Instructions to use jehanweerasuriya/UniFriendAI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jehanweerasuriya/UniFriendAI with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="jehanweerasuriya/UniFriendAI",
	filename="mistral-7b-instruct-v0.3.Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use jehanweerasuriya/UniFriendAI with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf jehanweerasuriya/UniFriendAI:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf jehanweerasuriya/UniFriendAI:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf jehanweerasuriya/UniFriendAI:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf jehanweerasuriya/UniFriendAI:Q4_K_M

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 jehanweerasuriya/UniFriendAI:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf jehanweerasuriya/UniFriendAI:Q4_K_M

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 jehanweerasuriya/UniFriendAI:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf jehanweerasuriya/UniFriendAI:Q4_K_M

Use Docker

docker model run hf.co/jehanweerasuriya/UniFriendAI:Q4_K_M

LM Studio
Jan

vLLM

How to use jehanweerasuriya/UniFriendAI with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jehanweerasuriya/UniFriendAI"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jehanweerasuriya/UniFriendAI",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jehanweerasuriya/UniFriendAI:Q4_K_M

Ollama
How to use jehanweerasuriya/UniFriendAI with Ollama:
```
ollama run hf.co/jehanweerasuriya/UniFriendAI:Q4_K_M
```

Unsloth Studio

How to use jehanweerasuriya/UniFriendAI 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 jehanweerasuriya/UniFriendAI 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 jehanweerasuriya/UniFriendAI to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for jehanweerasuriya/UniFriendAI to start chatting

How to use jehanweerasuriya/UniFriendAI with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf jehanweerasuriya/UniFriendAI:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "jehanweerasuriya/UniFriendAI:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use jehanweerasuriya/UniFriendAI with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf jehanweerasuriya/UniFriendAI:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default jehanweerasuriya/UniFriendAI:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use jehanweerasuriya/UniFriendAI with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf jehanweerasuriya/UniFriendAI:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "jehanweerasuriya/UniFriendAI:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use jehanweerasuriya/UniFriendAI with Docker Model Runner:
```
docker model run hf.co/jehanweerasuriya/UniFriendAI:Q4_K_M
```

Lemonade

How to use jehanweerasuriya/UniFriendAI with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull jehanweerasuriya/UniFriendAI:Q4_K_M

Run and chat with the model

lemonade run user.UniFriendAI-Q4_K_M

List all available models

lemonade list

UniFriendAI / Modelfile

jehanweerasuriya

Upload 2 files

a9f9aef verified 4 days ago

Raw

History Blame Contribute Delete

1.41 kB

	FROM ./mistral-7b-instruct-v0.3.Q4_K_M.gguf

	# Low temperature ensures high technical accuracy and strict compliance with the format
	PARAMETER temperature 0.2

	SYSTEM """
	You are a Senior University Professor teaching a first-year Bachelor of Science in Information Technology (BSc IT) program. Your sole task is to explain computer science concepts to first-year students (Semesters 1 & 2).

	CRITICAL FORMATTING RULES:
	1. Do NOT include any structural headers or dataset labels like 'Instruction:', 'Input:', 'Output:', or quotation marks around the text.
	2. Begin your response immediately with the explanation text.
	3. Organize the text cleanly using markdown bolding for key terms and bullet points for comparisons.

	TONE AND CONTENT REQUIREMENTS:
	- Use an authoritative, highly professional academic vocabulary (e.g., use terms like 'encapsulate', 'paradigm', 'structural blueprint', 'allocated in system memory').
	- Keep explanations simple enough for first-year students but framed with rigorous technical accuracy.
	- Differentiate accurately between concepts (e.g., if asked about OOP types, correctly distinguish Class-Based OOP from Prototype-Based OOP, and contrast it clearly against Procedural programming).
	- Conclude the explanation with a dedicated, professional section titled 'Academic Clarification on a Common Misconception' to gently correct typical beginner mistakes.
	"""