>6.2</td> <td>7.2</td> </tr> <tr> <td>MUSR (0-shot)</td> <td>8.2</td> <td>12.2</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>43.7</td> <td>25.2</td> </tr> <tr> <td rowspan="4">CommonSense Understanding</td> <td>PIQA (0-shot)</td> <td>75.6</td> <td>82.3</td> <td>78.9</td> <td>80.9</td> </tr> <tr> <td>SciQ (0-shot)</td> <td>29.2</td> <td>94.9</td> <td>80.2</td> <td>93.6</td> </tr> <tr> <td>OpenbookQA (0-shot)</td> <td>45.6</td> <td>45.8</td> <td>46.2</td> <td>47.2</td> </tr> </tbody> </table>

Useful links

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},
    author = {Falcon-LLM Team},
    month = {December},
    year = {2024}
}