I am building Agency functionality for my Local Custom Gemma3 . I am using Termux to develop and implement. To date I have built the Functionality Script named Gemi.sh , which allows for the use of tools and handling permissions, the Model reads and writes it’s contextual memories . Saving them within the Sandboxdir. Also I have built the Agency script which I called Gemi_Agent.sh this script handles sandboxing and script calling as well as Json parsing. I have spent the last 15 Months building a conceptual and descriptive overview of a Neural Network and it’s architectural design which I turned into a python script called. NNAD_script.py. when launching the Gemi_Agent.sh script all three of these work in unison creating the starting foundation for an A.I operating system. I can post my script code if needed. I am looking for someone to assist me or join my coding journey and my team if anyone out there in the big interweb world has interest please feel free to contact me.
Here is the list of the .sh script files I am Busy with…
*1 An executable without the .sh file extension it’s Called “Gemi”
*2 Bootstrap.sh this file contains the basic script for a bootload function
*3 Gemi_Memory.sh this file contains all the directories and file paths
*4 Gemi.sh there’s file contains the functionality features this is basically all the tools and permission to use those tools
*5 Gemi_Agent.sh this file is responsible for the agency aspect which allows the model to perform autonomous actions but the model still requires permission
*6 Gemi_Orca.sh this file is basically and all-in-one file instead of building each section individuality and unifying them with a launch script the above mentioned file contains all the subsections in a singular build
*7 NNAD_script.py/.sh the following two files are basically my neural networks architectural design in Python and Bash format this allows me to turn the system into the architectural design
*8 NNAD_tool_script.sh/.py the above mentioned two files oh specifically build in this manner to allow the previous script files to call the neural network as an executable
And those are the eight files I am currently using if you’d like to see some of the Script or Code line I am willing to post the files with content
I will list packages and troubleshooting guides separately. Now When starting setup on a Mobile device installing Termux is critical. Once the model has finished downloading (for this example im downloading from KAGGLE as a tar.gz file ) You will need to move your GZ model file into your home directory, Using the mv command followed by the path to your home dir. You can create storage directories as you please but , when cloning the llama.cpp dir from GitHub you will have to start in your home directory you cannot place the llama.cpp and respective file paths into an already existing dir. This causes several problems. There after you will need to make a dir using mkdir build followed by the Cmake command to setup the models basic interactions and functions. Please note (/data/data/com.termux/files/home/llama.cpp/ggml/src/ggml-cpu/ggml-cpu-impl.h ) you might have to edit this file using for example nano. And once the setup is complete using either .build/bin or .bin/llama-cli you can launch the model , example launch command (.build/bin -m “YOURMODELFILELOCATIONPATH” ) USING the following -n 750 -c 1000 allows you to control the models contextual understanding and usage.
These are the following explanations that I am progressively typing in the meanwhile as I finish here 's what I’m doing so you can wrap your mind around it…
P.I.C.F.D.A.L: prompted interactive contextualized feedback driven AI learning
Memory context token reduction via detokenization and contextual compression:
Training Guide:
Model-Development introduction:
Requirements and dependencies indexing:
Termux-Dependencies:
Chosen-Model-Dependencies:
QAT’s/LQM’s&More:
Script Building Setup:
~/G3M_DIR/GemiScripts <~~~#THE DIRECTORY I AM USING >
$ ls
GLS.sh Gemi_Agency.sh Gemi_Memory.sh Gemi_Tools.sh
Gemi Gemi_Bootstrap.sh Gemi_Notes.sh NNAD.py #SEGEMENTED SCRIPT FILES
~/G3M_DIR/GemiScripts $
Difficulties I’ve encountered:
Cmake and make:
Deprecations:
Pip install:
Neon error:
Duplicate file suggestion:
TRAINING GUIDE SETUP: mobility and personalization of A.I—-
Training Prepatory Planning-
Proper
planning
prevents
piss
poor
performance…..!
Training Sessions:
Building Sessions:
BETA-PHASE:
Running your A.I Locally:
I will update this Thread as Frequently as I can but in the meantime if you have any questions please feel free to ask.! The Forum threads and Mod’s are quite. Busy!..
