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--- |
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library_name: transformers |
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license: mit |
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task_categories: |
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- text-generation |
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language: |
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- en |
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tags: |
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- agent |
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- Agentic Learning |
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- tool use |
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- BFCL |
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--- |
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[](https://huggingface.co/collections/prem-research/funcdex) [](https://huggingface.co/datasets/prem-research/Funcdex-MT-Function-Calling) [](https://github.com/prem-research/Funcdex-Synthesizer) [](https://www.premai.io/) |
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# Funcdex-1.7B |
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<div align="center"> |
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<img src="assets/funcdex_hero.png" alt="Funcdex Hero" width="70%"> |
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</div> |
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Funcdex-1.7B 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 Qwen3-1.7B (with thinking disabled). |
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This model excels at Multi-turn Function Calling with tools from `gmail`, `jira`, `calendar`, `docs`, etc. |
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The code used to generate the dataset can be found [here](https://github.com/prem-research/Funcdex-Synthesizer). |
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# Evaluation |
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<div align="center"> |
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<img src="assets/line_plot.png" alt="Line Plot" width="80%"> |
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</div> |
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Notes: |
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- *Funcdex-0.6B is the average of performances of individual Funcdex-0.6B models.* |
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- For cost, we track the number of prompt/completion tokens for evaluating 300 conversations. |
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- e.g. If token cost is input=$1 and output=$10 per million tokens, and evaluation needed `0.5M` and `0.1M` input/output tokens, then cost is `1 * 0.5 + 0.1 * 10 = $1.5`. |
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- *Qwen3-0.6B and Qwen3-1.7B evaluation costs are estimated by extrapolating from Llama3.2-3B serverless costs. Other model's costs are sourced from Openrouter.* |
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## Results |
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### BFCL v3 |
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- We filtered BFCLv3 examples relevant to the toolkits/bundles and report performance: |
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- The filtered set is only 83 examples. Further emphasizing the need for workflow/toolkit-specialized workflows. |
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<table border="1" class="dataframe"> |
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<thead> |
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<tr style="text-align: center;"> |
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<th>LLM</th> |
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<th>Acc %</th> |
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</tr> |
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</thead> |
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<tbody> |
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<tr style="text-align: center;"> |
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<td>GPT-5 Mini<br>(medium)</td> |
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<td>0.71</td> |
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</tr> |
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<tr style="text-align: center;"> |
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<td>Qwen3-1.7B</td> |
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<td>0.82</td> |
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</tr> |
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<tr style="text-align: center;"> |
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<td><strong><a href="https://huggingface.co/prem-research/Funcdex-1.7B">Funcdex-1.7B</a><strong></td> |
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<td><strong>0.86</strong></td> |
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</tr> |
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</tbody> |
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</table> |
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### Funcdex-MT: Overall Performance |
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<table border="1" class="dataframe"> |
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<thead> |
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<tr style="text-align: center;"> |
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<th>LLM</th> |
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<th>Exact Match</th> |
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<th>String Ratio</th> |
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<th>Total Cost ($)</th> |
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</tr> |
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</thead> |
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<tbody> |
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<tr style="text-align: center;"> |
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<td>GPT-OSS-120B<br>(medium)</td> |
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<td>0.35</td> |
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<td>0.51</td> |
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<td>9.32</td> |
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</tr> |
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<tr style="text-align: center;"> |
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<td>GPT-5 Mini<br>(medium)</td> |
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<td>0.35</td> |
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<td>0.58</td> |
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<td>99.71</td> |
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</tr> |
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<tr style="text-align: center;"> |
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<td>GPT-5<br>(minimal)</td> |
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<td>0.18</td> |
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<td>0.59</td> |
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<td>205.45</td> |
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</tr> |
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<tr style="text-align: center;"> |
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<td>Qwen3-0.6B</td> |
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<td>0.27</td> |
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<td>0.59</td> |
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<td>2.83</td> |
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</tr> |
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<tr style="text-align: center;"> |
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<td>Qwen3-1.7B</td> |
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<td>0.27</td> |
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<td>0.69</td> |
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<td>5.73</td> |
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</tr> |
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<tr style="text-align: center;"> |
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<td><strong><a href="https://huggingface.co/collections/prem-research/funcdex">Funcdex-0.6B</a></strong></td> |
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<td><strong>0.39</strong></td> |
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<td><strong>0.70</strong></td> |
