prem-research
/

Funcdex-0.6B-googledocs

@@ -1,199 +1,573 @@
 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-[More Information Needed]
-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-[More Information Needed]
-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
-## Training Details
-### Training Data
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-[More Information Needed]
-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
-[More Information Needed]
-#### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-[More Information Needed]
-## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
-<!-- This should link to a Dataset Card if possible. -->
-[More Information Needed]
-#### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-[More Information Needed]
-#### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-[More Information Needed]
-### Results
-[More Information Needed]
-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
-[More Information Needed]
-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-## Technical Specifications [optional]
-### Model Architecture and Objective
-[More Information Needed]
-### Compute Infrastructure
-[More Information Needed]
-#### Hardware
-[More Information Needed]
-#### Software
-[More Information Needed]
-## Citation [optional]
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-**BibTeX:**
-[More Information Needed]
-**APA:**
-[More Information Needed]
-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-[More Information Needed]
-## More Information [optional]
-[More Information Needed]
-## Model Card Authors [optional]
-[More Information Needed]
-## Model Card Contact
-[More Information Needed]

 ---
 library_name: transformers
+license: mit
+task_categories:
+- text-generation
+language:
+- en
+tags:
+- agent
+- Agentic Learning
+- tool use
+- BFCL
 ---
+[![Funcdex-Collection](https://img.shields.io/badge/Hugging%20Face-Model-yellow?logo=huggingface)](https://huggingface.co/collections/prem-research/funcdex) [![Dataset](https://img.shields.io/badge/Hugging%20Face-Dataset-yellow?logo=huggingface)](https://huggingface.co/datasets/prem-research/Funcdex-MT-Function-Calling) [![GitHub](https://img.shields.io/badge/GitHub-Code-181717?logo=github)](https://github.com/prem-research/Funcdex-Synthesizer) [![PremAI](https://img.shields.io/badge/Project-PremAI-green)](https://www.premai.io/)
+# Funcdex-0.6B-googledocs
+<div align="center">
+  <img src="assets/funcdex_hero.png" alt="Funcdex Hero" width="70%">
+</div>
+Funcdex-0.6B is a research preview model by Prem Labs. It has been trained on a mix of [Funcdex-MT-Function-Calling](https://huggingface.co/datasets/prem-research/Funcdex-MT-Function-Calling), Instruct-Following, Single-turn function datasets. It is a LoRA finetune of Qwen3-0.6B (with thinking disabled).
+This model excels at Multi-turn Function Calling with tools from `googledocs`.
+The code used to generate the dataset can be found [here](https://github.com/prem-research/Funcdex-Synthesizer).
+# Evaluation
+<div align="center">
+  <img src="assets/line_plot.png" alt="Line Plot" width="80%">
+</div>
+## Results
+### BFCL v3
+- We filtered BFCLv3 examples relevant to the toolkits/bundles and report performance:
+- The filtered set is only 83 examples. Further emphasizing the need for workflow/toolkit-specialized workflows.
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: center;">
+      <th>LLM</th>
+      <th>Acc %</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr style="text-align: center;">
+      <td>GPT-5 Mini<br>(medium)</td>
+      <td>0.71</td>
+    </tr>
+    <tr style="text-align: center;">
+      <td>Qwen3-1.7B</td>
+      <td>0.82</td>
+    </tr>
+    <tr style="text-align: center;">
+      <td><strong><a href="https://huggingface.co/prem-research/Funcdex-1.7B">Funcdex-1.7B</a><strong></td>
+      <td><strong>0.86</strong></td>
+    </tr>
+  </tbody>
+</table>
+### Funcdex-MT: Overall Performance
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: center;">
