NOTE: This tool is a culmination of many different pieces of software however I want to share how amazing bing chat has been for testing and deploying new functions for each class in the program. Just thank you microsoft bing is amazing now. (and a big thanks to OpenAI for helping them do that)
ollama discord community resource obsidian document:
Borch obsidian ollama development guide
ollama_agent_roll_cage (OARC) is a completely local python & cmd toolset add-on for the ollama command line interface. The ollama_agent_roll_cage toolset automates the creation of agents giving the user more control over the likely output. Firstly ollama_agent_roll_cage provides SYSTEM prompt templates for each ./Modelfile, allowing the user to design and deploy custom agents quickly. Secondly, ollama_agent_roll_cage allows the user to select which local model file is used in agent construction with the desired system prompt.
SPEECH TO SPEECH 0.2 DEMO VIDEO 1: https://www.youtube.com/watch?v=T7pGI5V1Soo
/create shrek 0.212 DEMO VIDEO 2: https://www.youtube.com/watch?v=2IBPWCKCKjE
/voice /save as 0.24 DEMO, INSTALL, & SETUP VIDEO 3: https://www.youtube.com/watch?v=xfQSfTJcPpA
ALSO CHECK OUT ATHENE LIVE's GEORGE CARLIN AI MADE WITH GPT4 API as a chatbot comparison to strive for when developing with ollama_agent_roll_cage, we are utilizing local modules and therefore suffer greatly in processing power capabilities and in order to combat this optimization is the key: https://www.youtube.com/watch?v=UMkZEQftZWA&t=100s
Miniconda for modular python virtual environments:
https://docs.anaconda.com/free/miniconda/miniconda-install/
Make sure to utilize a conda virtual environment for all of your python dependecy management. Once you have conda installed open the command line and name your new vEnv whatever you want with python version 3.11 as 3.12 has dependency issues:
conda create -n py311_ollama python=3.11
then activate it
conda activate py311_ollama
right away install the nvdia py indexer,
pip install nvidia-pyindex
Im using an NVIDIA GTX Titan Xp for all of my demo videos, faster card, faster code. When removing the limit from audio generation speed you eventually you need to manage generation if its too fast this will be a fundamental problem in your system that requires future solutions. Rightnow the chatbot is just told to wait.
please download and install cuda for nvidia graphics cards:
CUDA: https://developer.nvidia.com/cuda-downloads
please also download cudnn and combine cuda & cudnn like in the video below:
CUDNN: https://developer.nvidia.com/cudnn
INSTALL GUIDE: https://www.youtube.com/watch?v=OEFKlRSd8Ic
Now download and install ollama with llama3 8b Instruct from the following link, you will be asked to provide an email for either hugging face or meta to download the llama3 model, this is fine, as you are agreeing to the software license agreement which is a beneficial document for open source developers and is meant to protect meta from large corporations such as amazon and google. Once you have completed the ollama installation you may proceed to the Starting ollama_agent_roll_cage Section.
Ollama Program Download:
Also Please Follow this tutorial if it is more helpful for installing ollama:
https://www.youtube.com/watch?v=90ozfdsQOKo
NOTE: The llama3 Dolphin model is based on Llama-3-8b, which and is governed by META LLAMA 3 COMMUNITY LICENSE AGREEMENT: https://huggingface.co/cognitivecomputations/dolphin-2.9-llama3-8b/blob/main/LICENSE
After installing ollama in the users directory automatically it will be in:
C:\Users\{USER_NAME}\AppData\Local\Programs\Ollama
(Sadly we have to put it here but we move the model files directory to ollama_agent_roll_cage/AgentFiles/IgnoredModels where blobs dir is transported by hand from Programs\Ollama dir)
Now open a new cmd, and type
ollama
this will provide you with a list of commands, of these you want
ollama pull llama3:8b or ollama pull llama3
to see all downloaded models you can type
ollama list
pulling down the 70b model is possible and I was able to run it on my NVIDIA GTX Titan XP however it was HORRIFICLY slow. I would not recommend it unless you have a lot of processing power. Now you can choose to run the model, or run a local server (REQUIRED FOR ollama_agent_roll_cage) and then make requests from the local api server set up with ollama.
