Instructions to use MouseWurX/28Bob-Riker with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use MouseWurX/28Bob-Riker 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 MouseWurX/28Bob-Riker:Q3_K_M
# Run inference directly in the terminal:
llama cli -hf MouseWurX/28Bob-Riker:Q3_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf MouseWurX/28Bob-Riker:Q3_K_M
# Run inference directly in the terminal:
llama cli -hf MouseWurX/28Bob-Riker:Q3_K_M

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 MouseWurX/28Bob-Riker:Q3_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf MouseWurX/28Bob-Riker:Q3_K_M

Use Docker

docker model run hf.co/MouseWurX/28Bob-Riker:Q3_K_M

Llama new
LM Studio
Jan
Ollama
How to use MouseWurX/28Bob-Riker with Ollama:
```
ollama run hf.co/MouseWurX/28Bob-Riker:Q3_K_M
```
Unsloth Desktop

How to use MouseWurX/28Bob-Riker with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf MouseWurX/28Bob-Riker:Q3_K_M

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": "MouseWurX/28Bob-Riker:Q3_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use MouseWurX/28Bob-Riker with Docker Model Runner:
```
docker model run hf.co/MouseWurX/28Bob-Riker:Q3_K_M
```

Lemonade

How to use MouseWurX/28Bob-Riker with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull MouseWurX/28Bob-Riker:Q3_K_M

Run and chat with the model

lemonade run user.28Bob-Riker-Q3_K_M

List all available models

lemonade list

Hermes Agent

How to use MouseWurX/28Bob-Riker with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf MouseWurX/28Bob-Riker:Q3_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 MouseWurX/28Bob-Riker:Q3_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use MouseWurX/28Bob-Riker with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf MouseWurX/28Bob-Riker:Q3_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 "MouseWurX/28Bob-Riker:Q3_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"

28Bob Riker

A Bob cut from Qwen3.6-35B-A3B for Bob's own jobs: tools, coding, exact formats and everyday chat.

Made by Bob's Forge from Qwen/Qwen3.6-35B-A3B (all experts kept per layer, about 29 B parameters, 3 B active per word). Method, numbers and logs are in the Forge journal.

Files

File	Size	Needs
`28Bob-Q3_K_M.gguf`	13.9 GB	about 15 GB of card memory
`28Bob-Q4_K_M.gguf`	17.5 GB	about 19 GB of card memory

Each GGUF has a .bob.json card beside it that Bob's catalogue reads when you add the file as your own brain.

Home benchmarks (2026-11-08)

Test	28Bob	Qwen3.6 35B-A3B
Coding	69	92
Quick code	100	100
Tools	65	67
Facts	90	100
Routing	96
ARC-Challenge	92.9	95.8
ARC-Easy	97.6	98.9
ARC-Challenge (thinking)	93.9
ARC-Easy (thinking)	98.3

The project's own tests on one machine, for comparing Bobs with each other and with the model they came from, not for leaderboards.

Licence and credit

Apache 2.0, as the base model (Alibaba's Qwen team). The lessons it learned are Bob's own lesson sets (CC BY-SA 4.0) plus openly licensed traces from other models, credited in the Forge journal. The Bob names are a tribute to Dennis E. Taylor's novels; the project is not affiliated with the author.

Downloads last month: 47

GGUF

Model size

29B params

Architecture

qwen35moe

Hardware compatibility

3-bit

4-bit

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