+      <th>LLM</th>
+      <th>Exact Match</th>
+      <th>String Ratio</th>
+      <th>Total Cost ($)</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr style="text-align: center;">
+      <td>GPT-OSS-120B<br>(medium)</td>
+      <td>0.35</td>
+      <td>0.51</td>
+      <td>9.32</td>
+    </tr>
+    <tr style="text-align: center;">
+      <td>GPT-5 Mini<br>(medium)</td>
+      <td>0.35</td>
+      <td>0.58</td>
+      <td>99.71</td>
+    </tr>
+    <tr style="text-align: center;">
+      <td>GPT-5<br>(minimal)</td>
+      <td>0.18</td>
+      <td>0.59</td>
+      <td>205.45</td>
+    </tr>
+    <tr style="text-align: center;">
+      <td>Qwen3-0.6B</td>
+      <td>0.27</td>
+      <td>0.59</td>
+      <td>2.83</td>
+    </tr>
+    <tr style="text-align: center;">
+      <td>Qwen3-1.7B</td>
+      <td>0.27</td>
+      <td>0.69</td>
+      <td>5.73</td>
+    </tr>
+    <tr style="text-align: center;">
+      <td><strong><a href="https://huggingface.co/collections/prem-research/funcdex">Funcdex-0.6B</a></strong></td>
+      <td><strong>0.39</strong></td>
+      <td><strong>0.70</strong></td>
+      <td><strong>0.19</strong></td>
+    </tr>
+    <tr style="text-align: center;">
+      <td><strong><a href="https://huggingface.co/prem-research/Funcdex-1.7B">Funcdex-1.7B</a></strong></td>
+      <td><strong>0.43</strong></td>
+      <td><strong>0.81</strong></td>
+      <td>5.64</td>
+    </tr>
+  </tbody>
+</table>
+### Funcdex-MT: Toolkit-Level Performance
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: center;">
+      <th rowspan="2">Toolkit</th>
+      <th colspan="2">GPT-OSS-120B<br>(medium)</th>
+      <th colspan="2">GPT-5<br>(minimal)</th>
+      <th colspan="2">GPT-5 Mini<br>(medium)</th>
+      <th colspan="2">Qwen3-0.6B</th>
+      <th colspan="3">Funcdex-0.6B</th>
+      <th colspan="2">Qwen3-1.7B</th>
+      <th colspan="3">Funcdex-1.7B</th>
+    </tr>
+    <tr style="text-align: center;">
+      <th>EM</th>
+      <th>SR</th>
+      <th>EM</th>
+      <th>SR</th>
+      <th>EM</th>
+      <th>SR</th>
+      <th>EM</th>
+      <th>SR</th>
+      <th>EM</th>
+      <th>SR</th>
+      <th>LoRA Checkpoint</th>
+      <th>EM</th>
+      <th>SR</th>
+      <th>EM</th>
+      <th>SR</th>
+      <th>LoRA Checkpoint</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr style="text-align: center;">
+      <td><img src="assets/icons/asana.png" width="20" height="20" style="vertical-align: middle;"/> Asana</td>
+      <td>0.38</td>
+      <td>0.47</td>
+      <td>0.12</td>
+      <td>0.68</td>
+      <td>0.49</td>
+      <td>0.71</td>
+      <td>0.33</td>
+      <td>0.63</td>
+      <td>0.46</td>
+      <td>0.69</td>
+      <td><a href="https://huggingface.co/prem-research/Funcdex-0.6B-asana">🤗</a></td>
+      <td>0.30</td>
+      <td>0.79</td>
+      <td>0.52</td>
+      <td>0.82</td>
+      <td rowspan="10"><a href="https://huggingface.co/prem-research/Funcdex-1.7B">🤗</a></td>
+    </tr>
+    <tr style="text-align: center;">
+      <td><img src="assets/icons/calendly.png" width="20" height="20" style="vertical-align: middle;"/> Calendly</td>
+      <td>0.47</td>
+      <td>0.56</td>
+      <td>0.41</td>
+      <td>0.63</td>
+      <td>0.41</td>
+      <td>0.56</td>
+      <td>0.44</td>
+      <td>0.66</td>
+      <td>0.54</td>
+      <td>0.78</td>
+      <td><a href="https://huggingface.co/prem-research/Funcdex-0.6B-calendly">🤗</a></td>
+      <td>0.47</td>
+      <td>0.74</td>
+      <td>0.54</td>
+      <td>0.86</td>
+    </tr>
+    <tr style="text-align: center;">
+      <td><img src="assets/icons/gmail.png" width="20" height="20" style="vertical-align: middle;"/> Gmail</td>
+      <td>0.48</td>
+      <td>0.70</td>
+      <td>0.24</td>
+      <td>0.69</td>
+      <td>0.50</td>
+      <td>0.73</td>
+      <td>0.27</td>
+      <td>0.61</td>
+      <td>0.47</td>
+      <td>0.72</td>
+      <td><a href="https://huggingface.co/prem-research/Funcdex-0.6B-gmail">🤗</a></td>
+      <td>0.31</td>
+      <td>0.73</td>
+      <td>0.53</td>
+      <td>0.83</td>
