Create README.md

README.md

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+---
+license: other
+license_name: katanemo-research
+license_link: >-
+  https://huggingface.co/katanemolabs/Arch-Function-Chat-1.5B/blob/main/LICENSE
+base_model:
+- Qwen/Qwen2.5-Coder-1.5B-Instruct
+language:
+- en
+pipeline_tag: text-generation
+library_name: transformers
+---
+
+# katanemo/Arch-Function-Chat-1.5B
+
+## Overview
+The Arch-Function-Chat collection builds upon the Katanemo's [Arch-Function](https://huggingface.co/collections/katanemo/arch-function-66f209a693ea8df14317ad68) collection by extending its capabilities beyond basic function calling. This new collection maintains the state-of-the-art function calling abilities of the original while adding powerful new features that make it even more versatile in real-world applications.
+
+In addition to the core function calling capabilities, this enhanced collection now offers:
+
+- **Intent matching**: Automatically identifies user intent and maps it to the most appropriate functions
+- **Parameter gathering**: Generates natural follow-up questions to collect missing required parameters
+- **Result interpretation**: Provides human-friendly responses based on function execution results
+- **Multi-turn dialogue management**: Maintains context throughout complex interactions
+
+# Requirements
+The code of Arch-Function-Chat-1.5B has been in the Hugging Face `transformers` library and we advise you to install latest version:
+```bash
+pip install transformers>=4.37.0
+```
+
+
+# How to use
+We use the following example to illustrate how to use our model to perform function calling tasks. Please note that, our model works best with our provided prompt format. It allows us to extract JSON output that is similar to the [OpenAI's function calling](https://platform.openai.com/docs/guides/function-calling).
+
+
+### Quickstart
+````python
+import json
+from typing import Any, Dict, List
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "katanemo/Arch-Function-Chat-1.5B"
+model = AutoModelForCausalLM.from_pretrained(
+    model_name, device_map="auto", torch_dtype="auto", trust_remote_code=True
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# Please use our provided prompt for best performance
+TASK_PROMPT = (
+    "You are a helpful assistant designed to assist with the user query by making one or more function calls if needed."
+    "\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>\n{tools}\n</tools>"
+    "\n\nYour task is to decide which functions are needed and collect missing parameters if necessary."
+)
+
+FORMAT_PROMPT = (
+    "\n\nBased on your analysis, provide your response in one of the following JSON formats:"
+    '\n1. If no functions are needed:\n```json\n{"response": "Your response text here"}\n```'
+    '\n2. If functions are needed but some required parameters are missing:\n```json\n{"required_functions": ["func_name1", "func_name2", ...], "clarification": "Text asking for missing parameters"}\n```'
+    '\n3. If functions are needed and all required parameters are available:\n```json\n{"tool_calls": [{"name": "func_name1", "arguments": {"argument1": "value1", "argument2": "value2"}},... (more tool calls as required)]}\n```'
+)
+
+# Define available tools
+get_weather_api = {
+    "type": "function",
+    "function": {
+        "name": "get_weather",
+        "description": "Get the current weather for a location",
+        "parameters": {
+            "type": "object",
+            "properties": {
+                "location": {
+                    "type": "str",
+                    "description": "The city and state, e.g. San Francisco, New York",
+                },
+                "unit": {
+                    "type": "str",
+                    "enum": ["celsius", "fahrenheit"],
+                    "description": "The unit of temperature to return",
+                },
+            },
+            "required": ["location"],
+        },
+    },
+}
+
+openai_format_tools = [get_weather_api]
+
+
+def convert_tools(tools: List[Dict[str, Any]]):
+    converted = [json.dumps(tool["function"], ensure_ascii=False) for tool in tools]
+    return "\n".join(converted)
+
+# Helper function to create the system prompt for our model
+def format_prompt(tools: List[Dict[str, Any]]):
+    tools = convert_tools(tools)
+    return  TASK_PROMPT.format(tools=tools) + FORMAT_PROMPT
+
+system_prompt = format_prompt(openai_format_tools)
+
+messages = [
+    {"role": "system", "content": system_prompt},
+    {"role": "user", "content": "What is the weather in Seattle?"},
+]
+
+inputs = tokenizer.apply_chat_template(
+    messages, add_generation_prompt=True, return_tensors="pt"
+).to(model.device)
+
+outputs = model.generate(
+    inputs,
+    max_new_tokens=512,
+    do_sample=False,
+    num_return_sequences=1,
+    eos_token_id=tokenizer.eos_token_id,
+)
+
+response = tokenizer.decode(outputs[0][len(inputs[0]) :], skip_special_tokens=True)
+print(response)
+````
+
+Then you should be able to see the following output string in JSON format:
+````python
+```json
+{"tool_calls": [{"name": "get_weather", "arguments": {"location": "Seattle"}}]}
+```
+````
+
+# License
+Katanemo Arch-Function collection is distributed under the [Katanemo license](https://huggingface.co/katanemolabs/Arch-Function-Chat-1.5B/blob/main/LICENSE).