Instructions to use tensor-tailor/ralph-qwen3-8b-ternary-r4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use tensor-tailor/ralph-qwen3-8b-ternary-r4 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 tensor-tailor/ralph-qwen3-8b-ternary-r4
# Run inference directly in the terminal:
llama cli -hf tensor-tailor/ralph-qwen3-8b-ternary-r4

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf tensor-tailor/ralph-qwen3-8b-ternary-r4
# Run inference directly in the terminal:
llama cli -hf tensor-tailor/ralph-qwen3-8b-ternary-r4

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 tensor-tailor/ralph-qwen3-8b-ternary-r4
# Run inference directly in the terminal:
./llama-cli -hf tensor-tailor/ralph-qwen3-8b-ternary-r4

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 tensor-tailor/ralph-qwen3-8b-ternary-r4
# Run inference directly in the terminal:
./build/bin/llama-cli -hf tensor-tailor/ralph-qwen3-8b-ternary-r4

Use Docker

docker model run hf.co/tensor-tailor/ralph-qwen3-8b-ternary-r4

LM Studio
Jan
Ollama
How to use tensor-tailor/ralph-qwen3-8b-ternary-r4 with Ollama:
```
ollama run hf.co/tensor-tailor/ralph-qwen3-8b-ternary-r4
```
Unsloth Desktop

How to use tensor-tailor/ralph-qwen3-8b-ternary-r4 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf tensor-tailor/ralph-qwen3-8b-ternary-r4

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": "tensor-tailor/ralph-qwen3-8b-ternary-r4"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use tensor-tailor/ralph-qwen3-8b-ternary-r4 with Docker Model Runner:
```
docker model run hf.co/tensor-tailor/ralph-qwen3-8b-ternary-r4
```

Lemonade

How to use tensor-tailor/ralph-qwen3-8b-ternary-r4 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull tensor-tailor/ralph-qwen3-8b-ternary-r4

Run and chat with the model

lemonade run user.ralph-qwen3-8b-ternary-r4-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use tensor-tailor/ralph-qwen3-8b-ternary-r4 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf tensor-tailor/ralph-qwen3-8b-ternary-r4

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 tensor-tailor/ralph-qwen3-8b-ternary-r4

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use tensor-tailor/ralph-qwen3-8b-ternary-r4 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf tensor-tailor/ralph-qwen3-8b-ternary-r4

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 "tensor-tailor/ralph-qwen3-8b-ternary-r4" \
  --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"

ralph-qwen3-8b-ternary-r4

Ternary-tier GGUF compression of Qwen/Qwen3-8B — architecture and parameter count unchanged (8,190,735,360 weight-bearing parameters), weights re-stored at reduced bit-width. Submitted to the ternary bit-tier on Bittensor subnet 40 (Ralph, model-compression).

License

Released under Apache License 2.0 — see LICENSE. This is a derivative of Qwen3-8B, itself Apache-2.0; the original copyright/attribution notices and a description of what changed (including the third-party quantization tooling used) are preserved in NOTICE.

Model overview


Parent	Qwen/Qwen3-8B (Apache-2.0)
Parameters	8,190,735,360
Architecture	unchanged from parent
Format	GGUF
Bit tier	ternary
File size	2,430,026,816 bytes (~2.26 GiB)
Measured bits/weight	~2.37 (file size / param count; embedding/output tensors kept at higher precision outside the compressed blocks)
Quantization method	llama.cpp, imatrix-calibrated