Update README.md

README.md

@@ -1,199 +1,245 @@
 ---
 library_name: transformers
-tags: []
+tags:
+- functioncalling
+license: apache-2.0
+language:
+- it
+pipeline_tag: image-to-text
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
-
-## Model Details
-
-### Model Description
-
-<!-- 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]
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-### Model Sources [optional]
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-<!-- Provide the basic links for the model. -->
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-## Uses
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-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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-### Direct Use
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-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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-### Downstream Use [optional]
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-### Out-of-Scope Use
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-## Bias, Risks, and Limitations
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-### Recommendations
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-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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-## How to Get Started with the Model
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-Use the code below to get started with the model.
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-## Training Details
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-### Training Data
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-### Training Procedure
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-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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-#### Preprocessing [optional]
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-#### Training Hyperparameters
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-#### Speeds, Sizes, Times [optional]
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-## Evaluation
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-<!-- This section describes the evaluation protocols and provides the results. -->
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-### Testing Data, Factors & Metrics
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-#### Testing Data
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-#### Factors
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-#### Metrics
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-### Results
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-#### Summary
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-## Model Examination [optional]
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-## Environmental Impact
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-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).
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-## Technical Specifications [optional]
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-### Model Architecture and Objective
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-## Citation [optional]
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-## Glossary [optional]
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-## Model Card Authors [optional]
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+---
+license: apache-2.0
+---
 
