mitzen

This is my main.py which uses the  Qwen3.8-2B-Distill-GGUF model. And the instruction is pretty much the same as previous model how ever, it does not seems to 'want' to read from start until end.  And because of that, the results is not so accurate.  """Have the model READ a README, detect its runtime, and extract the commands. Then start a matching container, run each command in it, and stop it. Usage:     python run_agent.py path/to/README.md """ import argparse import asyncio import inspect import json import re import shlex from pathlib import Path from dotenv import load_dotenv from google . adk . agents import LlmAgent from google . adk . runners import Runner from google . adk . sessions import InMemorySessionService from google . genai import types from pydantic import BaseModel , Field load_dotenv () # Reuse the same model your commander agent uses. from app . agent import root_agent   # ASSUMPTION: plain fu...

When running my commander app with XHToken/Spark-X2.5-4B-GGUF the result is pretty good. The model is able to follow instruction and extracts out the right container image to run and the commands too.   This is my codebase for setting up my model XHToken/Spark-X2.5-4B-GGUF. import os from dotenv import load_dotenv from google . adk . agents import LlmAgent from google . adk . models . lite_llm import LiteLlm from google . adk . tools import google_search # Create a LiteLLM model pointing to your local server model = LiteLlm (     model = "openai/XHToken/Spark-X2.5-4B-GGUF" ,     api_base = "http://localhost:8888/v1" ,   # Your local server     api_key = "sk-unsloth-4d0a1b198bd177a2a72ee1954585342a" ,     temperature = 0.0 ,     extra_body = { "chat_template_kwargs" : { "enable_thinking" : False }}, ) from app . prompt import ROOT_AGENT_INSTRUCTION from app . tools . container_toolset import (   ...

 After trying out a couple of model, I decides to see if using a different model would sway the results differently. I am using ornith-ai/Ornith-1.0-9B-GGUF. model here.  Noticed that I have added "openai" in front of the model name otherwise it throw an error message. import os from dotenv import load_dotenv from google . adk . agents import LlmAgent from google . adk . models . lite_llm import LiteLlm from google . adk . tools import google_search # Create a LiteLLM model pointing to your local server model = LiteLlm (     model = "openai/ornith-ai/Ornith-1.0-9B-GGUF" ,     api_base = "http://localhost:8888/v1" ,   # Your local server     api_key = "sk-unsloth-4d0a1b198bd177a2a72ee1954585342a" ,     temperature = 0.0 ) from app . prompt import ROOT_AGENT_INSTRUCTION from app . tools . container_toolset import (     container_image_finder_tool , run_container_command_tool ) load_dotenv () root_agent ...