Instructions to use malgamves/peripheral-8b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use malgamves/peripheral-8b 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 malgamves/peripheral-8b:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf malgamves/peripheral-8b:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf malgamves/peripheral-8b:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf malgamves/peripheral-8b: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 malgamves/peripheral-8b:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf malgamves/peripheral-8b: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 malgamves/peripheral-8b:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf malgamves/peripheral-8b:Q4_K_M

Use Docker

docker model run hf.co/malgamves/peripheral-8b:Q4_K_M

LM Studio
Jan

vLLM

How to use malgamves/peripheral-8b with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/malgamves/peripheral-8b:Q4_K_M

Ollama
How to use malgamves/peripheral-8b with Ollama:
```
ollama run hf.co/malgamves/peripheral-8b:Q4_K_M
```

Unsloth Studio

How to use malgamves/peripheral-8b 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 malgamves/peripheral-8b 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 malgamves/peripheral-8b to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for malgamves/peripheral-8b to start chatting

How to use malgamves/peripheral-8b with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf malgamves/peripheral-8b: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": "malgamves/peripheral-8b:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use malgamves/peripheral-8b with Docker Model Runner:
```
docker model run hf.co/malgamves/peripheral-8b:Q4_K_M
```

Lemonade

How to use malgamves/peripheral-8b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull malgamves/peripheral-8b:Q4_K_M

Run and chat with the model

lemonade run user.peripheral-8b-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use malgamves/peripheral-8b with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf malgamves/peripheral-8b: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 malgamves/peripheral-8b:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use malgamves/peripheral-8b with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf malgamves/peripheral-8b: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 "malgamves/peripheral-8b: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"

malgamves commited on Jun 27

Commit

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@@ -21,7 +21,7 @@ Peripheral is a fine-tuned 8B **knowledge-management agent (KMA)** that manages
 This is the model behind the write-up *[Not all Context is Knowledge](https://madalitso.me/notes/not-all-context-is-knowledge)*.
-> Peripheral is a specialist rather than a better frontier model. It was trained on about 7,000 task examples and beats a zero-shot generalist on its own turf, but it transfers only partially to unseen domains and grows less reliable the further it gets from what it saw in training. Read the Limitations before you deploy it.
 ## What it does

 This is the model behind the write-up *[Not all Context is Knowledge](https://madalitso.me/notes/not-all-context-is-knowledge)*.
+> Peripheral is a specialist that was trained on about 7,000 task examples and beats a zero-shot generalist, but it transfers only partially to unseen domains and grows less reliable the further it gets from what it saw in training. Read the Limitations before you deploy it.
 ## What it does