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README.md
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license: other
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license_name: katanemo-research
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license_link: >-
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https://huggingface.co/katanemolabs/Arch-Function-
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base_model:
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- Qwen/Qwen2.5-
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language:
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- en
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pipeline_tag: text-generation
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library_name: transformers
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---
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# katanemo/Arch-Function-
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## Overview
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The Katanemo Arch-Function collection of large language models (LLMs) is a collection state-of-the-art (SOTA) LLMs specifically designed for **function calling** tasks. The models are designed to understand complex function signatures, identify required parameters, and produce accurate function call outputs based on natural language prompts. Achieving performance on par with GPT-4, these models set a new benchmark in the domain of function-oriented tasks, making them suitable for scenarios where automated API interaction and function execution is crucial.
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## Performance Benchmarks
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We evaluate Katanemo Arch-Function series on the [Berkeley Function-Calling Leaderboard (BFCL)](https://gorilla.cs.berkeley.edu/leaderboard.html#leaderboard). For each model family, we select the one with the highest rank.
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<table>
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<tr style="text-align: center; vertical-align: middle; font-weight: bold;">
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</tr>
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<tr style="text-align: center; vertical-align: middle;">
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<td>1</td>
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<td>GPT-
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<tr style="text-align: center; vertical-align: middle;">
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<td>59.
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</tr>
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<tr style="text-align: center; vertical-align: middle; font-weight: bold;">
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<td> </td>
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<td>Arch-Function-7B</td>
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</tr>
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<tr style="text-align: center; vertical-align: middle; font-weight: bold;">
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<td> </td>
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<td>Arch-Function-3B</td>
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<td>56.
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<td>7</td>
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<td>mistral-large-2407</td>
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<td>55.82%</td>
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<td>84.12%</td>
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<td>83.09%</td>
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<td>67.17%</td>
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<td>20.50%</td>
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<td>78.05%</td>
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<td>48.93%</td>
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</tr>
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<tr style="text-align: center; vertical-align: middle;">
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<td>9</td>
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<td>Claude-3.5-Sonnet-20240620</td>
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<td>54.83%</td>
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<td>70.35%</td>
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<td>66.34%</td>
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<td>71.39%</td>
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<td>23.5%</td>
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<td>63.41%</td>
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<td>75.91%</td>
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</tr>
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</tr>
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<tr style="text-align: center; vertical-align: middle; font-weight: bold;">
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<td> </td>
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<td>Arch-Function-1.5B</td>
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<tr style="text-align: center; vertical-align: middle;">
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<td>53.
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<td>76
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</tr>
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</table>
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# Requirements
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The code of Arch-Function-
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```bash
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pip install transformers>=4.37.0
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```
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from typing import Any, Dict, List
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "katanemo/Arch-Function-
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model = AutoModelForCausalLM.from_pretrained(
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model_name, device_map="auto", torch_dtype="auto", trust_remote_code=True
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)
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# License
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Katanemo Arch-Function collection is distributed under the [Katanemo license](https://huggingface.co/katanemolabs/Arch-Function-
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license: other
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license_name: katanemo-research
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license_link: >-
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https://huggingface.co/katanemolabs/Arch-Function-1.5B/blob/main/LICENSE
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base_model:
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- Qwen/Qwen2.5-1.5B-Instruct
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language:
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- en
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pipeline_tag: text-generation
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library_name: transformers
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---
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# katanemo/Arch-Function-1.5B
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## Overview
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The Katanemo Arch-Function collection of large language models (LLMs) is a collection state-of-the-art (SOTA) LLMs specifically designed for **function calling** tasks. The models are designed to understand complex function signatures, identify required parameters, and produce accurate function call outputs based on natural language prompts. Achieving performance on par with GPT-4, these models set a new benchmark in the domain of function-oriented tasks, making them suitable for scenarios where automated API interaction and function execution is crucial.
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## Performance Benchmarks
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We evaluate Katanemo Arch-Function series on the [Berkeley Function-Calling Leaderboard (BFCL)](https://gorilla.cs.berkeley.edu/leaderboard.html#leaderboard). We compare with commonly-used models and the results (as of Oct 21st, 2024) are shwon below. For each model family, we select the one with the highest rank.
