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Arch-Function-7B-Q2_K.gguf

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README.md

@@ -1,16 +1,17 @@
 ---
 license: other
 license_name: katanemo-research
-license_link: https://huggingface.co/katanemolabs/Arch-Function-7B.gguf/blob/main/LICENSE
+license_link: >-
+  https://huggingface.co/katanemolabs/Arch-Function-1.5B/blob/main/LICENSE
 base_model:
-- katanemo/Arch-Function-7B
+- Qwen/Qwen2.5-1.5B-Instruct
 language:
 - en
 pipeline_tag: text-generation
 library_name: transformers
 ---
 
-# katanemo/Arch-Function-7B
+# katanemo/Arch-Function-1.5B
 
 ## Overview
 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.
@@ -53,7 +54,7 @@ Katanemo Arch-Function collection is built on top of the [Qwen 2.5](https://hugg
 
 
 ## Performance Benchmarks
-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. The results are shwon below:
+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. The results (as of Oct 21st, 2024) are shwon below:
 
 <table>
   <tr style="text-align: center; vertical-align: middle; font-weight: bold;">
@@ -74,109 +75,186 @@ We evaluate Katanemo Arch-Function series on the [Berkeley Function-Calling Lead
   </tr>
   <tr style="text-align: center; vertical-align: middle;">
     <td>1</td>
-    <td>GPT-4-turbo-2024-04-09</td>
-    <td>59.49%</td>
-    <td>82.65%</td>
-    <td>83.80%</td>
-    <td>73.39%</td>
-    <td>21.62%</td>
+    <td>GPT-4o-2024-08-06 (FC)</td>
+    <td>62.19%</td>
+    <td>85.90%</td>
+    <td>85.64%</td>
+    <td>75.43%</td>
+    <td>25.00%</td>
+    <td>63.41%</td>
+    <td>82.93%</td>
+  </tr>
+  <tr style="text-align: center; vertical-align: middle;">
+    <td>2</td>
+    <td>Functionary-Medium-v3.1 (FC)</td>
+    <td>62.02%</td>
+    <td>89.52%</td>
+    <td>89.77%</td>
+    <td>73.48%</td>
+    <td>23.50%</td>
     <td>70.73%</td>
-    <td>79.79%</td>
+    <td>73.32%</td>
   </tr>
   <tr style="text-align: center; vertical-align: middle;">
-    <td>3</td>
-    <td>xLAM-8x22b-r</td>
-    <td>59.13%</td>
-    <td>89.75%</td>
-    <td>89.32%</td>
-    <td>72.81%</td>
-    <td>15.62%</td>
-    <td>97.56%</td>
-    <td>75.23%</td>
+    <td>5</td>
+    <td>ToolACE-8B (FC)</td>
+    <td>60.44%</td>
+    <td>87.06%</td>
+    <td>89.52%</td>
+    <td>74.99%</td>
+    <td>17.38%</td>
+    <td>80.49%</td>
+    <td>85.71%</td>
+  </tr>
+  <tr style="text-align: center; vertical-align: middle;">
+    <td>6</td>
+    <td>o1-preview-2024-09-12 (Prompt)</td>
+    <td>59.27%</td>
+    <td>86.42%</td>
+    <td>88.88%</td>
+    <td>73.08%</td>
+    <td>17.62%</td>
+    <td>73.17%</td>
+    <td>74.60%</td>
   </tr>
   <tr style="text-align: center; vertical-align: middle; font-weight: bold;">
     <td> </td>
     <td>Arch-Function-7B</td>
-    <td>57.48%</td>
-    <td>87.50%</td>
-    <td>86.80%</td>
-    <td>72.19%</td>
-    <td>13.75%</td>
-    <td>82.93%</td>
-    <td>79.54%</td>
+    <td>58.44%</td>
+    <td>85.58%</td>
+    <td>88.14%</td>
+    <td>69.08%</td>
+    <td>20.50%</td>
+    <td>92.68%</td>
+    <td>74.05%</td>
+  </tr>
+  <tr style="text-align: center; vertical-align: middle; ">
+    <td>8</td>
+    <td>xLAM-8x22b-r (FC)</td>
+    <td>57.99%</td>
+    <td>88.15%</td>
+    <td>90.11%</td>
+    <td>71.97%</td>
+    <td>14.50%</td>
+    <td>85.37%</td>
+    <td>67.29%</td>
+  </tr>
+  <tr style="text-align: center; vertical-align: middle; ">
+    <td>9</td>
+    <td>Gemini-1.5-Flash-002 (Prompt)</td>
+    <td>57.92%</td>
+    <td>86.58%</td>
+    <td>89.48%</td>
+    <td>76.28%</td>
+    <td>9.88%</td>
+    <td>85.37%</td>
+    <td>78.54%</td>
+  </tr>
+  <tr style="text-align: center; vertical-align: middle; ">
+    <td>10</td>
+    <td>Hammer2.0-7b (FC)</td>
+    <td>57.69%</td>
+    <td>90.27%</td>
+    <td>89.25%</td>
+    <td>69.79%</td>
+    <td>14.75%</td>
+    <td>95.12%</td>
+    <td>68.46%</td>
+  </tr>
+  <tr style="text-align: center; vertical-align: middle; ">
+    <td>12</td>
+    <td>Claude-3.5-Sonnet-20240620 (FC)</td>
+    <td>57.42%</td>
+    <td>70.04%</td>
+    <td>66.27%</td>
+    <td>74.68%</td>
+    <td>28.38%</td>
+    <td>68.29%</td>
+    <td>74.58%</td>
+  </tr>
+  <tr style="text-align: center; vertical-align: middle; ">
+    <td>13</td>
+    <td>mistral-large-2407 (FC)</td>
