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Falcon3-Mamba-7B-Instruct

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  2. README.md +217 -0
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  10. tokenizer.json +0 -0
  11. tokenizer_config.json +130 -0
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README.md ADDED
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1
+ ---
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+ language:
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+ - en
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+ tags:
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+ - falcon3
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+ - falcon3_mamba
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+ - falcon_mamba
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+ base_model:
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+ - tiiuae/Falcon3-Mamba-7B-Base
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+ ---
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+
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+ # Falcon3-Mamba-7B-Instruct
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+
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+ **Falcon3** family of Open Foundation Models is a set of pretrained and instruct LLMs ranging from 1B to 10B.
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+
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+ 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.
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+ Falcon3-Mamba-7B-Instruct supports a context length up to 32K and was mainly trained on english corpus.
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+
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+ ## Model Details
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+ - Architecture (same as [Falcon-Mamba-7b](https://huggingface.co/tiiuae/falcon-mamba-7b))
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+ - Mamba1 based causal decoder only architecture trained on a causal language modeling task (i.e., predict the next token).
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+ - 64 decoder blocks
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+ - width: 4096
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+ - state_size: 16
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+ - 32k context length
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+ - 65k vocab size
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+ - Continue Pretrained from [Falcon Mamba 7B](https://huggingface.co/tiiuae/falcon-mamba-7b), with another 1500 Gigatokens of data comprising of web, code, STEM and high quality data.
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+ - Postrained on 1.2 million samples of STEM, conversations, code, and safety.
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+ - Developed by [Technology Innovation Institute](https://www.tii.ae)
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+ - License: TII Falcon-LLM License 2.0
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+ - Model Release Date: December 2024
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+
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+
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+ ## Getting started
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+
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+ <details>
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+ <summary> Click to expand </summary>
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+
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+ ```python
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+ from transformers import AutoTokenizer, AutoModelForCausalLM
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+
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+
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+ from transformers import AutoModelForCausalLM, AutoTokenizer
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+
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+ model_name = "tiiuae/Falcon3-Mamba-7B-Instruct"
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+
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+ model = AutoModelForCausalLM.from_pretrained(
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+ model_name,
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+ torch_dtype="auto",
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+ device_map="auto"
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+ )
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+ tokenizer = AutoTokenizer.from_pretrained(model_name)
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+
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+ prompt = "How many hours in one day?"
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+ messages = [
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+ {"role": "system", "content": "You are a helpful friendly assistant Falcon3 from TII, try to follow instructions as much as possible."},
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+ {"role": "user", "content": prompt}
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+ ]
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+ text = tokenizer.apply_chat_template(
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+ messages,
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+ tokenize=False,
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+ add_generation_prompt=True
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+ )
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+ model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
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+
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+ generated_ids = model.generate(
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+ **model_inputs,
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+ max_new_tokens=1024
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+ )
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+ generated_ids = [
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+ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
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+ ]
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+
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+ response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
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+ print(response)
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+ ```
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+
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+ </details>
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+
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+ <br>
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+
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+ # Benchmarks
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+ We report in the following table our internal pipeline benchmarks. For the benchmarks marked by star, we normalize the results with HuggingFace score normalization:
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+
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+ <table border="1" style="width: 100%; text-align: center; border-collapse: collapse;">
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+ <colgroup>
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+ <col style="width: 10%;">
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+ <col style="width: 10%;">
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+ <col style="width: 7%;">
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+ <col style="width: 7%;">
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+ <col style="width: 7%;">
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+ <col style="background-color: rgba(80, 15, 213, 0.5); width: 7%;">
