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README.md CHANGED
@@ -2,16 +2,16 @@
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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-7B/blob/main/LICENSE
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  base_model:
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- - Qwen/Qwen2.5-7B-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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14
- # katanemo/Arch-Function-7B
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16
  ## Overview
17
  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.
@@ -54,7 +54,7 @@ Katanemo Arch-Function collection is built on top of the [Qwen 2.5](https://hugg
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  ## Performance Benchmarks
57
- 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:
58
 
59
  <table>
60
  <tr style="text-align: center; vertical-align: middle; font-weight: bold;">
@@ -75,109 +75,121 @@ We evaluate Katanemo Arch-Function series on the [Berkeley Function-Calling Lead
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  </tr>
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  <tr style="text-align: center; vertical-align: middle;">
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  <td>1</td>
78
- <td>GPT-4-turbo-2024-04-09</td>
79
- <td>59.49%</td>
80
- <td>82.65%</td>
81
- <td>83.80%</td>
82
- <td>73.39%</td>
83
- <td>21.62%</td>
84
- <td>70.73%</td>
85
- <td>79.79%</td>
86
  </tr>
87
  <tr style="text-align: center; vertical-align: middle;">
88
- <td>3</td>
89
- <td>xLAM-8x22b-r</td>
90
- <td>59.13%</td>
91
- <td>89.75%</td>
92
- <td>89.32%</td>
93
- <td>72.81%</td>
94
- <td>15.62%</td>
95
- <td>97.56%</td>
96
- <td>75.23%</td>
97
  </tr>
98
  <tr style="text-align: center; vertical-align: middle; font-weight: bold;">
99
  <td> </td>
100
  <td>Arch-Function-7B</td>
101
- <td>57.48%</td>
102
- <td>87.50%</td>
103
- <td>86.80%</td>
104
- <td>72.19%</td>
105
- <td>13.75%</td>
106
- <td>82.93%</td>
107
- <td>79.54%</td>
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
108
  </tr>
109
  <tr style="text-align: center; vertical-align: middle; font-weight: bold;">
110
  <td> </td>
111
  <td>Arch-Function-3B</td>
112
- <td>56.23%</td>
113
- <td>85.10%</td>
114
- <td>89.16%</td>
115
- <td>70.72%</td>
116
- <td>12.28%</td>
117
- <td>90.24%</td>
118
- <td>73.98%</td>
119
- </tr>
120
- <tr style="text-align: center; vertical-align: middle;">
121
- <td>7</td>
122
- <td>mistral-large-2407</td>
123
- <td>55.82%</td>
124
- <td>84.12%</td>
125
- <td>83.09%</td>
126
- <td>67.17%</td>
127
- <td>20.50%</td>
128
- <td>78.05%</td>
129
- <td>48.93%</td>
130
- </tr>
131
- <tr style="text-align: center; vertical-align: middle;">
132
- <td>9</td>
133
- <td>Claude-3.5-Sonnet-20240620</td>
134
- <td>54.83%</td>
135
- <td>70.35%</td>
136
- <td>66.34%</td>
137
- <td>71.39%</td>
138
- <td>23.5%</td>
139
- <td>63.41%</td>
140
- <td>75.91%</td>
141
  </tr>
142
  </tr>
143
  <tr style="text-align: center; vertical-align: middle; font-weight: bold;">
144
  <td> </td>
145
  <td>Arch-Function-1.5B</td>
146
- <td>53.61%</td>
147
- <td>82.60%</td>
148
- <td>87.36%</td>
149
- <td>68.19%</td>
150
- <td>8.62%</td>
151
- <td>87.80%</td>
152
- <td>75.90%</td>
153
  </tr>
154
- <tr style="text-align: center; vertical-align: middle;">
155
- <td>11</td>
156
- <td>o1-mini-2024-09-12</td>
157
- <td>53.43%</td>
158
- <td>75.48%</td>
159
- <td>76.86%</td>
160
- <td>71.17%</td>
161
- <td>11.00%</td>
162
- <td>46.34%</td>
163
- <td>88.07%</td>
 
164
  </tr>
165
- <tr style="text-align: center; vertical-align: middle;">
166
- <td>12</td>
167
- <td>Gemini-1.5-Flash-Preview-0514</td>
168
- <td>53.01%</td>
169
- <td>77.10%</td>
170
- <td>71.23%</td>
171
- <td>71.17%</td>
172
- <td>13.12%</td>
173
- <td>60.98%</td>
174
- <td>76.15%</td>
175
  </tr>
176
  </table>
177
 
178
 
179
  # Requirements
180
- The code of Arch-Function-7B has been in the Hugging Face `transformers` library and we advise you to install latest version:
181
  ```bash
182
  pip install transformers>=4.37.0
183
  ```
@@ -193,7 +205,7 @@ import json
193
  from typing import Any, Dict, List
194
  from transformers import AutoModelForCausalLM, AutoTokenizer
195
 
196
- model_name = "katanemo/Arch-Function-7B"
197
  model = AutoModelForCausalLM.from_pretrained(
198
  model_name, device_map="auto", torch_dtype="auto", trust_remote_code=True
199
  )
@@ -332,4 +344,4 @@ The current temperature in Seattle is 62 degrees in Fahrenheit.
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333
 
334
  # License
335
- Katanemo Arch-Function collection is distributed under the [Katanemo license](https://huggingface.co/katanemolabs/Arch-Function-7B/blob/main/LICENSE).
 
