Update README.md

README.md

@@ -34,8 +34,6 @@ In addition, we evaluated the Hammer 2.1 models on other academic benchmarks to
 
 Hammer 2.1 models showcase highly stable performance, suggesting the robustness of Hammer 2.1 series. In contrast, the baseline approaches display varying levels of effectiveness.
 
-## Tuning Details
-Thanks so much for your attention, a report with all the technical details leading to our models will be published soon.
 
 
 
@@ -43,15 +41,153 @@ Thanks so much for your attention, a report with all the technical details leadi
 The code of Hammer 2.1 models have been in the latest Hugging face transformers and we advise you to install `transformers>=4.34.0`.
 
 ## How to Use
-Hammer2.1 models offer flexibility in deployment and usage, fully supporting both vLLM deployment and Hugging Face Transformers tool calling. For more detailed examples and use cases, please refer to the [examples/README_USING.md](https://github.com/MadeAgents/Hammer/tree/main/examples/README_USING.md) in our repository. 
-This is a simple example of how to use our model.
-~~~python
+Hammer models offer flexibility in deployment and usage, fully supporting both **vLLM** deployment and **Hugging Face Transformers** tool calling. Below are the specifics on how to make use of these features:
+
+### Using vLLM
+#### Option 1: Using Hammer client
+vLLM offers efficient serving with lower latency. To serve the model with vLLM:
+```
+vllm serve MadeAgents/Hammer2.1-1.5b --host 0.0.0.0 --port 8000 --tensor-parallel-size 1
+```
+Once the model is served, you can use the following Hammer client to interact with it for function calling:
+~~~
+from client import HammerChatCompletion,HammerConfig
+config = HammerConfig(base_url="http://localhost:8000/v1/", model="MadeAgents/Hammer2.1-1.5b")
+llm = HammerChatCompletion.from_config(config)
+
+# Example conversation
+messages = [
+    {"role": "user", "content": "What's the weather like in New York?"},
+    {"role": "assistant","content": '```\n{"name": "get_weather", "arguments": {"location": "New York, NY ", "unit": "celsius"}\n```'},
+    {"role": "tool", "name": "get_weather", "content": '{"temperature": 72, "description": "Partly cloudy"}'},
+    {"role": "user", "content": "Now, search for the weather in San Francisco."}
+]
+
+# Example function definition (optional)
+tools = [
+    {
+        "name": "get_weather",
+        "description": "Get the current weather for a location",
+        "parameters": {
+            "type": "object",
+            "properties": {
+                "location": {"type": "string", "description": "The city and state, e.g. San Francisco, CA"},
+                "unit": {"type": "string", "enum": ["celsius", "fahrenheit"], "description": "The unit of temperature to return"}
+            },
+            "required": ["location"]
+        }
+    },
+    {
+        "name": "respond",
+        "description": "When you are ready to respond, use this function. This function allows the assistant to formulate and deliver appropriate replies based on the input message and the context of the conversation. Generate a concise response for simple questions, and a more detailed response for complex questions.",
+        "parameters": {
+            "type": "object",
+            "properties": {
+                "message": {"type": "string", "description": "The content of the message to respond to."}
+            },
+            "required": ["message"]
+        }
+    }
+]
+
+response = llm.completion(messages, tools=tools)
+print(response)
+~~~
+
+
+#### Option 2: Using vLLM’s built-in tool calling
+Hammer2.1 supports vllm’s built-in tool calling. This functionality requires vllm>=0.6. If you want to enable this functionality, please start vllm’s OpenAI-compatible service with:
+~~~
+vllm serve MadeAgents/Hammer2.1-1.5b --enable-auto-tool-choice --tool-call-parser hermes
+~~~
+And then use it in the same way you use GPT’s tool calling:
+~~~
+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",
+                    },
+                    "format": {
+                        "type": "string",
+                        "enum": ["celsius", "fahrenheit"],
+                        "description": "The temperature unit to use. Infer this from the users location.",
+                        "default": "celsius"
+                    },
+                },
+                "required": ["location","format"],
+            },
+        }
+    },
+    {
+        "type": "function",
+        "function": {
+            "name": "get_n_day_weather_forecast",
+            "description": "Get an N-day weather forecast",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "location": {
+                        "type": "string",
+                        "description": "The city and state, e.g. San Francisco, CA",
+                    },
+                    "format": {
+                        "type": "string",
+                        "enum": ["celsius", "fahrenheit"],
+                        "description": "The temperature unit to use. Infer this from the users location.",
+                        "default": "celsius"
+                    },
+                    "num_days": {
+                        "type": "integer",
+                        "description": "The number of days to forecast",
+                        "default": 1
+                    }
+                },
+                "required": ["location", "format", "num_days"]
+            },
+        }
+    },
+]
+
+
+from openai import OpenAI
+openai_api_key = "None"
+openai_api_base = "http://localhost:8000/v1"
+
+client = OpenAI(
+    api_key=openai_api_key,
+    base_url=openai_api_base,
+)
+
+query = """What's the weather like today in San Francisco"""
+
+chat_response = client.chat.completions.create(
+    model="MadeAgents/Hammer2.1-1.5b",
+    messages=[
+        {"role": "user", "content": query},],
+    tools = tools,
+    temperature=0
+)
+print(chat_response.choices[0].message.content)
+~~~
+
+
+### Using Hugging Face Transformers
+Hammer2.1’s chat template also includes a tool calling template, meaning that you can use Hugging Face transformers’ tool calling support. This is a simple example of how to use our model using Transformers.
+~~~
 import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
 
-tokenizer = AutoTokenizer.from_pretrained("MadeAgents/Hammer2.1-0.5b")
-model = AutoModelForCausalLM.from_pretrained("MadeAgents/Hammer2.1-0.5b", torch_dtype=torch.bfloat16, device_map="auto")
+tokenizer = AutoTokenizer.from_pretrained("MadeAgents/Hammer2.1-1.5b")
+model = AutoModelForCausalLM.from_pretrained("MadeAgents/Hammer2.1-1.5b", torch_dtype=torch.bfloat16, device_map="auto")
 
 # Example conversation
 messages = [