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- Improve language tag (e72c8b2055c427afe8fb4ec927ac354bbb10c754)


Co-authored-by: Loïck BOURDOIS <lbourdois@users.noreply.huggingface.co>

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  1. README.md +92 -78
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@@ -1,79 +1,93 @@
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- ---
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- base_model: Qwen/Qwen2.5-3B-Instruct
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- library_name: transformers
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- model_name: Qwen2.5-3B-Instruct-thinking-function_calling-V0
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- tags:
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- - generated_from_trainer
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- - trl
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- - sft
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- licence: license
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- ---
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-
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- # Model Card for Qwen2.5-3B-Instruct-thinking-function_calling-V0
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-
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- This model is a fine-tuned version of [Qwen/Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct).
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- It has been trained using [TRL](https://github.com/huggingface/trl).
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-
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- ## Quick start
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-
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- ```python
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- from transformers import pipeline
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-
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- prompt="""<bos><start_of_turn>human
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- You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags.You may call one or more functions to assist with the user query.
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- Don't make assumptions about what values to plug into functions.Here are the available tools:
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- <tools>
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- [{'type': 'function', 'function': {'name': 'convert_currency', 'description': 'Convert from one currency to another', 'parameters': {'type': 'object', 'properties': {'amount': {'type': 'number', 'description': 'The amount to convert'}, 'from_currency': {'type': 'string', 'description': 'The currency to convert from'}, 'to_currency': {'type': 'string', 'description': 'The currency to convert to'}}, 'required': ['amount', 'from_currency', 'to_currency']}}},
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- {'type': 'function', 'function': {'name': 'calculate_distance', 'description': 'Calculate the distance between two locations', 'parameters': {'type': 'object', 'properties': {'start_location': {'type': 'string', 'description': 'The starting location'}, 'end_location': {'type': 'string', 'description': 'The ending location'}}, 'required': ['start_location', 'end_location']}}}
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- {'type': 'function', 'function': {'name': 'send_email', 'description': 'Send an email to a customer', 'parameters': {'type': 'object', 'properties': {'customer': {'type': 'string', 'description': 'The customer to send the email to'}, 'subject': {'type': 'string', 'description': 'The subject of the email'}, 'body': {'type': 'string', 'description': 'The body of the email'}}, 'required': ['customer', 'subject', 'body']}}}
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- ]
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- </tools>
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- Use the following pydantic model json schema for each tool call you will make:
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- {'title': 'FunctionCall', 'type': 'object', 'properties': {'arguments': {'title': 'Arguments', 'type': 'object'}, 'name': {'title': 'Name', 'type': 'string'}}, 'required': ['arguments', 'name']}
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- For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows:
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- <tool_call>
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- {tool_call}
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- </tool_call>Also, before making a call to a function take the time to plan the function to take. Make that thinking process between <think>{your thoughts}</think>
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-
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- Hi, I need you to tell John@doe.com that I received his package ?<end_of_turn><eos>
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- <start_of_turn>model
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- <think>"""
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- generator = pipeline("text-generation", model="Cotum/Qwen2.5-3B-Instruct-thinking-function_calling-V0", device="cuda")
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- output = generator([{"role": "user", "content": prompt}], max_new_tokens=128, return_full_text=False)[0]
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- print(output["generated_text"])
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- ```
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-
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- ## Training procedure
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-
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- This model was trained with SFT following the Bonus Unit 1 of the Agent Course of Hugging Face : https://huggingface.co/agents-course/notebooks/blob/main/bonus-unit1/bonus-unit1.ipynb
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-
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- ### Framework versions
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-
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- - TRL: 0.15.1
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- - Transformers: 4.49.0
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- - Pytorch: 2.6.0
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- - Datasets: 3.3.2
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- - Tokenizers: 0.21.0
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-
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- ## Citations
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-
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-
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-
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- Cite TRL as:
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-
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- ```bibtex
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- @misc{vonwerra2022trl,
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- title = {{TRL: Transformer Reinforcement Learning}},
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- author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
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- year = 2020,
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- journal = {GitHub repository},
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- publisher = {GitHub},
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- howpublished = {\url{https://github.com/huggingface/trl}}
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- }
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- ```
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-
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- <div align="center" style="line-height: 1;">
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- <a href="https://www.huggingface.co/Cotum" target="_blank" style="margin: 2px;">
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- <img alt="Follow" src="https://huggingface.co/datasets/Cotum/MATH-500-french-thoughts/resolve/main/Cotum_banner.png" style="display: inline-block; vertical-align: middle; width: 200px;"/>
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- </a>
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  </div>
 