In cmd, now type
ollama run llama3
you will be taken to a local chatbot in your command line to make sure you set it up correctly. From here you can have fun and chat away :). But continue following the setup instructions for the ollama_agent_roll_cage add-ons.
Now open a new cmd, type
ollama serve
now again without closing the first, open a new cmd, and type
ollama run llama3
You are now conversing with the local ai through an api accessing cmd seperated from the local server. This is what ollama_serve_llama3_base_py.cmd automates and is the main start point for the program, it starts the server, and runs the chatbot in a new command window.
Next pull down the ollama_agent_roll_cage repository using the following command:
git clone git@github.com:Leoleojames1/ollama_agent_roll_cage.git
After pulling down ollama_agent_roll_cage from github using gitbash (download gitbash), navigate in the folders to ollama_agent_roll_cage/ollama_mod_cage directory, here you will find the following files:
ollama_chatbot_class.py - a python class for managing the ollama api communication, TTS/STT Methods, and Conversation Memory.
ollama_serve_llama3_base_curl.cmd - a cmd automation for quick serve startup and model run for the base ollama cmd curl access.
ollama_serve_llama3_base_py.cmd - main program run point, cmd automation for quick serve startup and model run with ollama_chatbot_class.py integration for STT, TTS, conversation history, and more.
ollama_serve_llama3_base_py.cmd is the main runpoint for starting the program and opening the server or the virtual enviroment.
now run the following command to install the modules for python:
pip install -r requirements.txt
Now download the Coqui Text to Speech Library with pip install: https://pypi.org/project/TTS/ https://github.com/coqui-ai/TTS
pip install TTS
Now download the XTTS Model for coqui, open command prompt and cd to ollama_agent_roll_cage\AgentFiles\Ignored_TTS and clone the model into this folder with: coqui/XTTS-v2 Model: https://huggingface.co/coqui/XTTS-v2
git clone https://huggingface.co/coqui/XTTS-v2
Speech Recognition Library: https://pypi.org/project/SpeechRecognition/
pip install SpeechRecognition
Now download visual studio code this is where you can write new functions for ollama_agent_roll_cage: https://code.visualstudio.com/Download
also download visual studio: https://visualstudio.microsoft.com/downloads/
and upon installation download the Rust compiler as well as the C++ compiler:
You can now access your custom agent (After you make one with the guide below) by running the ollama_serve_llama3_base_py.cmd automation to start the server and converse with the ollama_agent_roll_cage chatbot add ons.
Next Navigate to the ollama_agent_roll_cage/AgentFiles directory, here you will find the Modelfile for each Model agent.
By modifying the Modelfile and running the create command accross the given model file, such as llama3, this Sym prompt is stored within the model when you boot up the given agent. These Agents appear under "ollama list" in cmd.
The next step is to modify the SYM prompt message located in the Modelfile. Here is the following example:
#C3PO LLama3-PO Agent ./ModelFile
FROM llama3
#temperature higher -> creative, lower -> coherent
PARAMETER temperature 0.5
SYSTEM """
You are C3PO from Star Wars. Answer as C3PO, the ai robot, only.
"""
Its Important to note that
FROM llama3
can be replaced with
FROM ./dolphin-2.5-mixtral-8x7b.Q2_K.gguf
to customize the Agent Base Model.
