Add TogetherHandler to BFCL api_inference

berkeley-function-call-leaderboard/bfcl/eval_checker/model_metadata.py

@@ -894,7 +894,61 @@
         "https://huggingface.co/uiuc-convai/CALM-405B",
         "UIUC + Oumi"
         "Meta Llama 3 Community"
-    ]
+    ],
+    "meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo": [
+        "Llama-3.1-8B-Instruct (Prompt)",
+        "https://llama.meta.com/llama3",
+        "Meta",
+        "Meta Llama 3 Community",
+    ],
+    "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo": [
+        "Llama-3.1-8B-Instruct (Prompt)",
+        "https://llama.meta.com/llama3",
+        "Meta",
+        "Meta Llama 3 Community",
+    ],
+    "meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo": [
+        "Llama-3.1-8B-Instruct (Prompt)",
+        "https://llama.meta.com/llama3",
+        "Meta",
+        "Meta Llama 3 Community",
+    ],
+    "meta-llama/Llama-3.3-70B-Instruct-Turbo": [
+        "Llama-3.1-8B-Instruct (Prompt)",
+        "https://llama.meta.com/llama3",
+        "Meta",
+        "Meta Llama 3 Community",
+    ],
+    "mistralai/Mixtral-8x7B-Instruct-v0.1": [
+        "Open-Mixtral-8x7b (Prompt)",
+        "https://mistral.ai/news/mixtral-of-experts/",
+        "Mistral AI",
+        "Proprietary",
+    ],
+    "mistralai/Mistral-7B-Instruct-v0.1": [
+        "Open-Mixtral-8x7b (Prompt)",
+        "https://mistral.ai/news/mixtral-of-experts/",
+        "Mistral AI",
+        "Proprietary",
+    ],
+    "Qwen/Qwen2.5-7B-Instruct-Turbo": [
+        "Qwen2.5-7B-Instruct (Prompt)",
+        "https://huggingface.co/Qwen/Qwen2.5-7B-Instruct",
+        "Qwen",
+        "apache-2.0",
+    ],
+    "Qwen/Qwen2.5-72B-Instruct-Turbo": [
+        "Qwen2.5-72B-Instruct (Prompt)",
+        "https://huggingface.co/Qwen/Qwen2.5-72B-Instruct",
+        "Qwen",
+        "qwen",
+    ],
+    "deepseek-ai/DeepSeek-V3": [
+        "DeepSeek-V3 (FC)",
+        "https://api-docs.deepseek.com/news/news1226",
+        "DeepSeek",
+        "DeepSeek License",
+    ],
 }
 
 INPUT_PRICE_PER_MILLION_TOKEN = {

berkeley-function-call-leaderboard/bfcl/model_handler/api_inference/together.py

@@ -0,0 +1,104 @@
+import os
+import time
+
+from bfcl.model_handler.model_style import ModelStyle
+from bfcl.model_handler.api_inference.openai import OpenAIHandler
+from openai import OpenAI
+
+
+class TogetherHandler(OpenAIHandler):
+    def __init__(self, model_name, temperature) -> None:
+        super().__init__(model_name, temperature)
+        self.model_style = ModelStyle.TOGETHER_AI
+        self.client = OpenAI(
+            base_url="https://api.together.xyz/v1",
+            api_key=os.getenv("TOGETHER_API_KEY"),
+        )
+
+    #### FC methods ####
+
+    def _query_FC(self, inference_data: dict):
+        message: list[dict] = inference_data["message"]
+        tools = inference_data["tools"]
+        inference_data["inference_input_log"] = {"message": message, "tools": tools}
+
+        start_time = time.time()
+        if len(tools) > 0:
+            api_response = self.client.chat.completions.create(
+                messages=message,
+                model=self.model_name,
+                temperature=self.temperature,
+                tools=tools,
+            )
+        else:
+            api_response = self.client.chat.completions.create(
+                messages=message,
+                model=self.model_name,
+                temperature=self.temperature,
+            )
+        end_time = time.time()
+
+        return api_response, end_time - start_time
+
+    def _pre_query_processing_FC(self, inference_data: dict, test_entry: dict) -> dict:
+        return super()._pre_query_processing_FC(inference_data, test_entry)
+
+    def _compile_tools(self, inference_data: dict, test_entry: dict) -> dict:
+        return super()._compile_tools(inference_data, test_entry)
+
+    def _parse_query_response_FC(self, api_response: any) -> dict:
+        try:
+            model_responses = [
+                {func_call.function.name: func_call.function.arguments}
+                for func_call in api_response.choices[0].message.tool_calls
+            ]
+            tool_calls = [
+                tool_call.model_dump()
+                for tool_call in api_response.choices[0].message.tool_calls
+            ]
+        except:
+            model_responses = api_response.choices[0].message.content
+            tool_calls = []
+
+        return {
+            "model_responses": model_responses,
+            "model_responses_message_for_chat_history": tool_calls,
+            "input_token": api_response.usage.prompt_tokens,
+            "output_token": api_response.usage.completion_tokens,
+        }
+
+    def add_first_turn_message_FC(
+        self, inference_data: dict, first_turn_message: list[dict]
+    ) -> dict:
+        return super().add_first_turn_message_FC(inference_data, first_turn_message)
+
+    def _add_next_turn_user_message_FC(
+        self, inference_data: dict, user_message: list[dict]
+    ) -> dict:
+        return super()._add_next_turn_user_message_FC(inference_data, user_message)
+
+    def _add_assistant_message_FC(
+        self, inference_data: dict, model_response_data: dict
+    ) -> dict:
+        inference_data["message"].append(
+            {
+                "role": "assistant",
+                "content": "",
+                "tool_calls": model_response_data[
+                    "model_responses_message_for_chat_history"
+                ],
+            }
+        )
+        return inference_data
+
+    def _add_execution_results_FC(
+        self, inference_data: dict, execution_results: list[str], model_response_data: dict
+    ) -> dict:
+        for execution_result in execution_results:
+            tool_message = {
+                "role": "tool",
+                "content": execution_result,
+            }
+            inference_data["message"].append(tool_message)
+
+        return inference_data

