Instructions to use samuelchristlie/tmax-9b-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use samuelchristlie/tmax-9b-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="samuelchristlie/tmax-9b-gguf",
	filename="allenai_tmax-9b-IQ3_S.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use samuelchristlie/tmax-9b-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 samuelchristlie/tmax-9b-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf samuelchristlie/tmax-9b-gguf:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf samuelchristlie/tmax-9b-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf samuelchristlie/tmax-9b-gguf:Q4_K_M

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 samuelchristlie/tmax-9b-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf samuelchristlie/tmax-9b-gguf:Q4_K_M

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 samuelchristlie/tmax-9b-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf samuelchristlie/tmax-9b-gguf:Q4_K_M

Use Docker

docker model run hf.co/samuelchristlie/tmax-9b-gguf:Q4_K_M

LM Studio
Jan
Ollama
How to use samuelchristlie/tmax-9b-gguf with Ollama:
```
ollama run hf.co/samuelchristlie/tmax-9b-gguf:Q4_K_M
```

Unsloth Studio

How to use samuelchristlie/tmax-9b-gguf 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 samuelchristlie/tmax-9b-gguf 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 samuelchristlie/tmax-9b-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for samuelchristlie/tmax-9b-gguf to start chatting

How to use samuelchristlie/tmax-9b-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf samuelchristlie/tmax-9b-gguf:Q4_K_M

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": "samuelchristlie/tmax-9b-gguf:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use samuelchristlie/tmax-9b-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 samuelchristlie/tmax-9b-gguf:Q4_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 samuelchristlie/tmax-9b-gguf:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use samuelchristlie/tmax-9b-gguf with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf samuelchristlie/tmax-9b-gguf:Q4_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 "samuelchristlie/tmax-9b-gguf:Q4_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"

Docker Model Runner
How to use samuelchristlie/tmax-9b-gguf with Docker Model Runner:
```
docker model run hf.co/samuelchristlie/tmax-9b-gguf:Q4_K_M
```

Lemonade

How to use samuelchristlie/tmax-9b-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull samuelchristlie/tmax-9b-gguf:Q4_K_M

Run and chat with the model

lemonade run user.tmax-9b-gguf-Q4_K_M

List all available models

lemonade list

Tmax-9B-GGUF

Direct GGUF Quantizations of Tmax-9B

This repository provides GGUF quantized models for allenai/tmax-9b.

Tmax-9B is a 9 billion parameter terminal-agent model developed by AllenAI and collaborators. Built on top of the Qwen3.5-9B architecture and further trained using reinforcement learning for terminal-based tasks, it is designed to perform complex command-line and software engineering workflows while maintaining strong general-purpose reasoning capabilities. These GGUF versions are optimized for efficient CPU and GPU inference using llama.cpp and compatible tools.

This release includes various quantization levels (e.g., Q2_K, Q3_K_M, Q4_K_M, Q5_K_M, Q6_K, Q8_0) to suit different hardware capabilities and performance requirements.

Table of Contents 📝

▶ Usage
📃 License
🙏 Acknowledgements

▶ Usage

1. Download Models

Download models using huggingface-cli:

pip install "huggingface_hub[cli]"
huggingface-cli download samuelchristlie/tmax-9B-gguf --local-dir ./tmax-9B-gguf

You can also download directly from this page

2. Inference

To use these GGUF files, you'll need a compatible inference engine like llama.cpp or clients built on top of it (e.g., Ollama, LM Studio, KoboldCpp, text-generation-webui with llama.cpp backend).

📃 License

This model is a GGUF conversion of the original allenai/tmax-9b model. The original model is licensed under the Apache 2.0 License, and this derivative work adheres to the terms of that license. Please review the original license for full details.

🙏 Acknowledgements

Allen Institute for AI (AI2) and collaborators for developing and open-sourcing the Tmax-9B model:
- https://huggingface.co/allenai/tmax-9b
Qwen Team for the Qwen3.5-9B base model that powers Tmax-9B:
- https://huggingface.co/Qwen/Qwen3.5-9B
The llama.cpp project and its contributors for the GGUF format and the incredible tooling that makes local LLM inference accessible.
- https://github.com/ggml-org/llama.cpp
city96:
- https://huggingface.co/city96

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GGUF

Model size

9B params

Architecture

qwen35

Hardware compatibility

2-bit

3-bit

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5-bit

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