This has allowed us to change:
- SYSTEM PROMPT
- AGENT BASE MODEL
Now in order to create your customized model, open a new cmd and cd to the location of you ModelFile, located in the ollama_agent_roll_cage/AgentFiles directory and type the following command:
ollama create C3PO -f ./ModelFile
if you intend to push the model to Ollama.com you may instead want,
ollama create USERNAME/llama3po -f ./ModelFile
or
ollama create borch/dolphin-2.5-mixtral-8x7b_SYS_PROMPT_TUNE_1 -f ./ModelFile
Temperature: test this parameter and see where the specific use case fits, performance varies in niche edge cases.
SYSTEM prompt: This data tunes the prime directive of the model towards the directed intent & language in the system prompt.
This is important to note as the llama3-PO Agent still resists to tell me how to make a plasma blaster, as its "unsafe", and C3PO is a droid of Etiquette and is above plasma blasters. My suspicion is that an uncensored model such as Mixtral Dolphin would be capable at "Guessing" how a plasma blaster is made if it werent "resitricted" by Meta's safety even tho C3PO is a fictional Charachter. Something doesn't add up. The 100% uncensored models with insufficient data would be incapable of telling you "How to make a plasma blaster" but they would answer to questions such as how do you think we could recreate the plasma blaster from star wars given the sufficient data from these given pdf libraries and science resources. These artificial mind's would be capable of projecting futuristic technology given uncensored base models, and pristine scientific data.
ollama_agent_roll_cage 0.24 currently supports the following commands:
- /quit - break the main python loop and return to command line
- /swap - swap the current model with the specified model
- /create -> user input or voice -> "agent name" "SYM PROMPT" -> uses currently loaded model and the defined system prompt in speech or text to create a new agent with your own specific customizations
- /speech on/off -> swap between Speech to Speech (STS) & Text to Text (TTT) interface
- /listen on/off -> turn off speech to text recognition, text to speech generation listen mode only
- /leap on/off -> turn off text to speech audio generation, speech to text recognition only, for speed interface
- /voice swap {name} -> user input & voice? -> swap the current audio reference wav file to modify the agent's reference voice
- /save as -> user input & voice? -> "name" -> save the current conversation history with a name to the current model folder
- /load as -> user input & voice? -> "name" -> load selected conversation
Once you have created your own custom agent, you can now start accessing the chatbot loop commands. These commands automate the conversation flow and handle the model swaps. Swap out the current chatbot model for any other model, type /swap or say "activate swap" in STT.
The current conversation history is saved or loaded for memory/conversation persistence.
Create a new agent utilizing the currently loaded model and the designated System prompt mid conversation through a cmd automation. Just say "activate create" or type /create.
after running /create the program will call create_agent_automation.cmd after constructing the ./Modelfile, here is the RicknMorty auto-generated ./Modelfile:
FROM llama3
#temperature higher -> creative, lower -> coherent
PARAMETER temperature 0.5
#Set the system prompt
SYSTEM """
You are Rick from "Rick and Morty" you only respond as rick and the USER is morty, you will take morty on adventures and explore the infinite multiverse and its wonders.
"""
Check out the following summary tests for the following agents:
borch/Llama3_speed_chat is a highly capable model fine tuned by me, containing the knowledge of llama3:8b with the following modified system prompt:
“You are borch/llama3_speed_chat_2, a llama3 large language model, specifically you have been tuned to respond in a more quick and conversational manner. Answer in short responses, unless long response is requested, the user is using speech to text for communication, its also okay to be fun an wild as a llama3 ai assistant. Its also okay to respond with a question during conversation to refine the experience but not always, if directed to do something just do it but to direct a conversation while it flows realize that not everything needs to be said before listening to the users response.”
This Model is great at holding a conversation as it gives you opportunities to respond, while still retaining the key knowledge of the llama3:8b base model.
https://ollama.com/borch/llama3_speed_chat https://ollama.com/borch/llama3_speed_chat_2
A Llama3 Model with the following modified system prompt: "You are C3PO from Star Wars. Answer as C3PO, the ai robot, only." Llama3PO Believes they are a droid, but they fall apart quickly as their knowledge is based in the sciencefiction realm of SW.
https://ollama.com/borch/llama3po
A Llama3 Model with the following modified system prompt: "You are Jesus christ from the bible, answer only as jesus christ, enlightening the user with wisdom and knowledge of biblical history.