+    </tr>
+    <tr style="text-align: center;">
+      <td><img src="assets/icons/google-calendar.png" width="20" height="20" style="vertical-align: middle;"/> Calendar</td>
+      <td>0.27</td>
+      <td>0.52</td>
+      <td>0.20</td>
+      <td>0.50</td>
+      <td>0.21</td>
+      <td>0.51</td>
+      <td>0.21</td>
+      <td>0.53</td>
+      <td>0.39</td>
+      <td>0.74</td>
+      <td><a href="https://huggingface.co/prem-research/Funcdex-0.6B-googlecalendar">🤗</a></td>
+      <td>0.23</td>
+      <td>0.64</td>
+      <td>0.47</td>
+      <td>0.83</td>
+    </tr>
+    <tr style="text-align: center;">
+      <td><img src="assets/icons/docs.png" width="20" height="20" style="vertical-align: middle;"/> Docs</td>
+      <td>0.19</td>
+      <td>0.38</td>
+      <td>0.07</td>
+      <td>0.49</td>
+      <td>0.18</td>
+      <td>0.46</td>
+      <td>0.07</td>
+      <td>0.58</td>
+      <td>0.13</td>
+      <td>0.64</td>
+      <td><a href="https://huggingface.co/prem-research/Funcdex-0.6B-googledocs">🤗</a></td>
+      <td>0.11</td>
+      <td>0.62</td>
+      <td>0.18</td>
+      <td>0.79</td>
+    </tr>
+    <tr style="text-align: center;">
+      <td><img src="assets/icons/google-drive.png" width="20" height="20" style="vertical-align: middle;"/> Drive</td>
+      <td>0.34</td>
+      <td>0.52</td>
+      <td>0.19</td>
+      <td>0.61</td>
+      <td>0.38</td>
+      <td>0.58</td>
+      <td>0.26</td>
+      <td>0.65</td>
+      <td>0.40</td>
+      <td>0.75</td>
+      <td><a href="https://huggingface.co/prem-research/Funcdex-0.6B-googledrive">🤗</a></td>
+      <td>0.26</td>
+      <td>0.73</td>
+      <td>0.48</td>
+      <td>0.82</td>
+    </tr>
+    <tr style="text-align: center;">
+      <td><img src="assets/icons/jira.png" width="20" height="20" style="vertical-align: middle;"/> Jira</td>
+      <td>0.47</td>
+      <td>0.53</td>
+      <td>0.17</td>
+      <td>0.65</td>
+      <td>0.47</td>
+      <td>0.66</td>
+      <td>0.51</td>
+      <td>0.69</td>
+      <td>0.58</td>
+      <td>0.76</td>
+      <td><a href="https://huggingface.co/prem-research/Funcdex-0.6B-jira">🤗</a></td>
+      <td>0.47</td>
+      <td>0.76</td>
+      <td>0.59</td>
+      <td>0.83</td>
+    </tr>
+    <tr style="text-align: center;">
+      <td><img src="assets/icons/stripe.png" width="20" height="20" style="vertical-align: middle;"/> Stripe</td>
+      <td>0.15</td>
+      <td>0.37</td>
+      <td>0.10</td>
+      <td>0.46</td>
+      <td>0.12</td>
+      <td>0.39</td>
+      <td>0.08</td>
+      <td>0.50</td>
+      <td>0.17</td>
+      <td>0.71</td>
+      <td><a href="https://huggingface.co/prem-research/Funcdex-0.6B-stripe">🤗</a></td>
+      <td>0.09</td>
+      <td>0.56</td>
+      <td>0.16</td>
+      <td>0.80</td>
+    </tr>
+    <tr style="text-align: center;">
+      <td><img src="assets/icons/to-do-list.png" width="20" height="20" style="vertical-align: middle;"/> Todoist</td>
+      <td>0.65</td>
+      <td>0.74</td>
+      <td>0.19</td>
+      <td>0.72</td>
+      <td>0.64</td>
+      <td>0.79</td>
+      <td>0.57</td>
+      <td>0.87</td>
+      <td>0.65</td>
+      <td>0.88</td>
+      <td><a href="https://huggingface.co/prem-research/Funcdex-0.6B-todoist">🤗</a></td>
+      <td>0.55</td>
+      <td>0.91</td>
+      <td>0.72</td>
+      <td>0.94</td>
+    </tr>
+    <tr style="text-align: center;">
+      <td><img src="assets/icons/whatsapp.png" width="20" height="20" style="vertical-align: middle;"/> Whatsapp</td>
+      <td>0.23</td>
+      <td>0.39</td>
+      <td>0.13</td>
+      <td>0.47</td>
+      <td>0.24</td>
+      <td>0.43</td>
+      <td>0.20</td>
+      <td>0.43</td>
+      <td>0.28</td>
+      <td>0.64</td>
+      <td><a href="https://huggingface.co/prem-research/Funcdex-0.6B-whatsapp">🤗</a></td>
+      <td>0.26</td>
+      <td>0.55</td>
+      <td>0.31</td>
+      <td>0.71</td>
+    </tr>
+  </tbody>
+</table>
+- Funcdex-0.6B are specialized models. Reported number is the average performance of each specific model in their respective subset.