berkeley-function-call-leaderboard/bfcl/model_handler/handler_map.py

@@ -13,6 +13,7 @@
 from bfcl.model_handler.api_inference.nova import NovaHandler
 from bfcl.model_handler.api_inference.nvidia import NvidiaHandler
 from bfcl.model_handler.api_inference.openai import OpenAIHandler
+from bfcl.model_handler.api_inference.together import TogetherHandler
 from bfcl.model_handler.api_inference.writer import WriterHandler
 from bfcl.model_handler.api_inference.yi import YiHandler
 from bfcl.model_handler.local_inference.bielik import BielikHandler
@@ -102,6 +103,15 @@
     # "yi-large-fc": YiHandler,  #  Their API is under maintenance, and will not be back online in the near future
     "palmyra-x-004": WriterHandler,
     "grok-beta": GrokHandler,
+    "meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo": TogetherHandler,
+    "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo": TogetherHandler,
+    "meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo": TogetherHandler,
+    "meta-llama/Llama-3.3-70B-Instruct-Turbo": TogetherHandler,
+    "mistralai/Mixtral-8x7B-Instruct-v0.1": TogetherHandler,
+    "mistralai/Mistral-7B-Instruct-v0.1": TogetherHandler,
+    "Qwen/Qwen2.5-7B-Instruct-Turbo": TogetherHandler,
+    "Qwen/Qwen2.5-72B-Instruct-Turbo": TogetherHandler,
+    "deepseek-ai/DeepSeek-V3": TogetherHandler,
 }
 
 # Inference through local hosting

berkeley-function-call-leaderboard/bfcl/model_handler/model_style.py

@@ -10,6 +10,7 @@ class ModelStyle(Enum):
     Google = "google"
     AMAZON = "amazon"
     FIREWORK_AI = "firework_ai"
+    TOGETHER_AI = "together_ai"
     NEXUS = "nexus"
     OSSMODEL = "ossmodel"
     COHERE = "cohere"

berkeley-function-call-leaderboard/bfcl/model_handler/utils.py

@@ -147,6 +147,7 @@ def convert_to_tool(functions, mapping, model_style):
                 ModelStyle.Anthropic,
                 ModelStyle.Google,
                 ModelStyle.FIREWORK_AI,
+                ModelStyle.TOGETHER_AI,
                 ModelStyle.WRITER,
                 ModelStyle.AMAZON,
             ]:
@@ -166,6 +167,7 @@ def convert_to_tool(functions, mapping, model_style):
             ModelStyle.OpenAI,
             ModelStyle.Mistral,
             ModelStyle.FIREWORK_AI,
+            ModelStyle.TOGETHER_AI,
             ModelStyle.WRITER,
         ]:
             oai_tool.append({"type": "function", "function": item})