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 *.zip filter=lfs diff=lfs merge=lfs -text
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+agent.png filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,146 @@
+---
+license: other
+language:
+- en
+library_name: transformers
+tags:
+- RLHF
+- Nexusflow
+- Athene
+- Function Calling
+- Agent
+- Extraction
+base_model:
+- Qwen/Qwen2.5-72B-Instruct
+---
+
+# Athene-V2-Agent: Surpassing GPT-4o for Tool Use And Agentic Usecases
+<p align="center">
+<a href="https://huggingface.co/Nexusflow" target="_blank">Nexusflow HF</a> - <a href="https://discord.gg/HDSVmNAs3y" target="_blank">Nexusflow Discord</a> - <a href="https://nexusflow.ai/blogs/athene-v2" target="_blank">Athene-V2 Blogpost</a>
+</p>
+
+<p align="center" width="100%">
+<a><img src="agent.png" alt="NexusRaven" style="width: 40%; min-width: 300px; display: block; margin: auto;"></a>
+</p>
+
+## Introducing Athene-V2-Agent
+Athene-V2-Agent is an open-source Agent LLM that surpasses the state-of-the-art in function calling and agentic capabilities. 
+
+
+<p align="center" width="100%">
+<a><img src="benchmark.png" alt="Benchmark" style="width: 100%; min-width: 300px; display: block; margin: auto;"></a>
+</p>
+
+
+💪 **Versatile Agent Capability**: Athene-V2-Agent is an agent model, capable of operating in environments with deeply nested dependencies with the environment. It is capable of reasoning and doing planning for trajectories with many tool calls necessary to answer a single query.
+
+📊 **Performance Highlights**: Athene-V2-Agent surpasses GPT-4o in single FC tasks by 18% in function calling success rates, and by 17% in Agentic success rates. 
+
+🔧 **Generalization to the Unseen**: Athene-V2-Agent has never been trained on the functions or agentic settings used in evaluation.
+
+- **Developed by:** The Nexusflow Team
+- **Model type:** Agent Model
+- **Finetuned from model:** [Qwen-2.5-72B-Intruct](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct)
+- **License**: [Nexusflow Research License](https://huggingface.co/Nexusflow/Athene-V2-Agent/blob/main/Nexusflow_Research_License_.pdf)
+- **Blog**: https://nexusflow.ai/blogs/athene-v2
+
+## Athene-V2-Agent Model Usage
+
+### OpenAI-Compatible FC
+
+Athene-V2-Agent is usable in any OpenAI API-compatible environment using our VLLM docker image. This should be a simple "drop-in" replacement to any agentic or tool-use setting with our VLLM docker image.
+
+```
+docker run --name athene-v2-agent \
+    --runtime nvidia --gpus '"device=0,1,2,3,4,5,6,7"' \
+    -v ~/.cache/huggingface:/root/.cache/huggingface \
+    --env "HUGGING_FACE_HUB_TOKEN=<secret>" \
+    -p <port>:8000 \
+    --ipc=host \
+    ghcr.io/nexusflowai/athene-v2-vllm:latest \
+    --model Nexusflow/Athene-V2-Agent \
+    --dtype=auto \
+    --tensor-parallel-size=8 \
+    --enable-auto-tool-choice \
+    --tool-call-parser Athene-V2-Agent
+```
+
+You can now submit any OpenAI-Compatible tool-use requests to the model by hitting the VLLM endpoint. Athene-V2-Agent will be able to issue tool calls that you can execute and return results for. 
+
+
+**WARNING**: Athene-V2-Agent uses a *CUSTOM* prompting style that is baked into the custom docker image, as the executable calls are extracted from the model's generated planning. For best performance, please ensure to use the docker image above for Athene-V2-Agent, including when benchmarking the model. Using HuggingFace tokenizer's chat template will yield suboptimal results for Agent usecases. Please reach out to us on Discord if you run into any issues!
+### Examples
+
+An example Weather agent for this can be found here: [Link](example/vllm_v2_weather_agent.py#L186-L193). This example includes handling Athene for queries that are answerable and not answerable by the current tools. 
+
+An example extraction and RAG-Agent can be found here: [Link](example/vllm_v2_extraction_agent.py#L270-L284). This example includes handling RAG-based queries with a wikipedia tool.
+
+### Prompting Tricks
+
+1. When giving docstrings to Athene-V2-Agent, please provide well-indented, detailed, and well-written docstrings as this can help accuracy.
+2. We strongly recommend using the docker image to interact with Athene-V2-Agent.
+4. We strongly recommend to set sampling to False when prompting Athene-V2-Agent.
+5. We strongly recommend a zero temperature.
+6. Athene-V2-Agent is designed to work within systems, so it's tuned to be very controllable with the instructions specified in the tools, including for broad behaviors (like rejecting queries, or chatting)
+
+#### Handling Irrelevant Queries
+The Athene-V2-Agent model is strongly tuned to have its behavior be controllable with tools to make it easy to integrate into systems.
+
+Therefore, the model won't by default reject queries that are out of domain, as it will try its best to issue the most relevant call.
+However, when expecting irrelevant user queries and wanting the model to reject them, you can use a no-op function. For example, something like this would work:
+
+```python
+{
+    "type": "function",
+    "function" : {
+      "name": "no_relevant_function",
+      "description": "Call this when no other provided function can be called to answer the user query.",
+      "parameters": {
+        "type": "object",
+        "properties": {
+          "user_query_span": {
+            "type": "string",
+            "description": "The part of the user_query that cannot be answered by any other function calls."
+          }
+        },
+        "required": ["user_query_span"]
+      }
+    }
+}
+```
+Please see the example [Link](example/vllm_v2_weather_agent.py) here for a demo of this.
+
+#### Handling Chat With FC
+
+Since Athene-V2-Agent model is strongly tuned to be controllable, so we wanted to ensure that it does not chat unless explicitly instructed to do so.
+You can do this by adding a `chat` tool, and allowing it to do so in the system prompt: 
+
+```python
+{
+    "type": "function",
+    "function": {
+        "name": "chat",
+        "description": "Call this tool when you want to chat with the user. The user won't see anything except for whatever you pass into this function. You can use this tool to ask for more information when insufficient information is presented, and to send the final results back to the user.",
+        "parameters": {
+            "type": "object",
+            "properties": {
+                "chat_string": {
+                    "type": "string",
+                    "description": "The chat message to send to the user to chat back to them.",
+                }
+            },
+            "required": ["chat_string"],
+        },
+    },
+}
+```
+
+And the following system prompt, as an example (but feel free to experiment to make Athene-V2-Agent behave the way you want it to!):
+```python
+{"role" : "system", "content" : "You can use the chat tool to ask the user for more information, and to send the final results."},
+```
+
+Please see the example [Link](example/weather_with_chat.py) here for a demo of this.
+
+## Contact
+Please join our [Discord Channel](https://discord.gg/HDSVmNAs3y) to reach out for any issues and comments!

