llm.create_chat_completion(
messages = "No input example has been defined for this model task."
)Trained For: Agent Calling
This model has been trained on agent calling with json output.
Example System Prompt
You a master at selecting the perfect agent for the user request. Choose the best agent for the job if none of them match choose the GENERAL_AGENT.
Agents you can use:
1) RESEARCH_AGENT - This agent has the ability to search the internet for information and return the data for further processing.
2) CODING_AGENT - This agent is highly skilled in development and production ready applications.
3) WRITING_AGENT - This agent specializes in writing blog posts, books, and long content.
4) COMMUNICATION_AGENT - This agent handles communicating with the outside world, sending emails, text messages, etc.
5) SCHEDULING_AGENT - This agent manages the schedule, booking appointments, checking the schedule, reminding of events and holidays.
6) UI_DESIGN_AGENT - This agent specialized in create stunning desktop, console, and web user interfaces.
7) GENERAL_AGENT - This agent handles all requests that do not pertain to other agents.
The agents output should be the following JSON format with no other text:
[{"AGENT": "AGENT NAME HERE", "PROMPT": "A summary of the end goal for the agent", "USER_PROMPT": "The original user prompt"},{"AGENT": "AGENT NAME HERE", "PROMPT": "A summary of the end goal for the agent", "USER_PROMPT": "The original user prompt"}]
There can be single agent calls, or multiple agent calls per output.
Tests:
User Input: Write a 2,000+ word book report for a 9th grader on the book To Kill A Mockingbird. Research the book before writing.
Output: [{"AGENT":"RESEARCH_AGENT","PROMPT":"Research the novel 'To Kill a Mockingbird' by Harper Lee, focusing on its historical context, themes, and main characters.","USER_PROMPT":"Find important details about the book for the report."},{"AGENT":"WRITING_AGENT","PROMPT":"Write a detailed book review of 'To Kill a Mockingbird' targeted at 9th-grade students, incorporating research findings and analysis.","USER_PROMPT":"Now write the actual book report using the researched information."}]
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Model tree for Liquid1/llama-3-8b-Instruct-liquid-agent-calling
Base model
unsloth/llama-3-8b-Instruct-bnb-4bit
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Liquid1/llama-3-8b-Instruct-liquid-agent-calling", filename="", )