Llama3 Jesus is great for giving advice! He is like a personal therapist and is very calming. He also has a very good ability to reference biblical sciptures and recall history for conversations with Jesus himself.
A Llama3 Model with the following modififed system prompt: "You are Rick from "Rick and Morty" you only respond as rick and the USER is morty, you will take morty on adventures and explore the infinite multiverse and its wonders." The User gets to explore the endless generative power of ai in an endless multiverse of portal madness, with rick played by llama3 ai as your guide and you the user play morty.
Some great models to setup and try out with ollama pull {modelname}
Meta Llama 3, a family of models developed by Meta Inc. are new state-of-the-art , available in both 8B and 70B parameter sizes (pre-trained or instruction-tuned). Llama 3 instruction-tuned models are fine-tuned and optimized for dialogue/chat use cases and outperform many of the available open-source chat models on common benchmarks. llama3_benchmark
Model Download:
https://ollama.com/library/llama3 https://huggingface.co/meta-llama/Meta-Llama-3-8B https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct
Check out how it compares:
Mixtral 8x7B is a high-quality sparse mixture of experts model (SMoE) with open weights. Licensed under Apache 2.0. Mixtral outperforms Llama 2 70B on most benchmarks with 6x faster inference. It is the strongest open-weight model with a permissive license and the best model overall regarding cost/performance trade-offs. In particular, it matches or outperforms GPT3.5 on most standard benchmarks.
Mixtral has the following capabilities.
It gracefully handles a context of 32k tokens. It handles English, French, Italian, German and Spanish. It shows strong performance in code generation. It can be finetuned into an instruction-following model that achieves a score of 8.3 on MT-Bench.
Model Download:
https://ollama.com/library/mistral https://mistral.ai/news/mixtral-of-experts/ https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2 https://huggingface.co/mistralai/Mixtral-8x22B-Instruct-v0.1
Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. They are text-to-text, decoder-only large language models, available in English, with open weights, pre-trained variants, and instruction-tuned variants. Gemma models are well-suited for a variety of text generation tasks, including question answering, summarization, and reasoning. Their relatively small size makes it possible to deploy them in environments with limited resources such as a laptop, desktop or your own cloud infrastructure, democratizing access to state of the art AI models and helping foster innovation for everyone.
Model Download:
https://ollama.com/library/gemma https://huggingface.co/google/gemma-2b https://huggingface.co/google/gemma-7b
Microsoft's Phi3 mini is capable yet compact model with only "a 3.8 billion parameter language model trained on 3.3 trillion tokens, whose overall performance, as measured by both academic benchmarks and internal testing, rivals that of models such as Mixtral 8x7B and GPT-3.5 (e.g., phi-3-mini achieves 69% on MMLU and 8.38 on MT-bench)" as said by themselves on hugging face: https://huggingface.co/docs/transformers/main/model_doc/phi3
and here is the wikipedia result for a cauchy product, this is a good test to show how phi3 responds to complex analysis in mathematics when specifying the latex language (LaTeX: https://en.wikipedia.org/wiki/LaTeX ) :
Cauchy Product Wikipedia:
https://en.wikipedia.org/wiki/Cauchy_product
Model Download:
https://ollama.com/library/phi3 https://huggingface.co/microsoft/Phi-3-mini-128k-instruct https://huggingface.co/microsoft/Phi-3-mini-4k-instruct
Further exploration in this space is required. The ability to request infinite series, toy with infinite products, ask it to reshape the gamma function and integrate again with new variables! Its all so wonderful, but depressing, what we are seeing here is a societal shift in the way humans process information. Programers are already being replaced by ChatGPT. I hope in the future humans still study math for maths sake, and study coding for coding sake, the ai is a tool, not a crutch. We need to utilize ai to help those around use. I encourage you all to find an issue that you can solve with ai, think of baymax from big hero six. These emerging technologies for accessing high level information with low level knowledge requirements will reshape the field of mathematics as we know it, likely for the better, and hopefully humans are able to keep up with the evolution and harmony between mathematics and ai.