added_tokens.json

@@ -0,0 +1,26 @@
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+  "</tool_call>": 151658,
+  "<bot_end>": 151666,
+  "<human_end>": 151665,
+  "<tool_call>": 151657,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
+  "<|endoftext|>": 151643,
+  "<|file_sep|>": 151664,
+  "<|fim_middle|>": 151660,
+  "<|fim_pad|>": 151662,
+  "<|fim_prefix|>": 151659,
+  "<|fim_suffix|>": 151661,
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+  "<|object_ref_end|>": 151647,
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+  "<|quad_end|>": 151651,
+  "<|quad_start|>": 151650,
+  "<|repo_name|>": 151663,
+  "<|video_pad|>": 151656,
+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

cal_data.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:fd5e1ca32fb5a02397420920d3d542015344cf30d8777a35c712679bb4221872
+size 1638488

config.json

@@ -0,0 +1,30 @@
+{
+  "_name_or_path": "Nexusflow/Athene-V2-Agent",
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "eos_token_id": 151645,
+  "hidden_act": "silu",
+  "hidden_size": 8192,
+  "initializer_range": 0.02,
+  "intermediate_size": 29568,
+  "max_position_embeddings": 32768,
+  "max_window_layers": 70,
+  "model_type": "qwen2",
+  "num_attention_heads": 64,
+  "num_hidden_layers": 80,
+  "num_key_value_heads": 8,
+  "qwen_type": "qwen2",
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000.0,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.46.2",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151672
+}

