updated model card with more precise information about the model

README.md

@@ -7,28 +7,67 @@ tags:
 - trl
 - sft
 licence: license
+datasets:
+- Jofthomas/hermes-function-calling-thinking-V1
 ---
 
 # Model Card for Phi-3.5-mini-thinking-function_calling-V0
 
-This model is a fine-tuned version of [microsoft/Phi-3.5-mini-instruct](https://huggingface.co/microsoft/Phi-3.5-mini-instruct).
-It has been trained using [TRL](https://github.com/huggingface/trl).
+This model is a fine-tuned version of [microsoft/Phi-3.5-mini-instruct](https://huggingface.co/microsoft/Phi-3.5-mini-instruct) on [Jofthomas/hermes-function-calling-thinking-V1](https://huggingface.co/datasets/NousResearch/hermes-function-calling-v1) for function calling.
+<br>This toy model has been training as an alternative exercise to the Unit 1 bonus section of the [Agent Ai course](https://huggingface.co/learn/agents-course/unit0/introduction).
 
-## Quick start
+💥 The training script to adapt the given example notebook for Phi-3.5-mini-instruct can be found [here](https://colab.research.google.com/drive/1b8_Kdzloqe_UMFasGXqItKuoP2D-wTrW?usp=sharing).
+
+
+## Example usage
 
 ```python
-from transformers import pipeline
+prompt = """<|user|>
+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. Don't make assumptions about what values to plug into functions.Here are the available tools:<tools> [{'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']}}}, {'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']}}}] </tools>Use the following pydantic model json schema for each tool call you will make: {'title': 'FunctionCall', 'type': 'object', 'properties': {'arguments': {'title': 'Arguments', 'type': 'object'}, 'name': {'title': 'Name', 'type': 'string'}}, 'required': ['arguments', 'name']}For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows:
+<tool_call>
+{tool_call}
+</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>
+
+Hi, I need to convert 500 USD to Euros. Can you help me with that?<|end|>
+<|assistant|>
+<think>"""
+
+eos_token_id = tokenizer.encode('<|endoftext|>')[0]
 
-question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
-generator = pipeline("text-generation", model="andreagemelli/Phi-3.5-mini-thinking-function_calling-V0", device="cuda")
-output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
-print(output["generated_text"])
+inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False)
+inputs = {k: v.to("cuda") for k,v in inputs.items()}
+outputs = model.generate(**inputs,
+                         max_new_tokens=300,# Adapt as necessary
+                         do_sample=True,
+                         top_p=0.95,
+                         temperature=0.01,
+                         repetition_penalty=1.0,
+                         eos_token_id=eos_token_id)
+
+print(tokenizer.decode(outputs[0]))
 ```
 
-## Training procedure
+You should expect a result similar to the following, where the model could successfully "think" and call a function to answer the user:
+```python
+<|user|> 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. Don't make assumptions about what values to plug into functions.Here are the available tools:<tools>  [{'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']}}}, {'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']}}}] </tools> Use the following pydantic model json schema for each tool call you will make: {'title': 'FunctionCall', 'type': 'object', 'properties': {'arguments': {'title': 'Arguments', 'type': 'object'}, 'name': {'title': 'Name', 'type': 'string'}}, 'required': ['arguments', 'name']}For each function call return a json object with function name and arguments within <tool_call></tool_call>  XML tags as follows:
+<tool_call> 
+{tool_call}
+</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> 
 
- 
+Hi, I need to convert 500 USD to Euros. Can you help me with that?<|end|><|assistant|><think> Okay, so the user is asking to convert 500 USD to Euros. I need to figure out which function to use. Looking at the available tools, there's a function called convert_currency that does exactly that. It requires the amount, the currency to convert from, and the currency to convert to. The user provided all these details: 500 USD to Euros. So, I'll call the convert_currency function with those parameters.
 
+I should make sure to format the function call correctly, using the specified JSON schema. The function name is 'convert_currency', and the arguments are amount: 500, from_currency: 'USD', and to_currency: 'EUR'. That should do it.
+
+Alright, I think that's all I need to do here. The user's request is straightforward, and the function is perfectly suited for this task.
+<|tool|><|tool|> 
+{'name': 'convert_currency', 'arguments': {'amount': 500, 'from_currency': 'USD', 'to_currency': 'EUR'}}
+<|tool|><|end|><|tool|> 
+<tool_response>
+{'converted_amount': 425.5, 'from_currency': 'USD', 'to_currency': 'EUR'}
+</tool_response><|end|><|assistant|> Sure, 500 USD is approximately 425.50 Euros.<|end|>
+```
+
+## Training procedure
 
 This model was trained with SFT.
 
@@ -42,8 +81,6 @@ This model was trained with SFT.
 
 ## Citations
 
-
-
 Cite TRL as:
     
 ```bibtex