This is the 70B parameter version of the Llama 3 Groq Tool Use model, specifically designed for advanced tool use and function calling tasks.
Model Details
Model Type: Causal language model fine-tuned for tool use
Language(s): English
License: Meta Llama 3 Community License
Model Architecture: Optimized transformer
Training Approach: Full fine-tuning and Direct Preference Optimization (DPO) on Llama 3 70B base model
Input: Text
Output: Text, with enhanced capabilities for tool use and function calling
Performance
Berkeley Function Calling Leaderboard (BFCL) Score: 90.76% overall accuracy
This score represents the best performance among all open-source 70B LLMs on the BFCL
Usage and Limitations
This model is designed for research and development in tool use and function calling scenarios. It excels at tasks involving API interactions, structured data manipulation, and complex tool use. However, users should note:
For general knowledge or open-ended tasks, a general-purpose language model may be more suitable
The model may still produce inaccurate or biased content in some cases
Users are responsible for implementing appropriate safety measures for their specific use case
Note the model is quite sensitive to the temperature and top_p sampling configuration. Start at temperature=0.5, top_p=0.65 and move up or down as needed.
Text prompt example:
We'd like to give a special shoutout to @NousResearch for pushing open source tool use forward with their public & open exploration of tool use in LLMs.
<|start_header_id|>system<|end_header_id|>
You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows:
<tool_call>
{"name": <function-name>,"arguments": <args-dict>}
</tool_call>
Here are the available tools:
<tools> {
"name": "get_current_weather",
"description": "Get the current weather in a given location",
"parameters": {
"properties": {
"location": {
"description": "The city and state, e.g. San Francisco, CA",
"type": "string"
},
"unit": {
"enum": [
"celsius",
"fahrenheit"
],
"type": "string"
}
},
"required": [
"location"
],
"type": "object"
}
} </tools><|eot_id|><|start_header_id|>user<|end_header_id|>
What is the weather like in San Francisco?<|eot_id|><|start_header_id|>assistant<|end_header_id|>
<tool_call>
{"id":"call_deok","name":"get_current_weather","arguments":{"location":"San Francisco","unit":"celsius"}}
</tool_call><|eot_id|><|start_header_id|>tool<|end_header_id|>
<tool_response>
{"id":"call_deok","result":{"temperature":"72","unit":"celsius"}}
</tool_response><|eot_id|><|start_header_id|>assistant<|end_header_id|>
Ethical Considerations
While fine-tuned for tool use, this model inherits the ethical considerations of the base Llama 3 model. Use responsibly and implement additional safeguards as needed for your application.
For full details on responsible use, ethical considerations, and latest benchmarks, please refer to the official Llama 3 documentation and the Groq model card.