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<table>
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<tr style="text-align: center; vertical-align: middle; font-weight: bold;">
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</tr>
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<tr style="text-align: center; vertical-align: middle;">
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<td>1</td>
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<td>GPT-4o-2024-08-06 (FC)</td>
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<td>62.19%</td>
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<td>85.90%</td>
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<td>85.64%</td>
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<td>75.43%</td>
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<td>25.00%</td>
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<td>63.41%</td>
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<td>82.93%</td>
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</tr>
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<tr style="text-align: center; vertical-align: middle;">
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<td>6</td>
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<td>o1-preview-2024-09-12 (Prompt)</td>
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<td>59.27%</td>
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<td>86.42%</td>
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<td>88.88%</td>
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<td>73.08%</td>
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<td>17.62%</td>
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<td>73.17%</td>
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<td>74.60%</td>
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</tr>
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<tr style="text-align: center; vertical-align: middle; font-weight: bold;">
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<td> </td>
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<td>Arch-Function-7B</td>
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<td>58.44%</td>
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<td>85.58%</td>
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<td>88.14%</td>
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<td>69.08%</td>
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<td>20.50%</td>
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<td>92.68%</td>
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<td>74.05%</td>
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<tr style="text-align: center; vertical-align: middle; ">
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<td>9</td>
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<td>Gemini-1.5-Flash-002 (Prompt)</td>
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<td>57.92%</td>
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<td>86.58%</td>
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<td>89.48%</td>
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<td>76.28%</td>
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<td>9.88%</td>
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<td>85.37%</td>
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<td>78.54%</td>
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<td>12</td>
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<td>Claude-3.5-Sonnet-20240620 (FC)</td>
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<td>57.42%</td>
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<td>70.04%</td>
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<td>66.27%</td>
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<td>74.68%</td>
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<td>28.38%</td>
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<td>68.29%</td>
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<td>74.58%</td>
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<td>13</td>
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<td>mistral-large-2407 (FC)</td>
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<td>56.80%</td>
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<td>86.62%</td>
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<td>84.57%</td>
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<td>68.37%</td>
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<td>20.62%</td>
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<td>75.61%</td>
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<td>49.44%</td>
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</tr>
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<tr style="text-align: center; vertical-align: middle; font-weight: bold;">
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<td> </td>
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<td>Arch-Function-3B</td>
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<td>56.57%</td>
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<td>83.62%</td>
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<td>85.36%</td>
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<td>66.90%</td>
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<td>19.50%</td>
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<td>97.56%</td>
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<td>70.99%</td>
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</tr>
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<tr style="text-align: center; vertical-align: middle; font-weight: bold;">
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<td> </td>
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<td>Arch-Function-1.5B</td>
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<td>54.52%</td>
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<td>80.31%</td>
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<td>82.04%</td>
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<td>66.19%</td>
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<td>17.25%</td>
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<td>97.56%</td>
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<td>69.95%</td>
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</tr>
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<td>21</td>
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<td>Llama-3.1-70B-Instruct (Prompt)</td>
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<td>53.67%</td>
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<td>88.90%</td>
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<td>89.34%</td>
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<td>61.13%</td>
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<td>12.38%</td>
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<td>92.68%</td>
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<td>58.38%</td>
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</tr>
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<tr style="text-align: center; vertical-align: middle; ">
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<td>22</td>
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<td>Gemma-2-27b-it (Prompt)</td>
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<td>53.66%</td>
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<td>88.52%</td>
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<td>87.89%</td>
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<td>69.48%</td>
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<td>4.12%</td>
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<td>87.8%</td>
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<td>68.76%</td>
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</tr>
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</table>
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# Requirements
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The code of Arch-Function-1.5B has been in the Hugging Face `transformers` library and we advise you to install latest version:
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```bash
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pip install transformers>=4.37.0
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```
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from typing import Any, Dict, List
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "katanemo/Arch-Function-1.5B"
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model = AutoModelForCausalLM.from_pretrained(
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model_name, device_map="auto", torch_dtype="auto", trust_remote_code=True
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)
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# License
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Katanemo Arch-Function collection is distributed under the [Katanemo license](https://huggingface.co/katanemolabs/Arch-Function-1.5B/blob/main/LICENSE).
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