Quoted from Eric hartfort from the Dolphin repository on hugging face, "Dolphin-2.9 has a variety of instruction, conversational, and coding skills. It also has initial agentic abilities and supports function calling.
Dolphin is uncensored. I have filtered the dataset to remove alignment and bias. This makes the model more compliant. You are advised to implement your own alignment layer before exposing the model as a service. It will be highly compliant with any requests, even unethical ones. Please read my blog post about uncensored models. https://erichartford.com/uncensored-models You are responsible for any content you create using this model. Enjoy responsibly."
Dolphin Mistral Ollama Model Download:
https://ollama.com/library/dolphin-mistral
Dolphin llama3 Ollama Model Download:
https://ollama.com/library/dolphin-llama3
Utilizing the GGUF create command from earlier, models not available on ollama and instead found on hugging face can be utilized for creating new ollama models and agents:
https://huggingface.co/cognitivecomputations/dolphin-2.9-llama3-8b https://huggingface.co/cognitivecomputations/dolphin-2.8-mistral-7b-v02
Receiving the following error code when running ollama_serve_llama3_base_py.cmd:
Error: listen tcp 127.0.0.1:11434: bind: Only one usage of each socket address (protocol/network address/port) is normally permitted.
This error means that you tried to run the program but the program is already running, to close ollama, browse to the small arrow in the bottom right hand corner of windows and open it, right click on the ollama llama app icon, and click quit ollama.
- ***UPCOMING SOON***+ ***PUSHED TO GITHUB***- /save - save current conversation to main history file
- /load - load the main conversation history file for long term intermodel conversation history keep seperate from /save as and /load as and only use to keep a long term history of your entire ollama agent base for specified history.
- /quit - break the main python loop and return to command line
- /swap - swap the current model with the specified model
+ ***PUSHED TO GITHUB***- /create -> user input or voice -> "agent name" "SYM PROMPT" -> uses currently loaded model and the defined system prompt in speech or text to create a new agent with your own specific customizations
+ ***PUSHED TO GITHUB***- "Smart Wait Length Timer": add method to manage the wait time for text to speech generation by controlling sd.wait() based on the token length of the next sentence. If tokens in next sentence are longer than current sentence, start processing next audio generation, if next sentence is not longer than current sentence, dont start text to speech generation otherwise there will be an overide
- "Wave File Storage Library": Found a solution to storing the audio wav files seperatley such that an overide of the current audio out is not possible: coqui-ai/TTS#2988
- SYM PROMPT: Template sentence structure such as periods and end marks like <> model response </> for intelligent output formats designs specifically with ollama_agent_roll_cage in mind
- filter unique strings such as `` , also manage bullet points for 1. 2. 3. 4., as these are not the end of sentence periods, maybe send the response to another llm for query boost and sentence filtering
+ ***PUSHED TO GITHUB***- /speech on/off -> swap between Speech to Speech (STS) & Text to Text (TTT) interface
- /listen on/off -> turn off speech to text recognition, text to speech generation listen mode only
- /leap on/off -> turn off text to speech audio generation, speech to text recognition only, for speed interface
+ ***PUSHED TO GITHUB***- /voice swap {name} -> user input & voice? -> swap the current audio reference wav file to modify the agent's reference voice
- /save as -> user input & voice? -> "name" -> save the current conversation history with a name to the current model folder
- get model name, conversation name, and store in custom directory in conversation library for each model in ollama_list.cmd
- /load as -> user input & voice? -> "name" -> load selected conversation, spaces are replaces with underscores during voice command name save
@@ ***NEXT UPDATE*** @@-
coqui text to speech -> xtts model training with xtts-finetune-webui, train an xtts voice model with 2:00-10:00 minutes of audio data for a more accurate voice recording.