2
  license: other
3
  license_name: katanemo-research
4
  license_link: >-
5
+ https://huggingface.co/katanemolabs/Arch-Function-1.5B/blob/main/LICENSE
6
  base_model:
7
+ - Qwen/Qwen2.5-1.5B-Instruct
8
  language:
9
  - en
10
  pipeline_tag: text-generation
11
  library_name: transformers
12
  ---
13
 
14
+ # katanemo/Arch-Function-1.5B
15
 
16
  ## Overview
17
  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.
 
54
 
55
 
56
  ## Performance Benchmarks
57
+ 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.
58
 
59
  <table>
60
  <tr style="text-align: center; vertical-align: middle; font-weight: bold;">
 
75
  </tr>
76
  <tr style="text-align: center; vertical-align: middle;">
77
  <td>1</td>
78
+ <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>
84
+ <td>63.41%</td>
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+ <td>82.93%</td>
86
  </tr>
87
  <tr style="text-align: center; vertical-align: middle;">
88
+ <td>6</td>
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+ <td>o1-preview-2024-09-12 (Prompt)</td>
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+ <td>59.27%</td>
91
+ <td>86.42%</td>
92
+ <td>88.88%</td>
93
+ <td>73.08%</td>
94
+ <td>17.62%</td>
95
+ <td>73.17%</td>
96
+ <td>74.60%</td>
97
  </tr>
98
  <tr style="text-align: center; vertical-align: middle; font-weight: bold;">
99
  <td> </td>
100
  <td>Arch-Function-7B</td>
101
+ <td>58.44%</td>
102
+ <td>85.58%</td>
103
+ <td>88.14%</td>
104
+ <td>69.08%</td>
105
+ <td>20.50%</td>
106
+ <td>92.68%</td>
107
+ <td>74.05%</td>
108
+ </tr>
109
+ <tr style="text-align: center; vertical-align: middle; ">
110
+ <td>9</td>
111
+ <td>Gemini-1.5-Flash-002 (Prompt)</td>
112
+ <td>57.92%</td>
113
+ <td>86.58%</td>
114
+ <td>89.48%</td>
115
+ <td>76.28%</td>
116
+ <td>9.88%</td>
117
+ <td>85.37%</td>
118
+ <td>78.54%</td>
119
+ </tr>
120
+ <tr style="text-align: center; vertical-align: middle; ">
121
+ <td>12</td>
122
+ <td>Claude-3.5-Sonnet-20240620 (FC)</td>
123
+ <td>57.42%</td>
124
+ <td>70.04%</td>
125
+ <td>66.27%</td>
126
+ <td>74.68%</td>
127
+ <td>28.38%</td>
128
+ <td>68.29%</td>
129
+ <td>74.58%</td>
130
+ </tr>
131
+ <tr style="text-align: center; vertical-align: middle; ">
132
+ <td>13</td>
133
+ <td>mistral-large-2407 (FC)</td>
134
+ <td>56.80%</td>
135
+ <td>86.62%</td>
136
+ <td>84.57%</td>
137
+ <td>68.37%</td>
138
+ <td>20.62%</td>
139
+ <td>75.61%</td>
140
+ <td>49.44%</td>
141
  </tr>
142
  <tr style="text-align: center; vertical-align: middle; font-weight: bold;">
143
  <td> </td>
144
  <td>Arch-Function-3B</td>
145
+ <td>56.57%</td>
146
+ <td>83.62%</td>
147
+ <td>85.36%</td>
148
+ <td>66.90%</td>
149
+ <td>19.50%</td>
150
+ <td>97.56%</td>
151
+ <td>70.99%</td>
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
152
  </tr>
153
  </tr>
154
  <tr style="text-align: center; vertical-align: middle; font-weight: bold;">
155
  <td> </td>
156
  <td>Arch-Function-1.5B</td>
157
+ <td>54.52%</td>
158
+ <td>80.31%</td>
159
+ <td>82.04%</td>
160
+ <td>66.19%</td>
161
+ <td>17.25%</td>
162
+ <td>97.56%</td>
163
+ <td>69.95%</td>
164
  </tr>
165
+
166
+ <tr style="text-align: center; vertical-align: middle; ">
167
+ <td>21</td>
168
+ <td>Llama-3.1-70B-Instruct (Prompt)</td>
169
+ <td>53.67%</td>
170
+ <td>88.90%</td>
171
+ <td>89.34%</td>
172
+ <td>61.13%</td>
173
+ <td>12.38%</td>
174
+ <td>92.68%</td>
175
+ <td>58.38%</td>
176
  </tr>
177
+ <tr style="text-align: center; vertical-align: middle; ">
178
+ <td>22</td>
179
+ <td>Gemma-2-27b-it (Prompt)</td>
180
+ <td>53.66%</td>
181
+ <td>88.52%</td>
182
+ <td>87.89%</td>
183
+ <td>69.48%</td>
184
+ <td>4.12%</td>
185
+ <td>87.8%</td>
186
+ <td>68.76%</td>
187
  </tr>
188
  </table>
189
 
190
 
191
  # Requirements
192
+ The code of Arch-Function-1.5B has been in the Hugging Face `transformers` library and we advise you to install latest version:
193
  ```bash
194
  pip install transformers>=4.37.0
195
  ```
 
205
  from typing import Any, Dict, List
206
  from transformers import AutoModelForCausalLM, AutoTokenizer
207
 
208
+ model_name = "katanemo/Arch-Function-1.5B"
209
  model = AutoModelForCausalLM.from_pretrained(
210
  model_name, device_map="auto", torch_dtype="auto", trust_remote_code=True
211
  )
 
344
 
345
 
346
  # License
347
+ 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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