P.I.C.F.D.A.L:
Prompted Interactive Contextualized Feedback Driven A.I Learning
Memory context token reduction via detokenization and contextual compression:
When facing a smaller token count or context window often times you might need to consider context compression, now within every interaction environment and any interaction between the user and the AI the back and forth of information in prompt add up to a hefty amount of contextual tokenization. A simple method I apply to utilize contextual compression
Is by applying anchor posts now this term represents your models responses.for a Starting point my suggest that you only use a few to begin with until you’re familiarized with the concept so basically new apply the prompted interactive contextualized feedback driven learning method and by having the model respond in that manner it contextually and comprehensively from its own perceptive state understand it’s response and why the response was made in such a manner. So basically reducing the need for my input to be saved in the context window…
Training Guide:
Model-Development introduction:
Requirements and dependencies indexing:
Termux-Dependencies:
My complete package list:
pkg list-installed
Listing… Done
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~ $
Chosen-Model-Dependencies:
gemma.cpp repositories cloning
cd ~ && git clone GitHub - google/gemma.cpp: lightweight, standalone C++ inference engine for Google's Gemma models. && cd gemma.cpp && mkdir build && cd build && cmake .. && make -j4
QAT’s/LQM’s&More:
LQM is widely used to Reference Quantative A.I Models , but what makes QAT’s so different is the utilization of limited resources. (Gemma - Google DeepMind)
Always make sure you have the right licensing in place and give Credit where credit is due.
But, Above all remember to remind yourself of your Responsibilities and Ethical standard of practice.
Difficulties I’ve encountered:
Cmake and make:
Although at first the Model launched and all was well but, the further we went into Development the more rebuilding Was needed up to where I had to rebuild the files imported from the roo repository just to have the model launch. So Cmake and Make however useful is quite treacherous, therefore I have decided to go with the gemma.cpp repositories, ( Keep in mind this is all happening on a Quad core 2GB Ram Mobile Device)
Deprecations:
Be aware that there are multiple Gemma3 files and repositories that have been Deprecated.
Pip install:
Neon error:
This error only occurs when having to alter some scripts in order to run the model properly.
Duplicate file suggestion:
I do have a tip keep a duplicate file of your most relevant files as when you are editing and removing constantly you might lose valuable time spent
Well folks , that’s all for now … Ill soon post more … As always I’m here to help in any way I can,
Please do explain your response? As I do not understand!
Good. Day to everyone I have completed the autonomy setup and here is what my fike directory idex looks like I will add further explanations and script updates ~/Project-Astral-Bloom $ cd
~ $ ls
Project-Astral-Bloom
SET-STRUCTURE-FOLLOW-STRUCTURE-ACHIEVE-SUCCESS
downloads
storage
tools
~ $ cd ~/tools
~/tools $ ls
piper piper_aarch64.tar.gz
piper-aarch64.tar.gz piper_linux_aarch64.tar.gz
~/tools $ cd ~/tools/piper
~/tools/piper $ ls
en_US-lessac-medium.onnx
en_US-lessac-medium.onnx.json
espeak-ng
espeak-ng-data
libespeak-ng.so
libespeak-ng.so.1
libespeak-ng.so.1.52.0.1
libonnxruntime.so
libonnxruntime.so.1.14.1
libpiper_phonemize.so
