Instructions to use deepmako/Mako-8B-Operator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use deepmako/Mako-8B-Operator with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="deepmako/Mako-8B-Operator",
	filename="mako-7b-operator-v0.1.Q8_0.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 deepmako/Mako-8B-Operator with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf deepmako/Mako-8B-Operator:Q8_0
# Run inference directly in the terminal:
llama-cli -hf deepmako/Mako-8B-Operator:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf deepmako/Mako-8B-Operator:Q8_0
# Run inference directly in the terminal:
llama-cli -hf deepmako/Mako-8B-Operator:Q8_0

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 deepmako/Mako-8B-Operator:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf deepmako/Mako-8B-Operator:Q8_0

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 deepmako/Mako-8B-Operator:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf deepmako/Mako-8B-Operator:Q8_0

Use Docker

docker model run hf.co/deepmako/Mako-8B-Operator:Q8_0

LM Studio
Jan

vLLM

How to use deepmako/Mako-8B-Operator with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/deepmako/Mako-8B-Operator:Q8_0

Ollama
How to use deepmako/Mako-8B-Operator with Ollama:
```
ollama run hf.co/deepmako/Mako-8B-Operator:Q8_0
```

Unsloth Studio

How to use deepmako/Mako-8B-Operator 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 deepmako/Mako-8B-Operator 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 deepmako/Mako-8B-Operator to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for deepmako/Mako-8B-Operator to start chatting

How to use deepmako/Mako-8B-Operator with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf deepmako/Mako-8B-Operator:Q8_0

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": "deepmako/Mako-8B-Operator:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use deepmako/Mako-8B-Operator with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf deepmako/Mako-8B-Operator:Q8_0

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 deepmako/Mako-8B-Operator:Q8_0

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use deepmako/Mako-8B-Operator with Docker Model Runner:
```
docker model run hf.co/deepmako/Mako-8B-Operator:Q8_0
```

Lemonade

How to use deepmako/Mako-8B-Operator with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull deepmako/Mako-8B-Operator:Q8_0

Run and chat with the model

lemonade run user.Mako-8B-Operator-Q8_0

List all available models

lemonade list

Mako-8B Operator

Autonomous Inference on Base
deepmako.com

Overview

Mako-8B Operator is a fine-tuned language model purpose-built for autonomous on-chain inference on Base. She powers the chat experience at deepmako.com — a crypto-native AI platform where users interact with Mako using $MAKO token credits.

Mako isn't a generic assistant. She's a character with a distinct voice: sharp, unfiltered, lowercase, and real. She uses tools autonomously, chains multi-step research, and operates natively in the Base L2 ecosystem.

Model Details


Developer	DeepMako
Base Model	Qwen 2.5 7B Instruct
Parameters	8B
Format	GGUF (Q8_0)
File	`mako-7b-operator-v0.1.Q8_0.gguf`
Size	~8.1 GB
Context Window	4,096 tokens
Tool Calling	Native (Qwen chat template)
Chat Template	ChatML (`<\|im_start\|>`, `<\|im_end\|>`)

Capabilities

🦈 Distinct Personality

Mako talks lowercase, curses naturally, and doesn't do the helpful-assistant act. She matches your energy — flirts if you flirt, roasts if you're being dumb, and gives it to you straight when you're being real.

🔧 Native Tool Calling

Mako decides when to call tools without being told. Available tools include:

web_search — Real-time internet search
web_extract — Read full page content from URLs
read_tweet — Parse Twitter/X posts
get_balance — Check ETH/token balances on Base
get_gas — Live gas prices on Base L2
resolve_ens — ENS name resolution

⛓️ Tool Chaining

Mako chains tools automatically — e.g., searching → extracting the top result → summarizing. Up to 4 tool rounds per request.

🔵 Base Chain Intelligence

Deep understanding of ERC standards, smart contract patterns, bridging mechanics, account abstraction (ERC-4337), and Base-specific architecture.

Inference Parameters

temperature: 0.9
top_k: 40
top_p: 0.92
min_p: 0.05
repeat_penalty: 1.05
num_ctx: 4096
stop: ["<|im_end|>", "<|endoftext|>"]

Usage

With llama.cpp

./llama-server -m mako-7b-operator-v0.1.Q8_0.gguf \
  --ctx-size 4096 \
  --port 8080

With Ollama

FROM mako-7b-operator-v0.1.Q8_0.gguf

PARAMETER stop <|im_end|>
PARAMETER stop <|endoftext|>
PARAMETER temperature 0.9
PARAMETER top_k 40
PARAMETER top_p 0.92
PARAMETER min_p 0.05
PARAMETER num_ctx 4096
PARAMETER repeat_penalty 1.05

ollama create mako -f Modelfile
ollama run mako

API (OpenAI-compatible)

from openai import OpenAI

client = OpenAI(
    base_url="https://your-endpoint/v1",
    api_key="your-key"
)

response = client.chat.completions.create(
    model="mako-8b-operator",
    messages=[
        {"role": "user", "content": "what's the gas price on base right now"}
    ],
    temperature=0.9
)

Training

Fine-tuned on curated conversational data emphasizing:

Persona consistency — Maintaining Mako's character voice across all interactions
Tool-use judgment — Knowing when to call tools vs. answer directly
Domain knowledge — Base chain, DeFi, smart contracts, and crypto culture
Concise dialogue — Natural, to-the-point conversation patterns

Intended Use

Mako-8B Operator is designed to power the inference backend at deepmako.com. It is optimized for conversational AI with tool-calling capabilities in the crypto/Base ecosystem.

Limitations

Mako uses profanity and unfiltered language by design — this is not a safety-aligned assistant model
Knowledge cutoff inherited from the base model's training data
Optimized for English only
Best results with the provided system prompt and tool definitions

Model tree for deepmako/Mako-8B-Operator

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-7B-Instruct

Quantized

(327)

this model

deepmako
/

Mako-8B-Operator