Text Generation
GGUF
English
llama.cpp
llama
llama-3.1
3-bit
quantization
evr
evrmind
instruct
chat
on-device
maano
conversational
Instructions to use Evrmind/EVR-1-Maano-8b-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
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
- Pi
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
File size: 2,492 Bytes
1669554 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 | # Web UI
## Start
```bash
./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):
```bash
./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:
```bash
# 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:
```bash
# 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.
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