Instructions to use electron271/graig-alpha with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use electron271/graig-alpha with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="electron271/graig-alpha",
	filename="model-F16.gguf",
)

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

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use electron271/graig-alpha 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 electron271/graig-alpha:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf electron271/graig-alpha:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf electron271/graig-alpha:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf electron271/graig-alpha: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 electron271/graig-alpha:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf electron271/graig-alpha: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 electron271/graig-alpha:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf electron271/graig-alpha:Q4_K_M

Use Docker

docker model run hf.co/electron271/graig-alpha:Q4_K_M

LM Studio
Jan

vLLM

How to use electron271/graig-alpha with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "electron271/graig-alpha"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "electron271/graig-alpha",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/electron271/graig-alpha:Q4_K_M

Ollama
How to use electron271/graig-alpha with Ollama:
```
ollama run hf.co/electron271/graig-alpha:Q4_K_M
```

Unsloth Studio

How to use electron271/graig-alpha 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 electron271/graig-alpha 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 electron271/graig-alpha to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for electron271/graig-alpha to start chatting

How to use electron271/graig-alpha with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf electron271/graig-alpha: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": "electron271/graig-alpha:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use electron271/graig-alpha with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf electron271/graig-alpha: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 electron271/graig-alpha:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use electron271/graig-alpha with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf electron271/graig-alpha: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 "electron271/graig-alpha: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 electron271/graig-alpha with Docker Model Runner:
```
docker model run hf.co/electron271/graig-alpha:Q4_K_M
```

Lemonade

How to use electron271/graig-alpha with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull electron271/graig-alpha:Q4_K_M

Run and chat with the model

lemonade run user.graig-alpha-Q4_K_M

List all available models

lemonade list

other companies may be trying to reach artificial general intelligence, but we are trying to reach artificial grain intelligence. with the help of our team of the best grain farmers in the world, we are making huge strides in the field. fine tuned fully locally using a RX 9070 XT using unsloth.

ollama run hf.co/electron271/graig-alpha:Q4_K_M

History

this is a continuation of the "tuxsentience" series made by @GrainWare, however using new advancements in AMD support in unsloth we are now able to pack in significantly more grain per parameter.

Recommended Settings

temperature = 0.6
top_k = 20
min_p = 0.00 (llama.cpp's default is 0.1)
top_p = 0.95
presence_penalty = 0.0 to 2.0 (llama.cpp default turns it off, but to reduce repetitions, you can use this) Try 1.0 for example.
Supports up to 262,144 context natively but you can set it to 32,768 tokens for less RAM use

Disclaimer

Graig can be prone to saying offensive statements in rare circumstances due to the unpredictability of LLMs. These do not reflect our opinions/views and are a byproduct we are trying to avoid. Newer graig models (such as this one) are significantly less prone to this, however if you do not setup settings correctly or do not prompt right this may still occur. If you find Graig to be saying offensive statements during normal circumstances please either open a community post on this model or email me at electron271@allthingslinux.org.

In public deployments such as on Discord, please setup a filter using something such as https://github.com/cherryl1k/llmcordplus.

(Normal circumstances is defined as using the recommended settings and talking to graig in a non aggressive manner.)