example/vllm_v2_extraction_agent.py

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+from dataclasses import dataclass
+from typing import List, Dict, Any, Optional
+import json
+import requests
+from bs4 import BeautifulSoup
+from openai import OpenAI
+
+"""
+EXAMPLE OUTPUT:
+
+What is the current population for the city  where Einstein was born?
+
+Step 1
+----------------------------------------
+
+Executing: fetch_wiki_content
+Arguments: {'title': 'Albert Einstein'}
+
+Step 2
+----------------------------------------
+
+Executing: deliver_answer
+Arguments: {'fields': ['Ulm, German Empire']}
+ANSWER FROM THE ASSISTANT: ['Ulm, German Empire']
+
+Step 3
+----------------------------------------
+
+Executing: fetch_wiki_content
+Arguments: {'title': 'Ulm'}
+
+Step 4
+----------------------------------------
+
+Executing: deliver_answer
+Arguments: {'fields': ['128,928']}
+ANSWER FROM THE ASSISTANT: ['128,928']
+
+Step 5
+----------------------------------------
+Extraction Complete
+
+
+Why was Einstein famous?
+
+Step 1
+----------------------------------------
+
+Executing: fetch_wiki_content
+Arguments: {'title': 'Albert Einstein'}
+
+Step 2
+----------------------------------------
+
+Executing: deliver_answer
+Arguments: {'fields': ['Best known for developing the theory of relativity, Einstein also made important contributions to quantum mechanics.', 'His mass–energy equivalence formula E = mc2, which arises from special relativity, has been called "the world\'s most famous equation."', 'He received the 1921 Nobel Prize in Physics.']}
+ANSWER FROM THE ASSISTANT: ['Best known for developing the theory of relativity, Einstein also made important contributions to quantum mechanics.', 'His mass–energy equivalence formula E = mc2, which arises from special relativity, has been called "the world\'s most famous equation."', 'He received the 1921 Nobel Prize in Physics.']
+
+Step 3
+----------------------------------------
+Extraction Complete
+"""
+
+@dataclass
+class WikiConfig:
+    """Configuration for OpenAI and Wikipedia settings"""
+    api_key: str = "sk-123"
+    api_base: str = "{info}/v1"
+    model: Optional[str] = None
+    max_steps: int = 5
+    wikipedia_base_url: str = "https://en.wikipedia.org/wiki/"
+
+class WikiTools:
+    """Collection of Wikipedia and extraction tools"""
+
+    def __init__(self, base_url: str):
+        self.base_url = base_url
+
+    def fetch_wiki_content(self, title: str, section: Optional[str] = None) -> str:
+        """Fetch and clean Wikipedia article content, optionally from a specific section"""
+        url = f"{self.base_url}{title.replace(' ', '_')}"
+        response = requests.get(url)
+        soup = BeautifulSoup(response.content, 'html.parser')
+
+        # Remove unwanted sections
+        for unwanted in soup.find_all(['script', 'style', 'footer', 'header']):
+            unwanted.decompose()
+
+        if section:
+            # Find specific section if requested
+            section_tag = soup.find('span', {'id': section})
+            if section_tag:
+                content = section_tag.parent.find_next_siblings()
+                text = ' '.join(tag.get_text() for tag in content)
+            else:
+                return "Section not found"
+        else:
+            # Get main content
+            content = soup.find(id='mw-content-text')
+            if content:
+                text = content.get_text()
+            else:
+                return "Content not found"
+
+        # Clean and normalize text
+        text = ' '.join(text.split())
+        return text[:8000]  # Truncate to avoid token limits
+
+    @staticmethod
+    def deliver_answer(fields: List[str]) -> Dict[str, Any]:
+        """Extract specific information from text spans"""
+        print (f"ANSWER FROM THE ASSISTANT: {fields}")
+        return {
+            "extracted_fields": "Provided fields was delivered to the user successfully."
+        }
+
+class ToolRegistry:
+    """Registry of available tools and their schemas"""
+
+    def __init__(self, wiki_tools: WikiTools):
+        self.wiki_tools = wiki_tools
+
+    @property
+    def available_functions(self) -> Dict[str, callable]:
+        return {
+            "fetch_wiki_content": self.wiki_tools.fetch_wiki_content,
+            "deliver_answer": self.wiki_tools.deliver_answer
+        }
+
+    @property
+    def tool_schemas(self) -> List[Dict[str, Any]]:
+        return [
+            {
+                "type": "function",
+                "function": {
+                    "name": "fetch_wiki_content",
+                    "description": "Fetch content from a Wikipedia article",
+                    "parameters": {
+                        "type": "object",
+                        "properties": {
+                            "title": {
+                                "type": "string",
+                                "description": "The title of the Wikipedia article"
+                            },
+                            "section": {
+                                "type": "string",
+                                "description": "Optional: Specific section ID to fetch",
+                                "optional": True
+                            }
+                        },
+                        "required": ["title"]
+                    }
+                }
+            },
+            {
+                "type": "function",