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<td><strong>0.19</strong></td> |
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</tr> |
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<tr style="text-align: center;"> |
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<td><strong><a href="https://huggingface.co/prem-research/Funcdex-1.7B">Funcdex-1.7B</a></strong></td> |
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<td><strong>0.43</strong></td> |
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<td><strong>0.81</strong></td> |
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<td>5.64</td> |
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</tr> |
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</tbody> |
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</table> |
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### Funcdex-MT: Toolkit-Level Performance |
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<table border="1" class="dataframe"> |
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<thead> |
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<tr style="text-align: center;"> |
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<th rowspan="2">Toolkit</th> |
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<th colspan="2">GPT-OSS-120B<br>(medium)</th> |
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<th colspan="2">GPT-5<br>(minimal)</th> |
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<th colspan="2">GPT-5 Mini<br>(medium)</th> |
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<th colspan="2">Qwen3-0.6B</th> |
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<th colspan="3">Funcdex-0.6B</th> |
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<th colspan="2">Qwen3-1.7B</th> |
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<th colspan="3">Funcdex-1.7B</th> |
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</tr> |
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<tr style="text-align: center;"> |
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<th>EM</th> |
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<th>SR</th> |
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<th>EM</th> |
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<th>SR</th> |
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<th>EM</th> |
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<th>SR</th> |
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<th>EM</th> |
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<th>SR</th> |
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<th>EM</th> |
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<th>SR</th> |
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<th>LoRA Checkpoint</th> |
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<th>EM</th> |
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<th>SR</th> |
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<th>EM</th> |
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<th>SR</th> |
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<th>LoRA Checkpoint</th> |
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</tr> |
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</thead> |
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<tbody> |
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<tr style="text-align: center;"> |
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<td><img src="assets/icons/asana.png" width="20" height="20" style="vertical-align: middle;"/> Asana</td> |
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<td>0.38</td> |
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<td>0.47</td> |
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<td>0.12</td> |
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<td>0.68</td> |
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<td>0.49</td> |
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<td>0.71</td> |
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<td>0.33</td> |
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<td>0.63</td> |
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<td>0.46</td> |
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<td>0.69</td> |
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<td><a href="https://huggingface.co/prem-research/Funcdex-0.6B-asana">🤗</a></td> |
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<td>0.30</td> |
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<td>0.79</td> |
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<td>0.52</td> |
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<td>0.82</td> |
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<td rowspan="10"><a href="https://huggingface.co/prem-research/Funcdex-1.7B">🤗</a></td> |
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</tr> |
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<tr style="text-align: center;"> |
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<td><img src="assets/icons/calendly.png" width="20" height="20" style="vertical-align: middle;"/> Calendly</td> |
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<td>0.47</td> |
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<td>0.56</td> |
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<td>0.41</td> |
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<td>0.63</td> |
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<td>0.41</td> |
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<td>0.56</td> |
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<td>0.44</td> |
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<td>0.66</td> |
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<td>0.54</td> |
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<td>0.78</td> |
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<td><a href="https://huggingface.co/prem-research/Funcdex-0.6B-calendly">🤗</a></td> |
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<td>0.47</td> |
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<td>0.74</td> |
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<td>0.54</td> |
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<td>0.86</td> |
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</tr> |
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<tr style="text-align: center;"> |
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<td><img src="assets/icons/gmail.png" width="20" height="20" style="vertical-align: middle;"/> Gmail</td> |
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<td>0.48</td> |
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<td>0.70</td> |
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<td>0.24</td> |
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<td>0.69</td> |
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<td>0.50</td> |
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<td>0.73</td> |
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<td>0.27</td> |
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<td>0.61</td> |
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<td>0.47</td> |
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<td>0.72</td> |
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<td><a href="https://huggingface.co/prem-research/Funcdex-0.6B-gmail">🤗</a></td> |
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<td>0.31</td> |
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<td>0.73</td> |
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<td>0.53</td> |
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<td>0.83</td> |
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</tr> |
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<tr style="text-align: center;"> |
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<td><img src="assets/icons/google-calendar.png" width="20" height="20" style="vertical-align: middle;"/> Calendar</td> |
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<td>0.27</td> |
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<td>0.52</td> |
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<td>0.20</td> |
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<td>0.50</td> |
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<td>0.21</td> |
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<td>0.51</td> |
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<td>0.21</td> |
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<td>0.53</td> |
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<td>0.39</td> |
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<td>0.74</td> |
