Instructions to use TroyDoesAI/Agent-Flow-Phone_Demo_3GB_RAM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use TroyDoesAI/Agent-Flow-Phone_Demo_3GB_RAM with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="TroyDoesAI/Agent-Flow-Phone_Demo_3GB_RAM", filename="AgentFlow-3B-Q2_K.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use TroyDoesAI/Agent-Flow-Phone_Demo_3GB_RAM with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf TroyDoesAI/Agent-Flow-Phone_Demo_3GB_RAM:Q2_K # Run inference directly in the terminal: llama-cli -hf TroyDoesAI/Agent-Flow-Phone_Demo_3GB_RAM:Q2_K
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf TroyDoesAI/Agent-Flow-Phone_Demo_3GB_RAM:Q2_K # Run inference directly in the terminal: llama-cli -hf TroyDoesAI/Agent-Flow-Phone_Demo_3GB_RAM:Q2_K
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf TroyDoesAI/Agent-Flow-Phone_Demo_3GB_RAM:Q2_K # Run inference directly in the terminal: ./llama-cli -hf TroyDoesAI/Agent-Flow-Phone_Demo_3GB_RAM:Q2_K
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf TroyDoesAI/Agent-Flow-Phone_Demo_3GB_RAM:Q2_K # Run inference directly in the terminal: ./build/bin/llama-cli -hf TroyDoesAI/Agent-Flow-Phone_Demo_3GB_RAM:Q2_K
Use Docker
docker model run hf.co/TroyDoesAI/Agent-Flow-Phone_Demo_3GB_RAM:Q2_K
- LM Studio
- Jan
- Ollama
How to use TroyDoesAI/Agent-Flow-Phone_Demo_3GB_RAM with Ollama:
ollama run hf.co/TroyDoesAI/Agent-Flow-Phone_Demo_3GB_RAM:Q2_K
- Unsloth Studio new
How to use TroyDoesAI/Agent-Flow-Phone_Demo_3GB_RAM with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for TroyDoesAI/Agent-Flow-Phone_Demo_3GB_RAM to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for TroyDoesAI/Agent-Flow-Phone_Demo_3GB_RAM to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for TroyDoesAI/Agent-Flow-Phone_Demo_3GB_RAM to start chatting
- Docker Model Runner
How to use TroyDoesAI/Agent-Flow-Phone_Demo_3GB_RAM with Docker Model Runner:
docker model run hf.co/TroyDoesAI/Agent-Flow-Phone_Demo_3GB_RAM:Q2_K
- Lemonade
How to use TroyDoesAI/Agent-Flow-Phone_Demo_3GB_RAM with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull TroyDoesAI/Agent-Flow-Phone_Demo_3GB_RAM:Q2_K
Run and chat with the model
lemonade run user.Agent-Flow-Phone_Demo_3GB_RAM-Q2_K
List all available models
lemonade list
Flowchart Generation Model
Overview
This model generates Mermaid diagrams from a sequence of steps provided in the input. It processes the input text and automatically outputs a flowchart that visually represents the steps. The model strictly follows a defined template and does not require any additional prompt engineering.
How It Works
The model takes a set of sequential instructions as input and produces a flowchart diagram in Mermaid format.
Video Performance On A Mac M1 8GB:
https://x.com/troydoesai/status/1833671273765020158
Input Format
The input is a sequence of steps written as plain text:
Step 1: Do something.
Step 2: Do the next thing.
Step 3: Complete the task.
Output Format
The output is a Mermaid diagram:
graph TD;
Step1 --> Step2;
Step2 --> Step3;
...
Example
Input:
Fill the coffee maker with water.
Add coffee grounds to the filter.
Turn on the coffee maker.
Wait for brewing to finish.
Pour the coffee.
Optional: Add sugar or milk.
Enjoy your coffee.
Output:
graph TD;
A[Fill Coffee Maker] --> B[Add Water];
B --> C[Add Coffee Grounds];
C --> D[Turn On Coffee Maker];
D --> E[Wait for Brewing];
E --> F[Pour Coffee];
F --> G{Add Sugar & Milk?};
G--Yes-->H[Mix In];
H-->I[Enjoy Coffee];
G--No-->I;
Features
- Transforms plain text instructions into Mermaid flowcharts. (It might work with code, stories, or anything that has a flow)
- Follows a consistent input-output template.
- Simple and direct without requiring additional prompt adjustments.
Use Cases
- Visualizing step-by-step processes.
- Converting written instructions into clear, easy-to-read diagrams.
- Ideal for documentation, workflows, and tutorials.
How To Use With Ollama
- Please Use Provided
Ollama:Modelfile
ollama create AgentFlow
ollama run AgentFlow
- Downloads last month
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docker model run hf.co/TroyDoesAI/Agent-Flow-Phone_Demo_3GB_RAM:Q2_K