Create README.md

README.md

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+---
+license: mit
+---
+# Model Card for AWQ quantisations of functionary-small-v3.2
+
+Available AWQ Quantisations:
+
+|   Revision   | Model Size | Group Size | w_bit |
+|--------------|------------|------------|-------|
+|    `main`    |   ~6.9 GB  |     32     |   4   |
+|  `gs_2_4bit` |   ~6.9 GB  |      2     |   4   |
+|  `gs_4_4bit` |   ~6.9 GB  |      4     |   4   |
+|  `gs_8_4bit` |   ~6.9 GB  |      8     |   4   |
+| `gs_16_4bit` |   ~6.9 GB  |     16     |   4   |
+| `gs_64_4bit` |   ~6.9 GB  |     64     |   4   |
+| `gs_128_4bit`|   ~6.9 GB  |    128     |   4   |
+| `gs_512_4bit`|   ~6.9 GB  |    512     |   4   |
+
+**This model was based on [meta-llama/Meta-Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct)**
+
+[https://github.com/MeetKai/functionary](https://github.com/MeetKai/functionary)
+
+<img src="https://huggingface.co/meetkai/functionary-medium-v2.2/resolve/main/functionary_logo.jpg" alt="Functionary Logo" width="300"/>
+
+Functionary is a language model that can interpret and execute functions/plugins.
+
+The model determines when to execute functions, whether in parallel or serially, and can understand their outputs. It only triggers functions as needed. Function definitions are given as JSON Schema Objects, similar to OpenAI GPT function calls.
+
+## Key Features
+
+- Intelligent **parallel tool use**
+- Able to analyze functions/tools outputs and provide relevant responses **grounded in the outputs**
+- Able to decide **when to not use tools/call functions** and provide normal chat response
+- Truly one of the best open-source alternative to GPT-4
+- Support code interpreter
+
+## How to Get Started
+
+We provide custom code for both converting tool definitions into the system prompts and parsing raw model response into a JSON object containing `role`, `content` and `tool_calls` fields. This enables the model to be able to generate tool calls.
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+tokenizer = AutoTokenizer.from_pretrained("meetkai/functionary-small-v2.5", trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained("meetkai/functionary-small-v2.5", device_map="auto", trust_remote_code=True)
+
+tools = [
+    {
+        "type": "function",
+        "function": {
+            "name": "get_current_weather",
+            "description": "Get the current weather",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "location": {
+                        "type": "string",
+                        "description": "The city and state, e.g. San Francisco, CA"
+                    }
+                },
+                "required": ["location"]
+            }
+        }
+    }
+]
+messages = [{"role": "user", "content": "What is the weather in Istanbul and Singapore respectively?"}]
+
+final_prompt = tokenizer.apply_chat_template(messages, tools, add_generation_prompt=True, tokenize=False)
+tokenizer.padding_side = "left"
+inputs = tokenizer(final_prompt, return_tensors="pt").to("cuda")
+pred = model.generate_tool_use(**inputs, max_new_tokens=128, tokenizer=tokenizer)
+print(tokenizer.decode(pred.cpu()[0]))
+```
+
+## Prompt Template
+
+We convert function definitions to a similar text to TypeScript definitions. Then we inject these definitions as system prompts. After that, we inject the default system prompt. Then we start the conversation messages.
+
+This formatting is also available via our vLLM server which we process the functions into Typescript definitions encapsulated in a system message and use a pre-defined Transformers chat template. This means that lists of messages can be formatted for you with the apply_chat_template() method within our server:
+
+```python
+from openai import OpenAI
+
+client = OpenAI(base_url="http://localhost:8000/v1", api_key="functionary")
+
+client.chat.completions.create(
+    model="path/to/functionary/model/",
+    messages=[{"role": "user",
+            "content": "What is the weather for Istanbul?"}
+    ],
+    tools=[{
+            "type": "function",
+            "function": {
+                "name": "get_current_weather",
+                "description": "Get the current weather",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "location": {
+                            "type": "string",
+                            "description": "The city and state, e.g. San Francisco, CA"
+                        }
+                    },
+                    "required": ["location"]
+                }
+            }
+        }],
+    tool_choice="auto"
+)
+```
+
+will yield:
+
+```
+<|start_header_id|>system<|end_header_id|>
+
+You are capable of executing available function(s) if required.
+Only execute function(s) when absolutely necessary.
+Ask for the required input to:recipient==all
+Use JSON for function arguments.
+Respond in this format:
+>>>${recipient}
+${content}
+Available functions:
+// Supported function definitions that should be called when necessary.
+namespace functions {
+
+// Get the current weather
+type get_current_weather = (_: {
+// The city and state, e.g. San Francisco, CA
+location: string,
+}) => any;
+
+} // namespace functions<|eot_id|><|start_header_id|>user<|end_header_id|>
+
+What is the weather for Istanbul?
+```
+
+A more detailed example is provided [here](https://github.com/MeetKai/functionary/blob/main/tests/prompt_test_v3.llama3.txt). 
+
+## Run the model
+
+We encourage users to run our models using our OpenAI-compatible vLLM server [here](https://github.com/MeetKai/functionary).
+
+# The MeetKai Team
+![MeetKai Logo](https://huggingface.co/meetkai/functionary-medium-v2.2/resolve/main/meetkai_logo.png "MeetKai Logo")