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README.md

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+---
+language:
+- en
+- fr
+- es
+- pt
+tags:
+- falcon3
+base_model: tiiuae/Falcon3-7B-Base
+license: other
+license_name: falcon-llm-license
+license_link: https://falconllm.tii.ae/falcon-terms-and-conditions.html
+library_name: transformers
+---
+### exl2 quant (measurement.json in main branch)
+---
+### check revisions for quants
+---
+
+
+<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](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
+
+
+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](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="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}
+}
+```