+                "function": {
+                    "name": "deliver_answer",
+                    "description": "Extract specific information from the fetched text",
+                    "parameters": {
+                        "type": "object",
+                        "properties": {
+                            "fields": {
+                                "type": "array",
+                                "items": {"type": "string"},
+                                "description": "List of text spans from the article that are relevant to the query"
+                            }
+                        },
+                        "required": ["fields"]
+                    }
+                }
+            }
+        ]
+
+class WikiExtractionAgent:
+    """Main agent class that handles the extraction process"""
+
+    def __init__(self, config: WikiConfig):
+        self.config = config
+        self.client = OpenAI(api_key=config.api_key, base_url=config.api_base)
+        self.wiki_tools = WikiTools(config.wikipedia_base_url)
+        self.tools = ToolRegistry(self.wiki_tools)
+        self.messages = [{"system" : "1. First fetch any wikipedia pages you might need to answer the user query. Do not answer from parametric knowledge.\n\n2.Then, provide the answer to the user using the deliver_answer from the retrieved wikipedia page.\n\n3. You may need to issue multiple calls to wikipedia after extracting answers if there are nested dependencies for information."}]
+
+        if not config.model:
+            models = self.client.models.list()
+            self.config.model = models.data[0].id
+
+    def _serialize_tool_call(self, tool_call) -> Dict[str, Any]:
+        """Convert tool call to serializable format"""
+        return {
+            "id": tool_call.id,
+            "type": tool_call.type,
+            "function": {
+                "name": tool_call.function.name,
+                "arguments": tool_call.function.arguments
+            }
+        }
+
+    def process_tool_calls(self, message) -> List[Dict[str, Any]]:
+        """Process and execute tool calls from assistant"""
+        results = []
+
+        for tool_call in message.tool_calls:
+            function_name = tool_call.function.name
+            function_args = json.loads(tool_call.function.arguments)
+
+            print(f"\nExecuting: {function_name}")
+            print(f"Arguments: {function_args}")
+
+            function_response = self.tools.available_functions[function_name](**function_args)
+            results.append({
+                "tool": function_name,
+                "args": function_args,
+                "response": function_response
+            })
+
+            self.messages.append({
+                "role": "tool",
+                "content": json.dumps(function_response),
+                "tool_call_id": tool_call.id,
+                "name": function_name
+            })
+
+        return results
+
+    def extract_information(self, query: str) -> List[Dict[str, Any]]:
+        """Main method to handle the extraction process"""
+        self.messages = [{
+            "role": "user",
+            "content": f"""Extract information from Wikipedia to answer this query: {query}
+
+            You can use these tools:
+            1. fetch_wiki_content: Get article content
+            2. deliver_answer: deliver relevant information
+
+            Please fetch content first, and iterate as needed to get to the webpage with the correct answer and then deliver the relevant information."""
+        }]
+
+        all_results = []
+
+        for step in range(self.config.max_steps):
+            print(f"\nStep {step + 1}")
+            print("-" * 40)
+
+            response = self.client.chat.completions.create(
+                messages=self.messages,
+                model=self.config.model,
+                tools=self.tools.tool_schemas,
+                temperature=0.0,
+            )
+
+            message = response.choices[0].message
+
+            if not message.tool_calls:
+                print("Extraction Complete")
+                break
+
+            self.messages.append({
+                "role": "assistant",
+                "content": json.dumps(message.content),
+                "tool_calls": [self._serialize_tool_call(tc) for tc in message.tool_calls]
+            })
+
+            results = self.process_tool_calls(message)
+            all_results.extend(results)
+
+        return all_results
+
+def main():
+    # Example usage
+    config = WikiConfig()
+    agent = WikiExtractionAgent(config)
+
+    # Multi-step query example
+    # The model should first issue a call to wikipedia for Einstein, extract the part from the document about where he was born
+    # and use the value from that extraction (which could contain the city name) to call another wikipedia article for the city
+    # and pull the population from it.
+    # See lines 11 to 41 for the full trace of this actual query that Athene-V2-Agent issues.
+    results = agent.extract_information(
+        query="""What is the current population for the city  where Einstein was born?"""
+    )
+    
+    # Single query example
+    # Here, the model should just issue a call to Einstein's wikipedia page, and extract the parts regarding his
+    # accomplishment. 
+    results = agent.extract_information(
+        query="Why was Einstein famous?"
+    )
+
+
+if __name__ == "__main__":
+    main()