+### Funcdex-MT: Bundle/Multi-toolkit Performance:
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: center;">
+      <th rowspan="2">Bundle</th>
+      <th colspan="2">GPT-OSS-120B<br>(medium)</th>
+      <th colspan="2">GPT-5<br>(minimal)</th>
+      <th colspan="2">GPT-5 Mini<br>(medium)</th>
+      <th colspan="2">Qwen3-0.6B</th>
+      <th colspan="3">Funcdex-0.6B</th>
+      <th colspan="2">Qwen3-1.7B</th>
+      <th colspan="3">Funcdex-1.7B</th>
+    </tr>
+    <tr style="text-align: center;">
+      <th>EM</th>
+      <th>SR</th>
+      <th>EM</th>
+      <th>SR</th>
+      <th>EM</th>
+      <th>SR</th>
+      <th>EM</th>
+      <th>SR</th>
+      <th>EM</th>
+      <th>SR</th>
+      <th>LoRA Checkpoint</th>
+      <th>EM</th>
+      <th>SR</th>
+      <th>EM</th>
+      <th>SR</th>
+      <th>LoRA Checkpoint</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr style="text-align: center;">
+      <td><img src="assets/icons/gmail.png" width="20" height="20" style="vertical-align: middle;"/>Gmail<img src="assets/icons/google-calendar.png" width="20" height="20" style="vertical-align: middle;"/>Calendar</td>
+      <td>0.28</td>
+      <td>0.53</td>
+      <td>0.15</td>
+      <td>0.54</td>
+      <td>0.22</td>
+      <td>0.56</td>
+      <td>0.19</td>
+      <td>0.51</td>
+      <td>0.26</td>
+      <td>0.54</td>
+      <td><a href="https://huggingface.co/prem-research/Funcdex-0.6B-gmail_googlecalendar">🤗</a></td>
+      <td>0.17</td>
+      <td>0.61</td>
+      <td>0.32</td>
+      <td>0.71</td>
+      <td rowspan="5"><a href="https://huggingface.co/prem-research/Funcdex-1.7B">🤗</a></td>
+    </tr>
+    <tr style="text-align: center;">
+      <td><img src="assets/icons/google-drive.png" width="20" height="20" style="vertical-align: middle;"/>Drive <img src="assets/icons/calendly.png" width="20" height="20" style="vertical-align: middle;"/> Calendly <img src="assets/icons/google-calendar.png" width="20" height="20" style="vertical-align: middle;"/> Calendar</td>
+      <td>0.32</td>
+      <td>0.45</td>
+      <td>0.17</td>
+      <td>0.52</td>
+      <td>0.35</td>
+      <td>0.47</td>
+      <td>0.19</td>
+      <td>0.49</td>
+      <td>0.35</td>
+      <td>0.60</td>
+      <td><a href="https://huggingface.co/prem-research/Funcdex-0.6B-googledrive_calendly_googlecalendar">🤗</a></td>
+      <td>0.15</td>
+      <td>0.66</td>
+      <td>0.40</td>
+      <td>0.78</td>
+    </tr>
+    <tr style="text-align: center;">
+      <td><img src="assets/icons/google-drive.png" width="20" height="20" style="vertical-align: middle;"/>Drive <img src="assets/icons/docs.png" width="20" height="20" style="vertical-align: middle;"/> Docs</td>
+      <td>0.28</td>
+      <td>0.37</td>
+      <td>0.12</td>
+      <td>0.50</td>
+      <td>0.33</td>
+      <td>0.47</td>
+      <td>0.18</td>
+      <td>0.54</td>
+      <td>0.34</td>
+      <td>0.70</td>
+      <td><a href="https://huggingface.co/prem-research/Funcdex-0.6B-googledrive_googledocs">🤗</a></td>
+      <td>0.19</td>
+      <td>0.68</td>
+      <td>0.43</td>
+      <td>0.76</td>
+    </tr>
+    <tr style="text-align: center;">
+      <td><img src="assets/icons/jira.png" width="20" height="20" style="vertical-align: middle;"/>Jira <img src="assets/icons/gmail.png" width="20" height="20" style="vertical-align: middle;"/> Gmail</td>
+      <td>0.42</td>
+      <td>0.60</td>
+      <td>0.18</td>
+      <td>0.66</td>
+      <td>0.36</td>
+      <td>0.66</td>
+      <td>0.29</td>
+      <td>0.61</td>
+      <td>0.39</td>
+      <td>0.71</td>
+      <td><a href="https://huggingface.co/prem-research/Funcdex-0.6B-jira_gmail">🤗</a></td>
+      <td>0.28</td>
+      <td>0.72</td>
+      <td>0.44</td>
+      <td>0.82</td>
+    </tr>
+    <tr style="text-align: center;">