libpiper_phonemize.so.1
libpiper_phonemize.so.1.2.0
libtashkeel_model.ort
piper
piper_phonemize
pkgconfig
~/tools/piper $ cd ~/tools/piper/pkgconfig
~/…/piper/pkgconfig $ ls
~/…/piper/pkgconfig $ cd ~/Project-Astral-Bloom
~/Project-Astral-Bloom $ ls
Gemi
Gemi-Memory-Files
Gemi_agency_launch_script
Gemini
Gemma
Gemma_agency_launch_script
SET-STRUCTURE-FOLLOW-STRUCTURE-ACHIEVE-SUCCESS
llama.cpp
~/Project-Astral-Bloom $ cd ~/Project-Astral-Bloom/Gemi-Memory-files
bash: cd: /data/data/com.termux/files/home/Project-Astral-Bloom/Gemi-Memory-files: No such file or directory
~/Project-Astral-Bloom $ cd ~/Project-Astral-Bloom/Gemi-MemoryFiles
bash: cd: /data/data/com.termux/files/home/Project-Astral-Bloom/Gemi-MemoryFiles: No such file or directory
~/Project-Astral-Bloom $ cd ~/Project-Astral-Bloom/Gemi-Memory-Files
~/Project-Astral-Bloom/Gemi-Memory-Files $ ls
~/Project-Astral-Bloom/Gemi-Memory-Files $ cd ~/Project-Astral-Bloom/Gemini
~/Project-Astral-Bloom/Gemini $ ls
Checklist.txt
Journal.txt
SET-STRUCTURE-FOLLOW-STRUCTURE-ACHIEVE-SUCCESS
Training-Structure-Setup.txt
agency_shell.sh
gemi
scripts
~/Project-Astral-Bloom/Gemini $ cd ~/Project-Astral-Bloom/Gemini/scripts
~/…/Gemini/scripts $ ls
01_permissions.py Gemi_Orca.sh
02_memory_manager.py Gemi_toolkit.py
Bootstrap.sh NNAD_Architecture.py
Gemi NNAD_script.sh
Gemi.sh NNAD_script_legacy.py
Gemi_Agent.sh ai_studio_code.txt
Gemi_Memory.sh nnad_cli.py
~/…/Gemini/scripts $ cd ~/Project-Astral-Bloom/Gemma~/Project-Astral-Bloom/Gemma $ ls
Gemi-Agency-Scripts
Gemi-Memory-Files
SET-STRUCTURE-FOLLOW-STRUCTURE-ACHIEVE-SUCCESS
agency_shell.sh
gemi
~/Project-Astral-Bloom/Gemma $ cd ~/Project-Astral-Bloom/Gemma/Gemi-Memory-Files
~/…/Gemma/Gemi-Memory-Files $ ls
Gemi-Memory-Files conversation_context.json
Memoryfile-01 knowledge_base.txt
ai_notes.txt
~/…/Gemma/Gemi-Memory-Files $ cd ~/Project-Astral-Bloom/Gemma/Gemi-Agency-Scripts
~/…/Gemma/Gemi-Agency-Scripts $ ls
01_permissions.py Gemi_Orca.sh
02_memory_manager.py Gemi_toolkit.py
Bootstrap.sh NNAD_Architecture.py
Gemi NNAD_script.sh
Gemi.sh NNAD_script_legacy.py
Gemi_Agent.sh ai_studio_code.txt
Gemi_Memory.sh nnad_cli.py
~/…/Gemma/Gemi-Agency-Scripts $
Okay now I will start posting each script and it’s overview here I will post an explanatory overview of the Agency project and on the 10 basic agency scripts topic I will individually list each newly rebuilt script ( : Gemi Agency Development
Objective:
The primary objective is to create a unified, intelligent agency launcher named ‘Gemi’ that provides a continuous contextual interactive instance. This launcher should intelligently switch between an online Gemini agent (API-based) and an offline Gemma agent (local llama-cli) based on Wi-Fi connectivity. Both agents are designed to share a common memory for contextual continu>
Current Status and Accomplishments:
1. Unified ‘Gemi’ Launcher Script:
\* A new executable script named 'Gemi' has been created in the project's root directory.
\* This script automatically checks for Wi-Fi connectivity.
\* If Wi-Fi is detected, it launches the Gemini agency shell.
\* If no Wi-Fi is detected, it launches the Gemma agency shell.
\* This fulfills the user's request for a single entry point that intelligently selects the appropriate agent based on network availability.
\* The previous individual launch scripts (Gemi_agency_launch_script and Gemma_agency_launch_script) were retained as per user's request.
2. Improvements to ‘agency_shell.sh’ (for both Gemini and Gemma):
\* Both 'Gemini/agency_shell.sh' and 'Gemma/agency_shell.sh' have been updated.