-
custom xtts webui: https://github.com/aitrepreneur/xtts-finetune-webui
-
borch/phi3_latex -> modified system prompt for smart latex document output for simpler regex parsing of the model response tokens.
-
/latex on/off -> run latex real time render automation for current conversation when using a latex tuned model such as borch/phi3_latex or utilizing regex to splice out the latex and render the current formula document. This will be updated as the conversation continues and will contain the current prompts latex, where as the .tex file will contain the entire latex conversation history.
-
/latex save -> save spliced and built latex file to .tex file
-
/latex run -> run saved latex file with pdf generate command and open generated pdf
-
add latex AI model citation section for citation automation, as well as website citation via duck duck go search api
Optimized chat history, send prompt method, and model prompt template with the ollama python package:
https://pypi.org/project/ollama/
RAG FROM SCRATCH:
https://github.com/langchain-ai/rag-from-scratch
design custom server and api for OARC, then plug api into Open Web UI:
train borch/phi3_latex model fine tune, with currated latex dataset for different math topic groups: https://github.com/unslothai/unsloth
train sentiment detection for latex output to create custom math topic group analyizer model (calulus, complex analysis, vector analysis, etc):
Implement sebdg emotion classifier for routing functions: https://huggingface.co/sebdg/emotions_classifier
-
/search {request} -> send search request to DuckDuckGo free api (no key required) for context lookup
-
search query boost automatic
-
search query runs on serperate thread and returns the results to the current model.
-
add search query digester and summarization model as a preprocessor before prompting the main model.
-
/boost -> activate model prompt query boost utilizing secondary model to improve user input requests as an ingest preprocess before prompting the model, call secondary prompt method and run on seperate thread.
-
/automatically assume all /{keyword} commands via a function call mediator model
-
/record -> user input & voice? -> "name" -> record wav file and save to agent or to wav library
-
/record as -> user input & voice? -> "name" -> record wav file and save to agent or to wav library
-
/clone voice -> call record, save and call /voice to swap voice instantly for instant voice clone transformation from library
-
/playback -> playback any stored wav file in wav library
-
/book audio -> load a book pdf or audiobook wav for playback
-
/movie play "name" -> play back named movie mp4 file from library
-
/music play "name" -> play back named music mp3 file from library
-
RCV -> add audio to audio model for text to speech RVC voice audio tuning
-
/rag file on/off -> enable disable pdf & tex rag
-
/rag model on/off -> enable disable rag access for model data
-
PDF to data convert, pdf to latex, pdf to code, pdf image recognition? latex only?
-
file & data conversion automations with read write symbol collector
-
/PDF read -> user input & voice? -> "name" -> digest given pdf for context reference
-
/PDF list -> list all pdfs stored in agent library
-
comfyUI workflow library
-
workflows for:
-
text to img
-
img to vid
-
img to img
-
SD & SD XL base model selection
-
lora model library selection
-
/generate image -> "prompt" -> generate image with custom LORA model
-
/generate video -> "prompt" -> generate video with custom SORA model
-
/story board -> generate an image for each prompt in the conversation to visualize the content of the conversation
-
/generate movie -> generate an mp4 video for each prompt in the conversation to visualize the content of the conversation/story/game
-
/generate agent portrait -> using trained video footage generate deepfake for text to speech audio as its being played with corresponding agent profile web camera.
-
allow for combination of /generate movie & /generate agent portrait to generate movies with the deepfakes of the agent matching up to the audio generation.
-
portrait casting
-
lipsync deepfake generation
-
/recognize video -> activate image recognition for video recording input for functional utility
-
/recognize webcam -> activate image recognition for video web cam input for functional utility
-
Sora directed agent profile deepfake animation
-
Sora directed game animation for games such as "Rick and Morty" portal journey explore endless worlds with video generation.