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<td><a href="https://huggingface.co/prem-research/Funcdex-0.6B-googlecalendar">🤗</a></td> |
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<td>0.23</td> |
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<td>0.64</td> |
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<td>0.47</td> |
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<td>0.83</td> |
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</tr> |
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<tr style="text-align: center;"> |
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<td><img src="assets/icons/docs.png" width="20" height="20" style="vertical-align: middle;"/> Docs</td> |
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<td>0.19</td> |
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<td>0.38</td> |
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<td>0.07</td> |
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<td>0.49</td> |
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<td>0.18</td> |
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<td>0.46</td> |
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<td>0.07</td> |
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<td>0.58</td> |
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<td>0.13</td> |
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<td>0.64</td> |
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<td><a href="https://huggingface.co/prem-research/Funcdex-0.6B-googledocs">🤗</a></td> |
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<td>0.11</td> |
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<td>0.62</td> |
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<td>0.18</td> |
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<td>0.79</td> |
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</tr> |
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<tr style="text-align: center;"> |
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<td><img src="assets/icons/google-drive.png" width="20" height="20" style="vertical-align: middle;"/> Drive</td> |
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<td>0.34</td> |
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<td>0.52</td> |
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<td>0.19</td> |
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<td>0.61</td> |
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<td>0.38</td> |
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<td>0.58</td> |
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<td>0.26</td> |
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<td>0.65</td> |
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<td>0.40</td> |
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<td>0.75</td> |
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<td><a href="https://huggingface.co/prem-research/Funcdex-0.6B-googledrive">🤗</a></td> |
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<td>0.26</td> |
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<td>0.73</td> |
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<td>0.48</td> |
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<td>0.82</td> |
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</tr> |
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<tr style="text-align: center;"> |
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<td><img src="assets/icons/jira.png" width="20" height="20" style="vertical-align: middle;"/> Jira</td> |
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<td>0.47</td> |
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<td>0.53</td> |
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<td>0.17</td> |
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<td>0.65</td> |
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<td>0.47</td> |
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<td>0.66</td> |
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<td>0.51</td> |
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<td>0.69</td> |
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<td>0.58</td> |
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<td>0.76</td> |
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<td><a href="https://huggingface.co/prem-research/Funcdex-0.6B-jira">🤗</a></td> |
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<td>0.47</td> |
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<td>0.76</td> |
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<td>0.59</td> |
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<td>0.83</td> |
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</tr> |
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<tr style="text-align: center;"> |
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<td><img src="assets/icons/stripe.png" width="20" height="20" style="vertical-align: middle;"/> Stripe</td> |
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<td>0.15</td> |
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<td>0.37</td> |
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<td>0.10</td> |
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<td>0.46</td> |
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<td>0.12</td> |
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<td>0.39</td> |
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<td>0.08</td> |
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<td>0.50</td> |
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<td>0.17</td> |
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<td>0.71</td> |
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<td><a href="https://huggingface.co/prem-research/Funcdex-0.6B-stripe">🤗</a></td> |
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<td>0.09</td> |
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<td>0.56</td> |
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<td>0.16</td> |
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<td>0.80</td> |
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</tr> |
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<tr style="text-align: center;"> |
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<td><img src="assets/icons/to-do-list.png" width="20" height="20" style="vertical-align: middle;"/> Todoist</td> |
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<td>0.65</td> |
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<td>0.74</td> |
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<td>0.19</td> |
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<td>0.72</td> |
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<td>0.64</td> |
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<td>0.79</td> |
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<td>0.57</td> |
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<td>0.87</td> |
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<td>0.65</td> |
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<td>0.88</td> |
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<td><a href="https://huggingface.co/prem-research/Funcdex-0.6B-todoist">🤗</a></td> |
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<td>0.55</td> |
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<td>0.91</td> |
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<td>0.72</td> |
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<td>0.94</td> |
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</tr> |
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<tr style="text-align: center;"> |
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<td><img src="assets/icons/whatsapp.png" width="20" height="20" style="vertical-align: middle;"/> Whatsapp</td> |
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<td>0.23</td> |
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<td>0.39</td> |
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<td>0.13</td> |
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<td>0.47</td> |
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<td>0.24</td> |
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<td>0.43</td> |
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<td>0.20</td> |
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<td>0.43</td> |
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<td>0.28</td> |
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<td>0.64</td> |
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<td><a href="https://huggingface.co/prem-research/Funcdex-0.6B-whatsapp">🤗</a></td> |