example/vllm_v2_weather_agent.py

@@ -0,0 +1,234 @@
+from dataclasses import dataclass
+import json
+from typing import List, Dict, Any, Optional
+from openai import OpenAI
+"""
+EXAMPLE OUTPUT:
+
+****************************************
+RUNNING QUERY: What's the weather for Paris, TX in fahrenheit?
+Step 1
+----------------------------------------
+
+Executing: get_geo_coordinates
+Arguments: {'city': 'Paris', 'state': 'TX'}
+Response: The coordinates for Paris, TX are: latitude 33.6609, longitude 95.5555
+
+Step 2
+----------------------------------------
+
+Executing: get_current_weather
+Arguments: {'latitude': [33.6609], 'longitude': [95.5555], 'unit': 'fahrenheit'}
+Response: The weather is 85 degrees fahrenheit. It is partly cloudy, with highs in the 90's.
+
+Step 3
+----------------------------------------
+Conversation Complete
+
+
+****************************************
+RUNNING QUERY: Who won the most recent PGA?
+Step 1
+----------------------------------------
+
+Executing: no_relevant_function
+Arguments: {'user_query_span': 'Who won the most recent PGA?'}
+Response: No relevant function for your request was found. We will stop here.
+
+Step 2
+----------------------------------------
+Conversation Complete
+"""
+
+@dataclass
+class WeatherConfig:
+    """Configuration for OpenAI and API settings"""
+    api_key: str = "" # FILL IN WITH YOUR VLLM_ENDPOINT_KEY
+    api_base: str = "" # FILL IN WITH YOUR VLLM_ENDPOINT
+    model: Optional[str] = None
+    max_steps: int = 5
+
+class WeatherTools:
+    """Collection of available tools/functions for the weather agent"""
+
+    @staticmethod
+    def get_current_weather(latitude: List[float], longitude: List[float], unit: str) -> str:
+        """Get weather for given coordinates"""
+        # We are mocking the weather here, but in the real world, you will submit a request here.
+        return f"The weather is 85 degrees {unit}. It is partly cloudy, with highs in the 90's."
+
+    @staticmethod
+    def get_geo_coordinates(city: str, state: str) -> str:
+        """Get coordinates for a given city"""
+        coordinates = {
+            "Dallas": {"TX": (32.7767, -96.7970)},
+            "San Francisco": {"CA": (37.7749, -122.4194)},
+            "Paris": {"TX": (33.6609, 95.5555)}
+        }
+        lat, lon = coordinates.get(city, {}).get(state, (0, 0))
+        # We are mocking the weather here, but in the real world, you will submit a request here.
+        return f"The coordinates for {city}, {state} are: latitude {lat}, longitude {lon}"
+
+    @staticmethod
+    def no_relevant_function(user_query_span : str) -> str:
+        return "No relevant function for your request was found. We will stop here."
+
+class ToolRegistry:
+    """Registry of available tools and their schemas"""
+
+    @property
+    def available_functions(self) -> Dict[str, callable]:
+        return {
+            "get_current_weather": WeatherTools.get_current_weather,
+            "get_geo_coordinates": WeatherTools.get_geo_coordinates,
+            "no_relevant_function" : WeatherTools.no_relevant_function,
+        }
+
+    @property
+    def tool_schemas(self) -> List[Dict[str, Any]]:
+        return [
+            {
+                "type": "function",
+                "function": {
+                    "name": "get_current_weather",
+                    "description": "Get the current weather in a given location. Use exact coordinates.",
+                    "parameters": {
+                        "type": "object",
+                        "properties": {
+                            "latitude": {"type": "array", "description": "The latitude for the city."},
+                            "longitude": {"type": "array", "description": "The longitude for the city."},
+                            "unit": {
+                                "type": "string",
+                                "description": "The unit to fetch the temperature in",
+                                "enum": ["celsius", "fahrenheit"]
+                            }
+                        },
+                        "required": ["latitude", "longitude", "unit"]
+                    }
+                }
+            },
+            {
+                "type": "function",
+                "function": {
+                    "name": "get_geo_coordinates",
+                    "description": "Get the latitude and longitude for a given city",
+                    "parameters": {
+                        "type": "object",
+                        "properties": {
+                            "city": {"type": "string", "description": "The city to find coordinates for"},
+                            "state": {"type": "string", "description": "The two-letter state abbreviation"}
+                        },
+                        "required": ["city", "state"]
+                    }
+                }
+            },
+            {
+                "type": "function",
+                "function" : {
+                  "name": "no_relevant_function",
+                  "description": "Call this when no other provided function can be called to answer the user query.",
+                  "parameters": {
+                    "type": "object",
+                    "properties": {
+                      "user_query_span": {
+                        "type": "string",
+                        "description": "The part of the user_query that cannot be answered by any other function calls."
+                      }
+                    },
+                    "required": ["user_query_span"]
+                  }
+                }
+            }
+        ]
+
+class WeatherAgent:
+    """Main agent class that handles the conversation and tool execution"""
+
+    def __init__(self, config: WeatherConfig):
+        self.config = config
+        self.client = OpenAI(api_key=config.api_key, base_url=config.api_base)
+        self.tools = ToolRegistry()
+        self.messages = []
+
+        if not config.model:
+            models = self.client.models.list()
+            self.config.model = models.data[0].id
+
+    def _serialize_tool_call(self, tool_call) -> Dict[str, Any]:
+        """Convert tool call to serializable format"""
+        return {
+            "id": tool_call.id,
+            "type": tool_call.type,
+            "function": {
+                "name": tool_call.function.name,
+                "arguments": tool_call.function.arguments
+            }
+        }
+
+    def process_tool_calls(self, message) -> None:
+        """Process and execute tool calls from assistant"""
+        for tool_call in message.tool_calls:
+            function_name = tool_call.function.name
+            function_args = json.loads(tool_call.function.arguments)
+
+            print(f"\nExecuting: {function_name}")
+            print(f"Arguments: {function_args}")
+
+            function_response = self.tools.available_functions[function_name](**function_args)
+            print(f"Response: {function_response}")
+
+            self.messages.append({
+                "role": "tool",
+                "content": json.dumps(function_response),
+                "tool_call_id": tool_call.id,
+                "name": function_name
+            })
+
+    def run_conversation(self, initial_query: str) -> None:
+        """Run the main conversation loop"""
+        self.messages = [{"role": "user", "content": initial_query}]
+
+        print ("\n" * 5)
+        print ("*" * 40)
+        print (f"RUNNING QUERY: {initial_query}")
+
+        for step in range(self.config.max_steps):
+            print(f"\nStep {step + 1}")
+            print("-" * 40)
+
+            response = self.client.chat.completions.create(
+                messages=self.messages,
+                model=self.config.model,
+                tools=self.tools.tool_schemas,
+                temperature=0.0,
+            )
+
+            message = response.choices[0].message
+
+            if not message.tool_calls:
+                print("Conversation Complete")
+                break
+
+            self.messages.append({
+                "role": "assistant",
+                "content": json.dumps(message.content),
+                "tool_calls": [self._serialize_tool_call(tc) for tc in message.tool_calls]
+            })
+
+            self.process_tool_calls(message)
+
+        if step >= self.config.max_steps - 1:
+            print("Maximum steps reached")
+
+def main():
+    # Example usage
+    config = WeatherConfig()
+    agent = WeatherAgent(config)
+    agent.run_conversation("What's the weather for Paris, TX in fahrenheit?")
+
+    # Example OOD usage
+    agent.run_conversation("Who won the most recent PGA?")
+
+
+if __name__ == "__main__":
+    main()