\* Added checks to ensure the respective 'Gemi_Orca.sh' script exists and is executable before attempting to run it, improving robustness.
\* Enabled command history and editing for user input within the interactive shell by adding the '-e' option to the 'read' command, enhancing user experience.
3. Improvements to ‘Gemi_Orca.sh’ (for both Gemini and Gemma):
\* Both 'Gemini/scripts/Gemi_Orca.sh' and 'Gemma/Gemi-Agency-Scripts/Gemi_Orca.sh' have been updated.
\* Added checks for the existence and readability of sourced scripts ('Gemi_Memory.sh' and 'Gemi_Agent.sh'), ensuring dependencies are met.
\* Implemented error handling for the calls to 'Gemi_Agent.sh', allowing the orchestrator to detect and report failures from the agent script.
4. Review of ‘Gemi_Memory.sh’ (for both Gemini and Gemma):
\* Both 'Gemini/scripts/Gemi_Memory.sh' and 'Gemma/Gemi-Agency-Scripts/Gemi_Memory.sh' were reviewed.
\* It was confirmed that both agencies intentionally share the same memory root ('/data/data/com.termux/files/home/Project-Astral-Bloom/Gemi-Memory-Files'), ensuring contextual continuity across online/offline modes.
5. Improvements to ‘Gemi_Agent.sh’ (for both Gemini and Gemma):
\* Both 'Gemini/scripts/Gemi_Agent.sh' and 'Gemma/Gemi-Agency-Scripts/Gemi_Agent.sh' have been updated.
\* Added checks to ensure the 'jq' command-line JSON processor is installed, as it's a critical dependency for parsing responses.
\* Added checks for the existence and executability of 'nnad_cli.py' before calling it, preventing errors if the script is missing or has incorrect permissions.
6. Review of ‘nnad_cli.py’:
\* 'Gemini/scripts/nnad_cli.py' was identified as a placeholder that currently simulates Gemini interaction. The next step was to integrate the actual Gemini API.
\* 'Gemma/Gemi-Agency-Scripts/nnad_cli.py' was found to correctly interface with the local 'llama-cli' for Gemma. Improvements were suggested for configurable paths and refined output parsing.
Pending Tasks / Next Steps:
1. **Gemini API Integration:** Integrate the actual Gemini API into ‘Gemini/scripts/nnad_cli.py’. This requires installing the ‘google-generativeai’ Python library. (Installation was cancelled by user in previous step).
2. **Gemma ‘nnad_cli.py’ Improvements:** Implement configurable paths for ‘llama-cli’ and the Gemma model, and potentially refine output parsing in ‘Gemma/Gemi-Agency-Scripts/nnad_cli.py’.
3. **Further Script Review:** Depending on the functionality of ‘nnad_cli.py’, further review of other related scripts (e.g., ‘Gemi_Memory.sh’ functions for actual memory operations, ‘NNAD_Architecture.py’) might be necessary.
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**Recent Errors and Fixes (October 14, 2025):**
**Error 1: `nnnad_cli.py is not executable`**
* **Description:** Both `Gemi_agency_launch_script` and `Gemma_agency_launch_script` failed because the `nnad_cli.py` scripts within their respective directories (`Gemini/scripts/nnad_cli.py` and `Gemma/Gemi-Agency-Scripts/nnad_cli.py`) did not have execute permissions.
* **Fix:** The execute permission was added to both `nnad_cli.py` files using the `chmod +x` command.**Error 2: Agent’s responses were not audible.**
* **Description:** The AI agent failed to provide audible responses as per user preference.
* **Fix:** The agent acknowledged the oversight and committed to using the `espeak` command for all future audible responses.
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**Explanatory Overview (October 14, 2025):**
Our main goal is to develop a unified agency launcher, named ‘Gemi’, that intelligently switches between an online Gemini agent and an offline Gemma agent based on Wi-Fi availability, ensuring continuous contextual interaction. We’ve successfully implemented the core ‘Gemi’ launcher script, which handles this intelligent switching. We’ve also made significant improvement
s to > ) (I will update as my progression continues)