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<td>0.26</td> |
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<td>0.55</td> |
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<td>0.31</td> |
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<td>0.71</td> |
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</tr> |
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</tbody> |
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</table> |
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- Funcdex-0.6B are specialized models. Reported number is the average performance of each specific model in their respective subset. |
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### Funcdex-MT: Bundle/Multi-toolkit Performance: |
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<table border="1" class="dataframe"> |
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<thead> |
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<tr style="text-align: center;"> |
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<th rowspan="2">Bundle</th> |
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<th colspan="2">GPT-OSS-120B<br>(medium)</th> |
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<th colspan="2">GPT-5<br>(minimal)</th> |
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<th colspan="2">GPT-5 Mini<br>(medium)</th> |
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<th colspan="2">Qwen3-0.6B</th> |
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<th colspan="3">Funcdex-0.6B</th> |
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<th colspan="2">Qwen3-1.7B</th> |
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<th colspan="3">Funcdex-1.7B</th> |
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</tr> |
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<tr style="text-align: center;"> |
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<th>EM</th> |
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<th>SR</th> |
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<th>EM</th> |
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<th>SR</th> |
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<th>EM</th> |
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<th>SR</th> |
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<th>EM</th> |
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<th>SR</th> |
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<th>EM</th> |
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<th>SR</th> |
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<th>LoRA Checkpoint</th> |
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<th>EM</th> |
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<th>SR</th> |
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<th>EM</th> |
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<th>SR</th> |
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<th>LoRA Checkpoint</th> |
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</tr> |
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</thead> |
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<tbody> |
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<tr style="text-align: center;"> |
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<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> |
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<td>0.28</td> |
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<td>0.53</td> |
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<td>0.15</td> |
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<td>0.54</td> |
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<td>0.22</td> |
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<td>0.56</td> |
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<td>0.19</td> |
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<td>0.51</td> |
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<td>0.26</td> |
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<td>0.54</td> |
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<td><a href="https://huggingface.co/prem-research/Funcdex-0.6B-gmail_googlecalendar">🤗</a></td> |
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<td>0.17</td> |
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<td>0.61</td> |
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<td>0.32</td> |
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<td>0.71</td> |
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<td rowspan="5"><a href="https://huggingface.co/prem-research/Funcdex-1.7B">🤗</a></td> |
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</tr> |
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<tr style="text-align: center;"> |
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<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> |
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<td>0.32</td> |
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<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> |
|
|
|
|
|
|
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|
## Inference |
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- 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. |
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- We use vLLM deployment with `tool_choice="auto"`. |
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## Metrics |
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|
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. |
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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. |
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## Deployment with vLLM |
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|
`vllm serve ojus1/Qwen3-1.7B-Instruct --enable-lora --lora-modules prem-research/Funcdex-1.7B=prem-research/Funcdex-1.7B --enable-auto-tool-choice --tool-call-parser hermes` |
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|
# Quickstart |
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|
```python |
|
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from transformers import AutoModelForCausalLM, AutoTokenizer |
|
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from peft import PeftModel |
|
|
import torch |
|
|
import json |
|
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|
|
|
# Load model and tokenizer |
|
|
base_model_name = "ojus1/Qwen3-1.7B-Instruct" |
|
|
model_name = "prem-research/Funcdex-1.7B" |
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|
|
tokenizer = AutoTokenizer.from_pretrained(model_name) |
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|
|
base_model = AutoModelForCausalLM.from_pretrained( |
|
|
base_model_name, |
|
|
torch_dtype="auto", |
|
|
device_map="auto" |
|
|
) |
|
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|
|
model = PeftModel.from_pretrained( |
|
|
base_model, |
|
|
model_name, |
|
|
torch_dtype="auto", |
|
|
device_map="auto" |
|
|
) |
|
|
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|
|
# Define tools (supports all toolkits) |
|
|
tools = [ |
|
|
{ |
|
|
"type": "function", |
|
|
"function": { |
|
|
"name": "CREATE_SHARED_DRIVE", |
|
|
"description": "Create a new shared drive in Google Drive", |
|
|
"parameters": { |
|
|
"type": "object", |
|
|
"properties": { |
|
|
"name": {"type": "string", "description": "Name of the shared drive"}, |
|
|
"requestId": {"type": "string", "description": "Unique request ID"} |
|
|
}, |
|
|
"required": ["name", "requestId"] |
|
|
} |
|
|
} |
|
|
}, |
|
|
{ |
|
|
"type": "function", |
|
|
"function": { |
|
|
"name": "CREATE_A_FOLDER", |
|
|
"description": "Create a folder in Google Drive", |
|
|
"parameters": { |
|
|
"type": "object", |
|
|
"properties": { |
|
|
"folder_name": {"type": "string", "description": "Name of the folder"}, |
|
|
"parent_id": {"type": "string", "description": "Parent drive or folder ID"} |
|
|
}, |
|
|
"required": ["folder_name", "parent_id"] |
|
|
} |
|
|
} |
|
|
} |
|
|
] |
|
|
|
|
|
# Define conversation |
|
|
messages = [ |
|
|
{"role": "system", "content": "You are a helpful assistant that can help with tasks by using tools."}, |
|
|
{"role": "user", "content": "Create a shared drive named 'Partner-Alpha-Integration' with request ID 'req-12345'."} |
|
|
] |
|
|
|
|
|
# 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) |
|
|
# Expected output includes: <tool_call>{"name": "CREATE_SHARED_DRIVE", "arguments": {"name": "Partner-Alpha-Integration", "requestId": "req-12345"}}</tool_call> |
|
|
``` |
|
|
|
|
|
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. |
