Instructions to use Evrmind/EVR-1-Maano-8b-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Evrmind/EVR-1-Maano-8b-Instruct with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Evrmind/EVR-1-Maano-8b-Instruct",
	filename="evr-llama-3.1-8b-instruct.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 Evrmind/EVR-1-Maano-8b-Instruct with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Evrmind/EVR-1-Maano-8b-Instruct
# Run inference directly in the terminal:
llama-cli -hf Evrmind/EVR-1-Maano-8b-Instruct

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Evrmind/EVR-1-Maano-8b-Instruct
# Run inference directly in the terminal:
llama-cli -hf Evrmind/EVR-1-Maano-8b-Instruct

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 Evrmind/EVR-1-Maano-8b-Instruct
# Run inference directly in the terminal:
./llama-cli -hf Evrmind/EVR-1-Maano-8b-Instruct

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 Evrmind/EVR-1-Maano-8b-Instruct
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Evrmind/EVR-1-Maano-8b-Instruct

Use Docker

docker model run hf.co/Evrmind/EVR-1-Maano-8b-Instruct

LM Studio
Jan

vLLM

How to use Evrmind/EVR-1-Maano-8b-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Evrmind/EVR-1-Maano-8b-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Evrmind/EVR-1-Maano-8b-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Evrmind/EVR-1-Maano-8b-Instruct

Ollama
How to use Evrmind/EVR-1-Maano-8b-Instruct with Ollama:
```
ollama run hf.co/Evrmind/EVR-1-Maano-8b-Instruct
```

Unsloth Studio

How to use Evrmind/EVR-1-Maano-8b-Instruct 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 Evrmind/EVR-1-Maano-8b-Instruct 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 Evrmind/EVR-1-Maano-8b-Instruct to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Evrmind/EVR-1-Maano-8b-Instruct to start chatting

How to use Evrmind/EVR-1-Maano-8b-Instruct with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Evrmind/EVR-1-Maano-8b-Instruct

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": "Evrmind/EVR-1-Maano-8b-Instruct"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Evrmind/EVR-1-Maano-8b-Instruct with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Evrmind/EVR-1-Maano-8b-Instruct

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 Evrmind/EVR-1-Maano-8b-Instruct

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use Evrmind/EVR-1-Maano-8b-Instruct with Docker Model Runner:
```
docker model run hf.co/Evrmind/EVR-1-Maano-8b-Instruct
```

Lemonade

How to use Evrmind/EVR-1-Maano-8b-Instruct with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Evrmind/EVR-1-Maano-8b-Instruct

Run and chat with the model

lemonade run user.EVR-1-Maano-8b-Instruct-{{QUANT_TAG}}

List all available models

lemonade list

EVR-1-Maano-8b-Instruct / WEB_UI.md

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Web UI

Start

./start-server.sh

Opens the web interface at http://localhost:8080.

Network Mode

Access the web UI from your phone's browser or any device on the same WiFi (the model runs on your computer, your phone is just the display):

./start-server.sh --network

The script will print the URL to open on other devices.

Options

These flags work with start-server.sh (Linux, macOS, Android):

Flag	Description
`--network`	Bind to all interfaces (allows LAN access)
`--port=N`	Use a different port (default: 8080)
`--cpu`	CPU-only mode (no GPU offload)

Windows

Double-click start-server.bat or run from Command Prompt:

start-server.bat

Uses the CUDA build if available, otherwise falls back to Vulkan. The .bat script uses the default settings (localhost, port 8080, GPU enabled). To change these, use the Manual Start section below.

Manual Start

If you prefer to start the server directly:

# Linux (CUDA)
cd linux-cuda
LD_LIBRARY_PATH=. ./llama-server -m ../evr-llama-3.1-8b-instruct.gguf -ngl 99 --port 8080 --path ../webui

# Linux (Vulkan)
cd linux-vulkan
LD_LIBRARY_PATH=. ./llama-server -m ../evr-llama-3.1-8b-instruct.gguf -ngl 99 --port 8080 --path ../webui

# macOS (Apple Silicon)
cd metal
./llama-server -m ../evr-llama-3.1-8b-instruct.gguf -ngl 99 --port 8080 --path ../webui

# Windows (CUDA)
cd windows-cuda
llama-server.exe -m ..\evr-llama-3.1-8b-instruct.gguf -ngl 99 --port 8080 --path ..\webui

# Windows (Vulkan)
cd windows-vulkan
llama-server.exe -m ..\evr-llama-3.1-8b-instruct.gguf -ngl 99 --port 8080 --path ..\webui

API

The server exposes an OpenAI-compatible API:

# Chat completion
curl http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"messages":[{"role":"user","content":"Hello"}],"stream":false}'

# Text completion
curl http://localhost:8080/v1/completions \
  -H "Content-Type: application/json" \
  -d '{"prompt":"The main causes of","max_tokens":200,"stream":false}'

# Health check
curl http://localhost:8080/health

Troubleshooting

Server won't start: Make sure no other process is using port 8080. Try --port=8081.

Slow generation: Ensure GPU offload is working (-ngl 99). Check that CUDA/Vulkan drivers are installed.

Can't access from phone: Use --network flag. Make sure both devices are on the same WiFi network. Check firewall settings.