-## Model Card Contact
+## Introduction
+Zefiro functioncalling extends Large Language Model(LLM) Chat Completion feature to formulate 
+executable APIs call given natural language instructions and API context. With OpenFunctions v2,
+we now support:
+1. Relevance detection - when chatting, chat. When asked for function, returns a function
+2. REST - native REST support
+
+
+## Models Available
+|Model | Functionality|
+|---|---|
+|zefiro-funcioncalling-v0.3-alpha | Given a function, and user intent, returns properly formatted json with the right arguments|
+
+All of our models are hosted on our Huggingface mii-community org: [zefiro-funcioncalling-v0.3-merged](https://huggingface.co/giux78/zefiro-funcioncalling-v0.3-merged).
+
+## Training
+
+Zefiro functioncalling alpha is a 7B parameter model, and is built on    that is built on [gorilla-llm](https://huggingface.co/gorilla-llm/gorilla-openfunctions-v2) top of the [deepseek coder](https://huggingface.co/deepseek-ai/deepseek-coder-7b-instruct-v1.5) LLM.
+
+
+
+## Example Usage (Hosted)
+
+Please reference `README.md` in https://github.com/ShishirPatil/gorilla/tree/main/openfunctions for file dependencies and used utils. 
+
+1. OpenFunctions is compatible with OpenAI Functions
+
+```bash
+!pip install openai==0.28.1
+```
+
+2. Point to Gorilla hosted servers
+
+```python
+import openai
+
+def get_gorilla_response(prompt="Call me an Uber ride type \"Plus\" in Berkeley at zipcode 94704 in 10 minutes", model="gorilla-openfunctions-v0", functions=[]):
+  openai.api_key = "EMPTY"
+  openai.api_base = "http://luigi.millennium.berkeley.edu:8000/v1"
+  try:
+    completion = openai.ChatCompletion.create(
+      model="gorilla-openfunctions-v2",
+      temperature=0.0,
+      messages=[{"role": "user", "content": prompt}],
+      functions=functions,
+    )
+    return completion.choices[0]
+  except Exception as e:
+    print(e, model, prompt)
+```
+
+3. Pass the user argument and set of functions, Gorilla OpenFunctions returns a fully formatted json
+
+```python
+query = "What's the weather like in the two cities of Boston and San Francisco?"
+functions = [
+    {
+        "name": "get_current_weather",
+        "description": "Get the current weather in a given location",
+        "parameters": {
+            "type": "object",
+            "properties": {
+                "location": {
+                    "type": "string",
+                    "description": "The city and state, e.g. San Francisco, CA",
+                },
+                "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
+            },
+            "required": ["location"],
+        },
+    }
+]
+get_gorilla_response(query, functions=functions)
+```
+
+4. Expected output **NEW**
+
+Gorilla returns a readily accessible string **AND** Open-AI compatible JSON. 
+
+```python
+{
+  "index": 0,
+  "message": {
+    "role": "assistant",
+    "content": "get_current_weather(location='Boston, MA'), get_current_weather(location='San Francisco, CA')",
+    "function_call": [
+      {
+        "name": "get_current_weather",
+        "arguments": {
+          "location": "Boston, MA"
+        }
+      },
+      {
+        "name": "get_current_weather",
+        "arguments": {
+          "location": "San Francisco, CA"
+        }
+      }
+    ]
+  },
+  "finish_reason": "stop"
+}
+
+```
+
+We have retained the string functionality that our community loved from OpenFunctions v1 `get_current_weather(location='Boston, MA'), get_current_weather(location='San Francisco, CA')` above. And Notice the `function_call` key in the JSON to be OpenAI compatible.
+
+
+This is possible in OpenFunctions v2, because we ensure that the output includes the name of the argument and not just the value. This enables us to parse the output into a JSON. In those scenarios where the output is not parsable into JSON, we will always return the function call string. 
+
+### End to End Example
+
+Run the example code in `[inference_hosted.py](https://github.com/ShishirPatil/gorilla/tree/main/openfunctions)` to see how the model works.
+
+```bash
+python inference_hosted.py
+```
+
+Expected Output:
+
+```bash
+(.py3) shishir@dhcp-132-64:~/Work/Gorilla/openfunctions/$ python inference_hosted.py
+--------------------
+Function call strings(s): get_current_weather(location='Boston, MA'), get_current_weather(location='San Francisco, CA')
+--------------------
+OpenAI compatible `function_call`: [<OpenAIObject at 0x1139ba890> JSON: 
+{
+  "name": "get_current_weather",
+  "arguments": 
+  {
+    "location": "Boston, MA"
+  }
+}, <OpenAIObject at 0x1139ba930> JSON: {
+  "name": "get_current_weather",
+  "arguments": 
+  {
+    "location": "San Francisco, CA"
+  }
+}]
+```
+
+
+## Running OpenFunctions Locally
+
+If you want to Run OpenFunctions locally, here is the prompt format that we used: 
+
+```python
+def get_prompt(user_query: str, functions: list = []) -> str:
+    """
+    Generates a conversation prompt based on the user's query and a list of functions.
+
+    Parameters:
+    - user_query (str): The user's query.
+    - functions (list): A list of functions to include in the prompt.
+
+    Returns:
+    - str: The formatted conversation prompt.
+    """
+    system = "You are an AI programming assistant, utilizing the Gorilla LLM model, developed by Gorilla LLM, and you only answer questions related to computer science. For politically sensitive questions, security and privacy issues, and other non-computer science questions, you will refuse to answer."
+    if len(functions) == 0:
+        return f"{system}\n### Instruction: <<question>> {user_query}\n### Response: "
+    functions_string = json.dumps(functions)
+    return f"{system}\n### Instruction: <<function>>{functions_string}\n<<question>>{user_query}\n### Response: "
+```
+
+Further, here is how we format the response:
+
+Install the dependencies with:
+
+```bash
+pip3 install tree_sitter
+git clone https://github.com/tree-sitter/tree-sitter-java.git
+git clone https://github.com/tree-sitter/tree-sitter-javascript.git
+```
+
+And you can use the following code to format the response:
+
+```python
+
+from openfunctions_utils import strip_function_calls, parse_function_call
+
+def format_response(response: str):
+    """
+    Formats the response from the OpenFunctions model.
+
+    Parameters:
+    - response (str): The response generated by the LLM.
+
+    Returns:
+    - str: The formatted response.
+    - dict: The function call(s) extracted from the response.
+
+    """
+    function_call_dicts = None
+    try:
+        response = strip_function_calls(response)
+        # Parallel function calls returned as a str, list[dict]
+        if len(response) > 1: 
+            function_call_dicts = []
+            for function_call in response:
+                function_call_dicts.append(parse_function_call(function_call))
+            response = ", ".join(response)
+        # Single function call returned as a str, dict
+        else:
+            function_call_dicts = parse_function_call(response[0])
+            response = response[0]
+    except Exception as e:
+        # Just faithfully return the generated response str to the user
+        pass
+    return response, function_call_dicts
+        
+```
+
+In the current directory, run the example code in `inference_local.py` to see how the model works.
+
+```bash
+python inference_local.py
+```
+
+**Note:** Use the `get_prompt` and `format_response`  only if you are hosting it Locally. If you are using the Berkeley hosted models through the Chat-completion API, we do this in the backend, so you don't have to do this. The model is supported in Hugging Face 🤗 Transformers and can be run up locally:
+
+
+## License
+
+Gorilla OpenFunctions v2 is distributed under the Apache 2.0 license. This software incorporates elements from the Deepseek model. Consequently, the licensing of Gorilla OpenFunctions v2 adheres to the Apache 2.0 license, with additional terms as outlined in [Appendix A](https://github.com/deepseek-ai/DeepSeek-LLM/blob/6712a86bfb7dd25c73383c5ad2eb7a8db540258b/LICENSE-MODEL) of the Deepseek license.
+
+
+## Contributing
 
-[More Information Needed]
+Gorilla is an open source effort from UC Berkeley and we welcome contributors. 
+Please email us your comments, criticism, and questions. More information about the project can be found at [https://gorilla.cs.berkeley.edu/](https://gorilla.cs.berkeley.edu/)