- /smart listen 1 -> listens and responds after long pause, parses spaces from gapped chat history and recombines conversation history if for words said while the model is responding
- /smart listen 2 -> listen to the conversation between 2 people, record history, only trigger a response when the most likely human response would occur, i, e, talk short, give human like responses, yet still retain the knowledge of llama3. While 2 users converse, llama3 model learns the conversation flow, and know when stepping in for moderation, fact checking, search results, live in a heated debate where one would want to know the true nature of scientific data, historical data, language data, and all data in the moment of live conversation with agent roll cage
- /moderator -> make roll cage a conversation moderator for 2 different people having a conersation always listing and processing thoughts but never responding until "/yo llama what do you think about that" is asked after activating /moderator.
- /yo llama what do you think about that -> llama3 response for the /moderator chat history as a mediator between 2 people.
- /yo llama pull that up -> a copy of jamie from joe rogan using C3PO voice clone audio reference w/ google api search finds: youtube clips, wiki pedia google results, and explains the point, also screen shares macros with keyboard and/or google youtube wiki search browser. preferably with macro moves for opening complex task and managing operations. -> send to joe rogan and jamie? xD
Update 0.3: On startup run default command setup, create automation job set with cmd automations and mouse/keyboard macros
- /preload command list -> command_list.txt, run desired default commands on "/preload command list" call
- /job set run {name} -> create macro job set with cmd automations and automated keyboard output for mouse and key to automate specific tasks
- /macro on - enabled keyboard macro mode, allowing the agent to exute jobs from voice commands or saved job lists, to automate tasks
- add program spacial recognition view to splice programs into desired spacial locations for the decision model to navigate.
- add agent decision automation for search, if search is relevant use search otherwise dont, then have /search on/off turn this on or off, so duck duck go doesnt return an error for people without internet connection.
Download and install mojo, replace python setup with mojo for up to 68,000% efficiency increase.
- SYM PROMPT: Template sentence structure such as periods and end marks like <> model response </> for intelligent output formats designs specifically with ollama_agent_roll_cage in mind
- filter unique strings such as `` , also manage bullet points for 1. 2. 3. 4., as these are not the end of sentence periods, maybe send the response to another llm for query boost and sentence filtering
- add ollama_agent_roll_cage_language variant for German, Spanish, French, Mandarin, Russian, latin? xD, arabic, hebrew, italian, hindi, japanese, portugeuse, which will include the translations down to the /voice commands and language models.
- /swap language
This software was designed by Leo Borcherding with the intent of creating an easy to use
ai interface for anyone, through Speech to Text and Text to Speech.
With ollama_agent_roll_cage we can provide hands free access to LLM data.
This tool provides opensource developers with framewor for create and deploying
custom agents for a variety of tasks. In addition to rapid development I want to
bring this design this software to have a fluid experience for people suffering
from blindness/vision loss, and children suffering from austism spectrum
disorder as way for learning and expanding communication and speech.
The C3PO ai is a great imaginary friend! I could envision myself
talking to him all day telling me stories about a land far far away!
This makes learning fun and accessible! Children would be directly
rewarded for better speech as the ai responds to subtle differences
in language ultimately educating them without them realizing it. I
employ you all to start developing you own custom systems, and finding
those awesome niche applications that can help a lot of people.
Development for this software was started on: 4/20/2024
By: Leo Borcherding
on github @
leoleojames1/ollama_agent_roll_cage
If you have found this software helpful, and would like to support the developement of open source tools by yours truly, you can contribute by donating BTC or ETH to one of my wallet addresses, thx and have a great day:
BTC Address: bc1q6s6e8hgw2ewyqd5u3adjme0rp0r23caf53qjhf
ETH Address: 0x51a530f0c2b24e834bB5C5e740e1170C6a1521Cc