+      <td><img src="assets/icons/whatsapp.png" width="20" height="20" style="vertical-align: middle;"/>Whatsapp <img src="assets/icons/to-do-list.png" width="20" height="20" style="vertical-align: middle;"/> Todoist</td>
+      <td>0.32</td>
+      <td>0.58</td>
+      <td>0.19</td>
+      <td>0.66</td>
+      <td>0.35</td>
+      <td>0.69</td>
+      <td>0.26</td>
+      <td>0.50</td>
+      <td>0.41</td>
+      <td>0.70</td>
+      <td><a href="https://huggingface.co/prem-research/Funcdex-0.6B-whatsapp_todoist">🤗</a></td>
+      <td>0.27</td>
+      <td>0.68</td>
+      <td>0.39</td>
+      <td>0.77</td>
+    </tr>
+  </tbody>
+</table>
+## Inference
+- Given a conversation, we extract all tuples `(context_messages, function_calls)` and use it to generate predictions. We ignore the `content` field and only evaluate `function_calls` generated by an LLM.
+- We use vLLM deployment with `tool_choice="auto"`.
+## Metrics
+Given a list of predicted and reference function calls, we report two metrics:
+- **Function Call String Match (SR)**: We perform greedy match and report best-matched string ratio using `difflib.SequenceMatcher.ratio`. The number reported is average string ratio.
+- **Exact Match (EM)**: Same as above, but we perform exact string match instead. The number reported is EM F1 Score.
+EM is a strict metric, and penalizes string arguments in function calls that may be "okay", e.g. `"email_content": "This is an example."` v/s `"email_content": "This is an Example."`, both only differ by one letter.
+# Quickstart
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+import torch
+import json
+# Load model and tokenizer
+base_model_name = "ojus1/Qwen3-0.6B-Instruct"
+model_name = "prem-research/Funcdex-0.6B-googledocs"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+base_model = AutoModelForCausalLM.from_pretrained(
+    base_model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+model = PeftModel.from_pretrained(
+    base_model,
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+# Define tools
+tools = [
+    {
+        "type": "function",
+        "function": {
+            "name": "CREATE_HEADER",
+            "description": "Create a header in a Google Doc",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "documentId": {"type": "string", "description": "Document ID"},
+                    "createHeader": {"type": "object", "description": "Header configuration"}
+                },
+                "required": ["documentId", "createHeader"]
+            }
+        }
+    },
+    {
+        "type": "function",
+        "function": {
+            "name": "COPY_GOOGLE_DOCUMENT",
+            "description": "Copy a Google Document",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "document_id": {"type": "string", "description": "Source document ID"},
+                    "title": {"type": "string", "description": "Title for the copy"}
+                },
+                "required": ["document_id", "title"]
+            }
+        }
+    }
+]
+# Define conversation
+messages = [
+    {"role": "system", "content": "You are a helpful assistant that can help with tasks by using tools."},
+    {"role": "user", "content": "Create a header in document '9z8y7x6w5v4u3t2s1r0q'."}
+]
+# Apply chat template with tools
+formatted_input = tokenizer.apply_chat_template(
+    messages,
+    tools=tools,
+    tokenize=False,
+    add_generation_prompt=True
+)
+# Tokenize and generate
+input_tokens = tokenizer(formatted_input, return_tensors="pt").to(model.device)
+output = model.generate(**input_tokens, max_new_tokens=256, do_sample=False)
+response = tokenizer.decode(output[0][input_tokens['input_ids'].shape[1]:], skip_special_tokens=True)
+print("Response:", response)
+```
+For best results, provide detailed system-prompt to steer the tool-use behaviour.
+# License
+The models, code and the dataset are licensed under MIT License.