Update README.md

README.md

@@ -5,102 +5,134 @@ tags:
 - sft
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-# Model Card for Model ID
+# Model Card for phi-2-function-calling
 
-<!-- Provide a quick summary of what the model is/does. -->
+## Model Overview
 
+### Summary of the Model
 
+The primary purpose of this fine-tuned model is **Function Calling**. It is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) specifically adapted to handle function-calling tasks efficiently. The model can generate structured text, making it particularly suited for scenarios requiring automated function invocation based on textual instructions.
 
-## Model Details
-
-### Model Description
+### Model Type
 
-<!-- Provide a longer summary of what this model is. -->
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
 
-### Model Sources [optional]
 
-<!-- Provide the basic links for the model. -->
+## Model Details
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+### Model Description
 
-## Uses
+- **Developed by:** Microsoft and Fine-tuned by Carlos Rodrigues (at DataKensei)
+- **Model Type:** Text Generation, trained for Function Calling tasks.
+- **Language(s):** English
+- **License:** MIT License
+- **Finetuned from model:** [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) 
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+### Model Sources
 
-### Direct Use
+- **Repository:** - **Repository:** [DataKensei/phi-2-function-calling](https://huggingface.co/DataKensei/phi-2-function-calling)
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+## Uses
 
-[More Information Needed]
+### Direct Use
 
-### Downstream Use [optional]
+The model is directly usable for generating function calls based on user prompts. This includes structured tasks like scheduling meetings, calculating savings, or any scenario where a text input should translate into an actionable function.
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+### Downstream Use
 
-[More Information Needed]
+While the model is primarily designed for function calling, it can be fine-tuned further or integrated into larger systems where similar structured text generation is required. For example, it could be part of a larger chatbot system that automates task handling.
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+The model is not designed for tasks unrelated to structured text generation or function calling. Misuse might include attempts to use it for general-purpose language modeling or content generation beyond its specialized training focus.
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+### Biases
 
-[More Information Needed]
+The model may inherit biases from the base model (microsoft/phi-2), particularly those related to the English language and specific function-calling tasks. Users should be aware of potential biases in task framing and language interpretation.
 
-### Recommendations
+### Limitations
+
+- **Task-Specific**: The model is specialized for function-calling tasks and might not perform well on other types of text generation tasks.
+- **English Only**: The model is limited to English, and performance in other languages is not guaranteed.
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+### Recommendations
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+Users should test the model in their specific environment to ensure it performs as expected for the desired use case. Awareness of the model's biases and limitations is crucial when deploying it in critical systems.
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
+You can use the following code snippet to get started with the model:
+
+```python
+from transformers import pipeline
+
+# Load the model and tokenizer
+pipe = pipeline(task="text-generation", model="DataKensei/phi-2-function-calling")
+
+# Example prompt
+prompt = '''
+<|im_start|system
+You are a helpful assistant with access to the following functions. Use these functions when they are relevant to assist with a user's request
+[
+    {
+        "name": "calculate_retirement_savings",
+        "description": "Project the savings at retirement based on current contributions.",
+        "parameters": {
+            "type": "object",
+            "properties": {
+                "current_age": {
+                    "type": "integer",
+                    "description": "The current age of the individual."
+                },
+                "retirement_age": {
+                    "type": "integer",
+                    "description": "The desired retirement age."
+                },
+                "current_savings": {
+                    "type": "number",
+                    "description": "The current amount of savings."
+                },
+                "monthly_contribution": {
+                    "type": "number",
+                    "description": "The monthly contribution towards retirement savings."
+                }
+            },
+            "required": ["current_age", "retirement_age", "current_savings", "monthly_contribution"]
+        }
+    }
+]
+<|im_start|user
+I am currently 40 years old and plan to retire at 65. I have no savings at the moment, but I intend to save $500 every month. Could you project the savings at retirement based on current contributions?
+'''
+
+result = pipe(prompt)
+print(result[0]['generated_text'])
+```
 
-[More Information Needed]
 
 ## Training Details
 
 ### Training Data
 
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+The model was fine-tuned using a syntectic dataset of function-calling prompts and responses. The data was curated to cover a wide range of potential function calls, ensuring the model's applicability to various structured text generation tasks.
 
-[More Information Needed]
+The script to generate the data can be found in this [repository](https://xxxxxxxx).
 
 ### Training Procedure
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
+- **Training regime:** The model was fine-tuned using 4-bit precision with `bnb_4bit` quantization on NVIDIA GPUs.
+- **Optimizer:** PagedAdamW (32-bit)
+- **Learning Rate:** 2e-4
+- **Batch Size:** 2 (with gradient accumulation steps = 1)
+- **Epochs:** 1
 
-[More Information Needed]
+#### Preprocessing 
 
+The training and evaluation data was generated using this [repository](https://xxxxxxxx).
 
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
 
 ## Evaluation
 
@@ -110,15 +142,8 @@ Use the code below to get started with the model.
 
 #### Testing Data
 
-<!-- This should link to a Dataset Card if possible. -->
+The model was evaluated using a separate test set, comprising 10% of the original dataset, containing various function-calling scenarios.
 
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
 
 #### Metrics
 
@@ -133,30 +158,23 @@ Use the code below to get started with the model.
 #### Summary
 
 
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
 ## Environmental Impact
 
 <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
 
 Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
 
-- **Hardware Type:** [More Information Needed]
+- **Hardware Type:** NVIDIA GPUs
 - **Hours used:** [More Information Needed]
 - **Cloud Provider:** [More Information Needed]
 - **Compute Region:** [More Information Needed]
 - **Carbon Emitted:** [More Information Needed]
 
-## Technical Specifications [optional]
+## Technical Specifications
 
 ### Model Architecture and Objective
 
-[More Information Needed]
+The model is based on the "microsoft/phi-2" architecture, fine-tuned specifically for function-calling tasks. The objective was to optimize the model's ability to generate structured text suitable for automated function execution.
 
 ### Compute Infrastructure
 
@@ -164,38 +182,26 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 
 #### Hardware
 
-[More Information Needed]
+The model was trained on NVIDIA GPUs.
 
 #### Software
 
-[More Information Needed]
+The training used PyTorch and the Hugging Face Transformers library, with additional support from the PEFT library for fine-tuning.
 
-## Citation [optional]
+## Citation
 
 <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
 
 **BibTeX:**
 
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
+@misc{phi2functioncalling,
+  title={phi-2-function-calling},
+  author={Carlos Rodrigues},
+  year={2024},
+  publisher={Hugging Face},
+  howpublished={\url{https://huggingface.co/DataKensei/phi-2-function-calling}},
+}
 
 ## Model Card Contact
 
-[More Information Needed]
+For more information, please contact Carlos Rodrigues at DataKensei.