Instructions to use evalengine/this-that-model-1.1-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use evalengine/this-that-model-1.1-gguf 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 evalengine/this-that-model-1.1-gguf:Q8_0
# Run inference directly in the terminal:
llama cli -hf evalengine/this-that-model-1.1-gguf:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf evalengine/this-that-model-1.1-gguf:Q8_0
# Run inference directly in the terminal:
llama cli -hf evalengine/this-that-model-1.1-gguf: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 evalengine/this-that-model-1.1-gguf:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf evalengine/this-that-model-1.1-gguf: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 evalengine/this-that-model-1.1-gguf:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf evalengine/this-that-model-1.1-gguf:Q8_0

Use Docker

docker model run hf.co/evalengine/this-that-model-1.1-gguf:Q8_0

LM Studio
Jan
Ollama
How to use evalengine/this-that-model-1.1-gguf with Ollama:
```
ollama run hf.co/evalengine/this-that-model-1.1-gguf:Q8_0
```
Unsloth Desktop

How to use evalengine/this-that-model-1.1-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf evalengine/this-that-model-1.1-gguf: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": "evalengine/this-that-model-1.1-gguf:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use evalengine/this-that-model-1.1-gguf with Docker Model Runner:
```
docker model run hf.co/evalengine/this-that-model-1.1-gguf:Q8_0
```

Lemonade

How to use evalengine/this-that-model-1.1-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull evalengine/this-that-model-1.1-gguf:Q8_0

Run and chat with the model

lemonade run user.this-that-model-1.1-gguf-Q8_0

List all available models

lemonade list

Hermes Agent

How to use evalengine/this-that-model-1.1-gguf with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf evalengine/this-that-model-1.1-gguf: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 evalengine/this-that-model-1.1-gguf:Q8_0

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use evalengine/this-that-model-1.1-gguf with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf evalengine/this-that-model-1.1-gguf: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 "evalengine/this-that-model-1.1-gguf: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"

this-that-model-1.1 GGUF

GGUF conversion of flock-io/this-that-model-1.1, a 1.88B typed decision model. It is used by Unbound's on-device Snake demo, which runs in the browser via wllama and on iOS/Android via llama.rn.

File	Quant	Size
`this-that-model-1.1-Q8_0.gguf`	Q8_0	2.01 GB

Conversion

python convert_hf_to_gguf.py this-that-model-1.1 --no-mtp --outtype f16
llama-quantize this-that-model-1.1-f16.gguf this-that-model-1.1-Q8_0.gguf Q8_0

--no-mtp is required. The config declares one MTP layer whose weights are not shipped. Without the flag, llama.cpp fails to load the file with missing tensor 'blk.24.attn_norm.weight'.

Usage

This is not a chat model. Build the prompt in the thisthat state-first layout:

Context:
<state>

Question: <question>
Options:
(A) ...
(B) ...
Answer: (

Do not add a BOS token. Run one forward pass, take the next-token probabilities of the option letters A, B, …, and renormalise over those letters only.

Checks

The f16 file matches the PyTorch reference probabilities to three decimal places.
On 200 snake items from the model's spatial benchmark (limberc/this-that-spatial-bench), Q8_0 scores 99% on move safety and 94% on food direction. Q4_K_M drops to 87% on food direction, which is why only Q8_0 is published here.

Licence: MIT, same as the original model.

Downloads last month: 117

GGUF

Model size

2B params

Architecture

qwen35

Hardware compatibility

8-bit

Model tree for evalengine/this-that-model-1.1-gguf

Base model

flock-io/this-that-model-1.1

Quantized

(1)

this model