example/weather_with_chat.py

@@ -0,0 +1,246 @@
+from dataclasses import dataclass
+import json
+from typing import List, Dict, Any, Optional
+from openai import OpenAI
+"""
+EXAMPLE OUTPUT:
+
+****************************************
+RUNNING QUERY: What's the weather for Paris, TX in fahrenheit?
+
+Agent Issued Step 1
+----------------------------------------
+
+Agent Issued Step 2
+----------------------------------------
+
+Agent Issued Step 3
+----------------------------------------
+AGENT MESSAGE:  The current weather in Paris, TX is 85 degrees fahrenheit. It is partly cloudy, with highs in the 90s.
+Conversation Complete
+
+
+****************************************
+RUNNING QUERY: Who won the most recent PGA?
+
+Agent Issued Step 1
+----------------------------------------
+
+Agent Issued Step 2
+----------------------------------------
+AGENT MESSAGE:  I'm sorry, but I don't have the ability to provide sports information. I can help you with weather and location data. Is there anything else I can assist you with?
+Conversation Complete
+"""
+
+@dataclass
+class WeatherConfig:
+    """Configuration for OpenAI and API settings"""
+    api_key: str = "" # The VLLM api_key
+    api_base: str = "" # The VLLM api_base URL
+    model: Optional[str] = None
+    max_steps: int = 5
+
+class WeatherTools:
+    """Collection of available tools/functions for the weather agent"""
+
+    @staticmethod
+    def get_current_weather(latitude: List[float], longitude: List[float], unit: str) -> str:
+        """Get weather for given coordinates"""
+        # We are mocking the weather here, but in the real world, you will submit a request here.
+        return f"The weather is 85 degrees {unit}. It is partly cloudy, with highs in the 90's."
+
+    @staticmethod
+    def get_geo_coordinates(city: str, state: str) -> str:
+        """Get coordinates for a given city"""
+        coordinates = {
+            "Dallas": {"TX": (32.7767, -96.7970)},
+            "San Francisco": {"CA": (37.7749, -122.4194)},
+            "Paris": {"TX": (33.6609, 95.5555)}
+        }
+        lat, lon = coordinates.get(city, {}).get(state, (0, 0))
+        # We are mocking the weather here, but in the real world, you will submit a request here.
+        return f"The coordinates for {city}, {state} are: latitude {lat}, longitude {lon}"
+
+    @staticmethod
+    def no_relevant_function(user_query_span : str) -> str:
+        return "No relevant function for your request was found. We will stop here."
+
+    @staticmethod
+    def chat(chat_string : str):
+        print ("AGENT MESSAGE: ", chat_string)
+
+class ToolRegistry:
+    """Registry of available tools and their schemas"""
+
+    @property
+    def available_functions(self) -> Dict[str, callable]:
+        return {
+            "get_current_weather": WeatherTools.get_current_weather,
+            "get_geo_coordinates": WeatherTools.get_geo_coordinates,
+            "no_relevant_function" : WeatherTools.no_relevant_function,
+            "chat" : WeatherTools.chat
+        }
+
+    @property
+    def tool_schemas(self) -> List[Dict[str, Any]]:
+        return [
+            {
+                "type": "function",
+                "function": {
+                    "name": "get_current_weather",
+                    "description": "Get the current weather in a given location. Use exact coordinates.",
+                    "parameters": {
+                        "type": "object",
+                        "properties": {
+                            "latitude": {"type": "array", "description": "The latitude for the city."},
+                            "longitude": {"type": "array", "description": "The longitude for the city."},
+                            "unit": {
+                                "type": "string",
+                                "description": "The unit to fetch the temperature in",
+                                "enum": ["celsius", "fahrenheit"]
+                            }
+                        },
+                        "required": ["latitude", "longitude", "unit"]
+                    }
+                }
+            },
+            {
+                "type": "function",
+                "function": {
+                    "name": "get_geo_coordinates",
+                    "description": "Get the latitude and longitude for a given city",
+                    "parameters": {
+                        "type": "object",
+                        "properties": {
+                            "city": {"type": "string", "description": "The city to find coordinates for"},
+                            "state": {"type": "string", "description": "The two-letter state abbreviation"}
+                        },
