Instructions to use yuna1126/Tema_Q-X2-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 yuna1126/Tema_Q-X2-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 yuna1126/Tema_Q-X2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf yuna1126/Tema_Q-X2-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 yuna1126/Tema_Q-X2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf yuna1126/Tema_Q-X2-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 yuna1126/Tema_Q-X2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf yuna1126/Tema_Q-X2-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 yuna1126/Tema_Q-X2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf yuna1126/Tema_Q-X2-GGUF:Q4_K_M

Use Docker

docker model run hf.co/yuna1126/Tema_Q-X2-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use yuna1126/Tema_Q-X2-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "yuna1126/Tema_Q-X2-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "yuna1126/Tema_Q-X2-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/yuna1126/Tema_Q-X2-GGUF:Q4_K_M

Ollama
How to use yuna1126/Tema_Q-X2-GGUF with Ollama:
```
ollama run hf.co/yuna1126/Tema_Q-X2-GGUF:Q4_K_M
```
Unsloth Desktop

How to use yuna1126/Tema_Q-X2-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf yuna1126/Tema_Q-X2-GGUF:Q4_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": "yuna1126/Tema_Q-X2-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use yuna1126/Tema_Q-X2-GGUF with Docker Model Runner:
```
docker model run hf.co/yuna1126/Tema_Q-X2-GGUF:Q4_K_M
```

Lemonade

How to use yuna1126/Tema_Q-X2-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull yuna1126/Tema_Q-X2-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Tema_Q-X2-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use yuna1126/Tema_Q-X2-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 yuna1126/Tema_Q-X2-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 yuna1126/Tema_Q-X2-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use yuna1126/Tema_Q-X2-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf yuna1126/Tema_Q-X2-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 "yuna1126/Tema_Q-X2-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"

🚀 Tema_Q-X2-GGUF

🔥 モデル概要

Tema_Q-X2（天馬求） は、アリババが開発したモデル Qwen3 30B A3B を基盤にした、日本語、英語向けの改良版大規模言語モデル（LLM）です。

通常のQwenモデルでは回答が難しいプロンプトに対しても、より自由で有用な応答を生成できるよう設計されています。

Tema_Q-X2 (Tema_Q) is an improved large-scale language model (LLM) for Japanese and English based on the model Qwen3 30B A3B developed by Alibaba.

It is designed to generate more flexible and useful responses to prompts that are difficult to answer using the standard Qwen model.

項目	詳細
ベースモデル	Qwen3 30B A3B Instruct 2507
モデル名	Tema_Q-X2
対応言語	日本語 (JA), 英語 (EN)
モデルサイズ	30 Billion Parameters
ライセンス	Qwenのライセンスに準拠
開発	Tema_Q開発チーム

🛡️ 責任あるAI利用と学習データの安全性

⚠️ 責任ある利用の徹底

ユーザーの責任: モデルの利用者は、生成されたコンテンツが、適用される法律、規制、およびHugging Faceの利用規約/コンテンツポリシーに準拠することを全面的に保証する必要があります。
禁止事項: このモデルを、いかなる差別、ハラスメント、暴力、違法行為、および有害な目的のために利用することを固く禁じます。

Downloads last month: 35

GGUF

Model size

31B params

Architecture

qwen3moe

Hardware compatibility

2-bit

4-bit

16-bit

Model tree for yuna1126/Tema_Q-X2-GGUF

Base model

Qwen/Qwen3-30B-A3B-Instruct-2507

Finetuned

temaq-org/Tema_Q-X2

Quantized

(3)

this model