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+ </colgroup>
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+ <thead>
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+ <tr>
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+ <th>Category</th>
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+ <th>Benchmark</th>
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+ <th>Zamba2-7B-instruct</th>
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+ <th>Jamba-1.5-Mini</th>
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+ <th>Llama-3.1-8B-Instruct</th>
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+ <th>Falcon3-Mamba-7B-Instruct</th>
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+ </tr>
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+ </thead>
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+ <tbody>
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+ <tr>
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+ <td rowspan="3">General</td>
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+ <td>MMLU (5-shot)</td>
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+ <td>-</td>
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+ <td>68.7%</td>
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+ <td>55.9%</td>
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+ <td>65.3%</td>
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+ </tr>
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+ <tr>
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+ <td>MMLU-PRO (5-shot)*</td>
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+ <td>32.4%</td>
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+ <td>31.6%</td>
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+ <td>21.8%</td>
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+ <td>26.3%</td>
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+ </tr>
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+ <tr>
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+ <td>IFEval</td>
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+ <td>69.9%</td>
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+ <td>65.7%</td>
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+ <td>78.8%</td>
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+ <td>71.7%</td>
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+ </tr>
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+ <tr>
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+ <td rowspan="2">Math</td>
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+ <td>GSM8K (5-shot)</td>
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+ <td>-</td>
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+ <td>74.9%</td>
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+ <td>19.2%</td>
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+ <td>65.2%</td>
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+ </tr>
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+ <tr>
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+ <td>MATH Lvl-5 (4-shot)</td>
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+ <td>-</td>
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+ <td>6.9%</td>
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+ <td>10.4%</td>
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+ <td>27.3%</td>
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+ </tr>
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+ <tr>
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+ <td rowspan="4">Reasoning</td>
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+ <td>Arc Challenge (25-shot)</td>
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+ <td>-</td>
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+ <td>54.3%</td>
147
+ <td>46.6%</td>
148
+ <td>53.7%</td>
149
+ </tr>
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+ <tr>
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+ <td>GPQA (0-shot)*</td>
152
+ <td>10.3%</td>
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+ <td>11.1%</td>
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+ <td>33.6%</td>
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+ <td>7.2%</td>
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+ </tr>
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+ <tr>
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+ <td>MUSR (0-shot)*</td>
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+ <td>8.2%</td>
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+ <td>12.2%</td>
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+ <td>38.6%</td>
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+ <td>8.3%</td>
163
+ </tr>
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+ <tr>
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+ <td>BBH (3-shot)*</td>
166
+ <td>33.3%</td>
167
+ <td>35.3%</td>
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+ <td>43.7%</td>
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+ <td>25.2%</td>
170
+ </tr>
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+ <tr>
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+ <td rowspan="4">CommonSense Understanding</td>
173
+ <td>PIQA (0-shot)</td>
174
+ <td>-</td>
175
+ <td>82.3%</td>
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+ <td>78.9%</td>
177
+ <td>80.9%</td>
178
+ </tr>
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+ <tr>
180
+ <td>SciQ (0-shot)</td>
181
+ <td>-</td>
182
+ <td>94.9%</td>
183
+ <td>80.2%</td>
184
+ <td>93.6%</td>
185
+ </tr>
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+ <tr>
187
+ <td>Winogrande (0-shot)</td>
188
+ <td>-</td>
189
+ <td>64.5%</td>
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+ <td>-</td>
191
+ <td>-</td>
192
+ </tr>
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+ <tr>
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+ <td>OpenbookQA (0-shot)</td>
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+ <td>-</td>
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+ <td>34.6%</td>
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+ <td>46.2%</td>
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+ <td>47.2%</td>
199
+ </tr>
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+ </tbody>
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+ </table>
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+
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+ ## Useful links
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+ - View our [release blogpost](https://huggingface.co/blog/falcon3).
205
+ - Feel free to join [our discord server](https://discord.gg/fwXpMyGc) if you have any questions or to interact with our researchers and developers.
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+
207
+ ## Citation
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+ If the Falcon3 family of models were helpful to your work, feel free to give us a cite.
209
+
210
+ ```
211
+ @misc{Falcon3,
212
+ title = {The Falcon 3 Family of Open Models},
213
+ author = {Falcon-LLM Team},
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+ month = {December},
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+ year = {2024}
216
+ }
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+ ```
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+ "chat_template": "{{bos_token}}{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}",
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+ "clean_up_tokenization_spaces": true,
117
+ "eos_token": "<|end_of_text|>",
118
+ "extra_special_tokens": {},
119
+ "max_length": null,
120
+ "model_input_names": [
121
+ "input_ids",
122
+ "attention_mask"
123
+ ],
124
+ "model_max_length": 1000000000000000019884624838656,
125
+ "pad_to_multiple_of": null,
126
+ "pad_token": ">>TITLE<<",
127
+ "pad_token_type_id": 0,
128
+ "padding_side": "left",
129
+ "tokenizer_class": "PreTrainedTokenizerFast"
130
+ }