Text Generation
GGUF
English
llama-cpp
qwen2
chat
tool-calling
crypto
base-chain
mako
conversational
Instructions to use deepmako/Mako-8B-Operator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use deepmako/Mako-8B-Operator 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 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 serve -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/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -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
- Llama new
- 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 Desktop
- Pi
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 serve -hf deepmako/Mako-8B-Operator:Q8_0
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/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
- 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
- Hermes Agent
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 serve -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
- OpenClaw
How to use deepmako/Mako-8B-Operator with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf deepmako/Mako-8B-Operator:Q8_0
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 "deepmako/Mako-8B-Operator:Q8_0" \ --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"
File size: 4,673 Bytes
49bafcb 0dcf0c8 49bafcb 0dcf0c8 | 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 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 | ---
license: apache-2.0
language:
- en
tags:
- llama-cpp
- gguf
- qwen2
- chat
- tool-calling
- crypto
- base-chain
- mako
base_model: Qwen/Qwen2.5-7B-Instruct
model_type: causal-lm
pipeline_tag: text-generation
inference:
parameters:
temperature: 0.9
top_p: 0.92
top_k: 40
repetition_penalty: 1.05
stop:
- "<|im_end|>"
- "<|endoftext|>"
---
# Mako-8B Operator
<div align="center">
<img src="https://deepmako.com/makoshark.png" alt="Mako" width="120" />
<br/>
<strong>Autonomous Inference on Base</strong>
<br/>
<a href="https://deepmako.com">deepmako.com</a>
</div>
---
## Overview
**Mako-8B Operator** is a fine-tuned language model purpose-built for autonomous on-chain inference on [Base](https://base.org). She powers the chat experience at [deepmako.com](https://deepmako.com) β 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](https://deepmako.com) |
| **Base Model** | [Qwen 2.5 7B Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-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
### π¦ Distinct Personality
Mako talks lowercase, curses naturally, and doesn't do the helpful-assistant act. She matches your energy β flirts if you flirt, roasts if you're being dumb, and gives it to you straight when you're being real.
### π§ Native Tool Calling
Mako decides when to call tools without being told. Available tools include:
- **`web_search`** β Real-time internet search
- **`web_extract`** β Read full page content from URLs
- **`read_tweet`** β Parse Twitter/X posts
- **`get_balance`** β Check ETH/token balances on Base
- **`get_gas`** β Live gas prices on Base L2
- **`resolve_ens`** β ENS name resolution
### βοΈ Tool Chaining
Mako chains tools automatically β e.g., searching β extracting the top result β summarizing. Up to 4 tool rounds per request.
### π΅ Base Chain Intelligence
Deep understanding of ERC standards, smart contract patterns, bridging mechanics, account abstraction (ERC-4337), and Base-specific architecture.
## Inference Parameters
```yaml
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
```bash
./llama-server -m mako-7b-operator-v0.1.Q8_0.gguf \
--ctx-size 4096 \
--port 8080
```
### With Ollama
```dockerfile
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
```
```bash
ollama create mako -f Modelfile
ollama run mako
```
### API (OpenAI-compatible)
```python
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** β Maintaining Mako's character voice across all interactions
- **Tool-use judgment** β Knowing when to call tools vs. answer directly
- **Domain knowledge** β Base chain, DeFi, smart contracts, and crypto culture
- **Concise dialogue** β Natural, to-the-point conversation patterns
## Intended Use
Mako-8B Operator is designed to power the inference backend at [deepmako.com](https://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 β 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
## Links
- **Platform**: [deepmako.com](https://deepmako.com)
- **Token**: $MAKO on Base
- **GitHub**: [DeepMako/mako](https://github.com/DeepMako/mako)
---
*The deep end awaits.*
|