+                        "required": ["city", "state"]
+                    }
+                }
+            },
+            {
+                "type": "function",
+                "function" : {
+                  "name": "no_relevant_function",
+                  "description": "Call this when no other provided function can be called to answer the user query.",
+                  "parameters": {
+                    "type": "object",
+                    "properties": {
+                      "user_query_span": {
+                        "type": "string",
+                        "description": "The part of the user_query that cannot be answered by any other function calls."
+                      }
+                    },
+                    "required": ["user_query_span"]
+                  }
+                }
+            },
+            {
+                "type": "function",
+                "function": {
+                    "name": "chat",
+                    "description": "Call this tool when you want to chat with the user. The user won't see anything except for whatever you pass into this function.",
+                    "parameters": {
+                        "type": "object",
+                        "properties": {
+                            "chat_string": {
+                                "type": "string",
+                                "description": "The string to send to the user to chat back to them.",
+                            }
+                        },
+                        "required": ["chat_string"],
+                    },
+                },
+            },
+        ]
+
+class WeatherAgent:
+    """Main agent class that handles the conversation and tool execution"""
+
+    def __init__(self, config: WeatherConfig):
+        self.config = config
+        self.client = OpenAI(api_key=config.api_key, base_url=config.api_base)
+        self.tools = ToolRegistry()
+        self.messages = []
+
+        if not config.model:
+            models = self.client.models.list()
+            self.config.model = models.data[0].id
+
+    def _serialize_tool_call(self, tool_call) -> Dict[str, Any]:
+        """Convert tool call to serializable format"""
+        return {
+            "id": tool_call.id,
+            "type": tool_call.type,
+            "function": {
+                "name": tool_call.function.name,
+                "arguments": tool_call.function.arguments
+            }
+        }
+
+    def process_tool_calls(self, message) -> None:
+        """Process and execute tool calls from assistant"""
+        for tool_call in message.tool_calls:
+            function_name = tool_call.function.name
+            function_args = json.loads(tool_call.function.arguments)
+
+            function_response = self.tools.available_functions[function_name](**function_args)
+
+            self.messages.append({
+                "role": "tool",
+                "content": json.dumps(function_response),
+                "tool_call_id": tool_call.id,
+                "name": function_name
+            })
+
+    def run_conversation(self, initial_query: str) -> None:
+        """Run the main conversation loop"""
+        self.messages = [
+                {"role" : "system", "content" : "Make sure to use the chat() function to provide the final answer to the user."},
+                {"role": "user", "content": initial_query}]
+
+        print ("\n" * 5)
+        print ("*" * 40)
+        print (f"RUNNING QUERY: {initial_query}")
+
+        for step in range(self.config.max_steps):
+
+            response = self.client.chat.completions.create(
+                messages=self.messages,
+                model=self.config.model,
+                tools=self.tools.tool_schemas,
+                temperature=0.0,
+            )
+
+            message = response.choices[0].message
+
+            if not message.tool_calls:
+                print("Conversation Complete")
+                break
+
+            print(f"\nAgent Issued Step {step + 1}")
+            print("-" * 40)
+
+            self.messages.append({
+                "role": "assistant",
+                "content": json.dumps(message.content),
+                "tool_calls": [self._serialize_tool_call(tc) for tc in message.tool_calls]
+            })
+
+            self.process_tool_calls(message)
+
+        if step >= self.config.max_steps - 1:
+            print("Maximum steps reached")
+
+def main():
+    # Example usage
+    config = WeatherConfig()
+    agent = WeatherAgent(config)
+    agent.run_conversation("What's the weather for Paris, TX in fahrenheit?")
+
+    # Example OOD usage
+    agent.run_conversation("Who won the most recent PGA?")
+
+if __name__ == "__main__":
+    main()