+    <td>56.80%</td>
+    <td>86.62%</td>
+    <td>84.57%</td>
+    <td>68.37%</td>
+    <td>20.62%</td>
+    <td>75.61%</td>
+    <td>49.44%</td>
   </tr>
   <tr style="text-align: center; vertical-align: middle; font-weight: bold;">
     <td> </td>
     <td>Arch-Function-3B</td>
-    <td>56.23%</td>
-    <td>85.10%</td>
-    <td>89.16%</td>
-    <td>70.72%</td>
-    <td>12.28%</td>
-    <td>90.24%</td>
-    <td>73.98%</td>
-  </tr>
-  <tr style="text-align: center; vertical-align: middle;">
-    <td>7</td>
-    <td>mistral-large-2407</td>
-    <td>55.82%</td>
-    <td>84.12%</td>
-    <td>83.09%</td>
-    <td>67.17%</td>
-    <td>20.50%</td>
-    <td>78.05%</td>
-    <td>48.93%</td>
-  </tr>
-  <tr style="text-align: center; vertical-align: middle;">
-    <td>9</td>
-    <td>Claude-3.5-Sonnet-20240620</td>
-    <td>54.83%</td>
-    <td>70.35%</td>
-    <td>66.34%</td>
-    <td>71.39%</td>
-    <td>23.5%</td>
-    <td>63.41%</td>
-    <td>75.91%</td>
+    <td>56.57%</td>
+    <td>83.62%</td>
+    <td>85.36%</td>
+    <td>66.90%</td>
+    <td>19.50%</td>
+    <td>97.56%</td>
+    <td>70.99%</td>
   </tr>
   </tr>
   <tr style="text-align: center; vertical-align: middle; font-weight: bold;">
     <td> </td>
     <td>Arch-Function-1.5B</td>
-    <td>53.61%</td>
-    <td>82.60%</td>
-    <td>87.36%</td>
-    <td>68.19%</td>
-    <td>8.62%</td>
-    <td>87.80%</td>
-    <td>75.90%</td>
+    <td>54.52%</td>
+    <td>80.31%</td>
+    <td>82.04%</td>
+    <td>66.19%</td>
+    <td>17.25%</td>
+    <td>97.56%</td>
+    <td>69.95%</td>
   </tr>
-  <tr style="text-align: center; vertical-align: middle;">
-    <td>11</td>
-    <td>o1-mini-2024-09-12</td>
-    <td>53.43%</td>
-    <td>75.48%</td>
-    <td>76.86%</td>
-    <td>71.17%</td>
-    <td>11.00%</td>
-    <td>46.34%</td>
-    <td>88.07%</td>
+  <tr style="text-align: center; vertical-align: middle; ">
+    <td>19</td>
+    <td>xLAM-7b-r (FC)</td>
+    <td>54.41%</td>
+    <td>81.40%</td>
+    <td>83.46%</td>
+    <td>67.88%</td>
+    <td>14.50%</td>
+    <td>97.56%</td>
+    <td>64.05%</td>
   </tr>
-  <tr style="text-align: center; vertical-align: middle;">
-    <td>12</td>
-    <td>Gemini-1.5-Flash-Preview-0514</td>
-    <td>53.01%</td>
-    <td>77.10%</td>
-    <td>71.23%</td>
-    <td>71.17%</td>
-    <td>13.12%</td>
-    <td>60.98%</td>
-    <td>76.15%</td>
+  <tr style="text-align: center; vertical-align: middle; ">
+    <td>20</td>
+    <td>Qwen2.5-7B-Instruct (Prompt)</td>
+    <td>54.27%</td>
+    <td>85.79%</td>
+    <td>88.13%</td>
+    <td>65.97%</td>
+    <td>11.25%</td>
+    <td>92.68%</td>
+    <td>64.95%</td>
+  </tr>
+  <tr style="text-align: center; vertical-align: middle; ">
+    <td>21</td>
+    <td>Llama-3.1-70B-Instruct (Prompt)</td>
+    <td>53.67%</td>
+    <td>88.90%</td>
+    <td>89.34%</td>
+    <td>61.13%</td>
+    <td>12.38%</td>
+    <td>92.68%</td>
+    <td>58.38%</td>
+  </tr>
+  <tr style="text-align: center; vertical-align: middle; ">
+    <td>22</td>
+    <td>Gemma-2-27b-it (Prompt)</td>
+    <td>53.66%</td>
+    <td>88.52%</td>
+    <td>87.89%</td>
+    <td>69.48%</td>
+    <td>4.12%</td>
+    <td>87.8%</td>
+    <td>68.76%</td>
   </tr>
 </table>
 
 
 # Requirements
-The code of Arch-Function-7B has been in the Hugging Face `transformers` library and we advise you to install latest version:
+The code of Arch-Function-1.5B has been in the Hugging Face `transformers` library and we advise you to install latest version:
 ```bash
 pip install transformers>=4.37.0
 ```
@@ -192,7 +270,7 @@ import json
 from typing import Any, Dict, List
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
-model_name = "katanemo/Arch-Function-7B"
+model_name = "katanemo/Arch-Function-1.5B"
 model = AutoModelForCausalLM.from_pretrained(
     model_name, device_map="auto", torch_dtype="auto", trust_remote_code=True
 )
@@ -331,4 +409,4 @@ The current temperature in Seattle is 62 degrees in Fahrenheit.
 
 
 # License
-Katanemo Arch-Function collection is distributed under the [Katanemo license](https://huggingface.co/katanemolabs/Arch-Function-7B.gguf/blob/main/LICENSE).
+Katanemo Arch-Function collection is distributed under the [Katanemo license](https://huggingface.co/katanemolabs/Arch-Function-1.5B/blob/main/LICENSE).