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+ ---
2
+ base_model: Qwen/Qwen2.5-3B-Instruct
3
+ library_name: transformers
4
+ model_name: Qwen2.5-3B-Instruct-thinking-function_calling-V0
5
+ tags:
6
+ - generated_from_trainer
7
+ - trl
8
+ - sft
9
+ licence: license
10
+ language:
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+ - zho
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+ - eng
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+ - fra
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+ - spa
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+ - por
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+ - deu
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+ - ita
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+ - rus
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+ - jpn
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+ - kor
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+ - vie
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+ - tha
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+ - ara
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+ ---
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+
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+ # Model Card for Qwen2.5-3B-Instruct-thinking-function_calling-V0
27
+
28
+ This model is a fine-tuned version of [Qwen/Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct).
29
+ It has been trained using [TRL](https://github.com/huggingface/trl).
30
+
31
+ ## Quick start
32
+
33
+ ```python
34
+ from transformers import pipeline
35
+
36
+ prompt="""<bos><start_of_turn>human
37
+ You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags.You may call one or more functions to assist with the user query.
38
+ Don't make assumptions about what values to plug into functions.Here are the available tools:
39
+ <tools>
40
+ [{'type': 'function', 'function': {'name': 'convert_currency', 'description': 'Convert from one currency to another', 'parameters': {'type': 'object', 'properties': {'amount': {'type': 'number', 'description': 'The amount to convert'}, 'from_currency': {'type': 'string', 'description': 'The currency to convert from'}, 'to_currency': {'type': 'string', 'description': 'The currency to convert to'}}, 'required': ['amount', 'from_currency', 'to_currency']}}},
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+ {'type': 'function', 'function': {'name': 'calculate_distance', 'description': 'Calculate the distance between two locations', 'parameters': {'type': 'object', 'properties': {'start_location': {'type': 'string', 'description': 'The starting location'}, 'end_location': {'type': 'string', 'description': 'The ending location'}}, 'required': ['start_location', 'end_location']}}}
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+ {'type': 'function', 'function': {'name': 'send_email', 'description': 'Send an email to a customer', 'parameters': {'type': 'object', 'properties': {'customer': {'type': 'string', 'description': 'The customer to send the email to'}, 'subject': {'type': 'string', 'description': 'The subject of the email'}, 'body': {'type': 'string', 'description': 'The body of the email'}}, 'required': ['customer', 'subject', 'body']}}}
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+ ]
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+ </tools>
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+ Use the following pydantic model json schema for each tool call you will make:
46
+ {'title': 'FunctionCall', 'type': 'object', 'properties': {'arguments': {'title': 'Arguments', 'type': 'object'}, 'name': {'title': 'Name', 'type': 'string'}}, 'required': ['arguments', 'name']}
47
+ For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows:
48
+ <tool_call>
49
+ {tool_call}
50
+ </tool_call>Also, before making a call to a function take the time to plan the function to take. Make that thinking process between <think>{your thoughts}</think>
51
+
52
+ Hi, I need you to tell John@doe.com that I received his package ?<end_of_turn><eos>
53
+ <start_of_turn>model
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+ <think>"""
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+ generator = pipeline("text-generation", model="Cotum/Qwen2.5-3B-Instruct-thinking-function_calling-V0", device="cuda")
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+ output = generator([{"role": "user", "content": prompt}], max_new_tokens=128, return_full_text=False)[0]
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+ print(output["generated_text"])
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+ ```
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+
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+ ## Training procedure
61
+
62
+ This model was trained with SFT following the Bonus Unit 1 of the Agent Course of Hugging Face : https://huggingface.co/agents-course/notebooks/blob/main/bonus-unit1/bonus-unit1.ipynb
63
+
64
+ ### Framework versions
65
+
66
+ - TRL: 0.15.1
67
+ - Transformers: 4.49.0
68
+ - Pytorch: 2.6.0
69
+ - Datasets: 3.3.2
70
+ - Tokenizers: 0.21.0
71
+
72
+ ## Citations
73
+
74
+
75
+
76
+ Cite TRL as:
77
+
78
+ ```bibtex
79
+ @misc{vonwerra2022trl,
80
+ title = {{TRL: Transformer Reinforcement Learning}},
81
+ author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
82
+ year = 2020,
83
+ journal = {GitHub repository},
84
+ publisher = {GitHub},
85
+ howpublished = {\url{https://github.com/huggingface/trl}}
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+ }
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+ ```
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+
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+ <div align="center" style="line-height: 1;">
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+ <a href="https://www.huggingface.co/Cotum" target="_blank" style="margin: 2px;">
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+ <img alt="Follow" src="https://huggingface.co/datasets/Cotum/MATH-500-french-thoughts/resolve/main/Cotum_banner.png" style="display: inline-block; vertical-align: middle; width: 200px;"/>
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+ </a>
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  </div>