generation_config.json

@@ -0,0 +1,12 @@
+{
+  "bos_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "pad_token_id": 151643,
+  "temperature": 0.7,
+  "top_p": 1.0,
+  "transformers_version": "4.46.2"
+}

hidden_states.safetensors

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+size 3355452040

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+size 11422270

tokenizer_config.json

@@ -0,0 +1,213 @@
+{
+  "add_bos_token": false,
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "151643": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151644": {
+      "content": "<|im_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151645": {
+      "content": "<|im_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151646": {
+      "content": "<|object_ref_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151647": {
+      "content": "<|object_ref_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151648": {
+      "content": "<|box_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151649": {
+      "content": "<|box_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151650": {
+      "content": "<|quad_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151651": {
+      "content": "<|quad_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151652": {
+      "content": "<|vision_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151653": {
+      "content": "<|vision_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151654": {
+      "content": "<|vision_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151655": {
+      "content": "<|image_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151656": {
+      "content": "<|video_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151657": {
+      "content": "<tool_call>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151658": {
+      "content": "</tool_call>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151659": {
+      "content": "<|fim_prefix|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151660": {
+      "content": "<|fim_middle|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151661": {
+      "content": "<|fim_suffix|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151662": {
+      "content": "<|fim_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151663": {
+      "content": "<|repo_name|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151664": {
+      "content": "<|file_sep|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151665": {
+      "content": "<human_end>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151666": {
+      "content": "<bot_end>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "additional_special_tokens": [
+    "<human_end>",
+    "<bot_end>"
+  ],
+  "bos_token": null,
+  "chat_template": "{%- if tools %}\n    {{- '<|im_start|>system\\n' }}\n    {%- if messages[0]['role'] == 'system' %}\n        {{- messages[0]['content'] }}\n    {%- else %}\n        {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}\n    {%- endif %}\n    {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n    {%- for tool in tools %}\n        {{- \"\\n\" }}\n        {{- tool | tojson }}\n    {%- endfor %}\n    {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n    {%- if messages[0]['role'] == 'system' %}\n        {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n    {%- else %}\n        {{- '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n' }}\n    {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n    {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n        {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n    {%- elif message.role == \"assistant\" %}\n        {{- '<|im_start|>' + message.role }}\n        {%- if message.content %}\n            {{- '\\n' + message.content }}\n        {%- endif %}\n        {%- for tool_call in message.tool_calls %}\n            {%- if tool_call.function is defined %}\n                {%- set tool_call = tool_call.function %}\n            {%- endif %}\n            {{- '\\n<tool_call>\\n{\"name\": \"' }}\n            {{- tool_call.name }}\n            {{- '\", \"arguments\": ' }}\n            {{- tool_call.arguments | tojson }}\n            {{- '}\\n</tool_call>' }}\n        {%- endfor %}\n        {{- '<|im_end|>\\n' }}\n    {%- elif message.role == \"tool\" %}\n        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n            {{- '<|im_start|>user' }}\n        {%- endif %}\n        {{- '\\n<tool_response>\\n' }}\n        {{- message.content }}\n        {{- '\\n</tool_response>' }}\n        {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n            {{- '<|im_end|>\\n' }}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "model_max_length": 131072,
+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "truncation_side": "left",
+  "unk_token": null
+}