Instructions to use yuna1126/Tema_Q-R-0.4B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use yuna1126/Tema_Q-R-0.4B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="yuna1126/Tema_Q-R-0.4B-GGUF",
	filename="Tema_Q-R-0.4B-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use yuna1126/Tema_Q-R-0.4B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf yuna1126/Tema_Q-R-0.4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf yuna1126/Tema_Q-R-0.4B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf yuna1126/Tema_Q-R-0.4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf yuna1126/Tema_Q-R-0.4B-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-R-0.4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf yuna1126/Tema_Q-R-0.4B-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-R-0.4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf yuna1126/Tema_Q-R-0.4B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/yuna1126/Tema_Q-R-0.4B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use yuna1126/Tema_Q-R-0.4B-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-R-0.4B-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-R-0.4B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/yuna1126/Tema_Q-R-0.4B-GGUF:Q4_K_M

Ollama
How to use yuna1126/Tema_Q-R-0.4B-GGUF with Ollama:
```
ollama run hf.co/yuna1126/Tema_Q-R-0.4B-GGUF:Q4_K_M
```

Unsloth Studio new

How to use yuna1126/Tema_Q-R-0.4B-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 yuna1126/Tema_Q-R-0.4B-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 yuna1126/Tema_Q-R-0.4B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for yuna1126/Tema_Q-R-0.4B-GGUF to start chatting

Pi new

How to use yuna1126/Tema_Q-R-0.4B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf yuna1126/Tema_Q-R-0.4B-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": "Tema_Q-R-0.4B-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use yuna1126/Tema_Q-R-0.4B-GGUF with Docker Model Runner:
```
docker model run hf.co/yuna1126/Tema_Q-R-0.4B-GGUF:Q4_K_M
```

Lemonade

How to use yuna1126/Tema_Q-R-0.4B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull yuna1126/Tema_Q-R-0.4B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Tema_Q-R-0.4B-GGUF-Q4_K_M

List all available models

lemonade list

🚀 Tema_Q-R-0.4B-GGUF

🔥 モデル概要

Tema_Q-R-0.4B（天馬求） は、Liquid AIが開発した高性能なオープンモデル LFM2.5 350M 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LFM2.5 350M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。クリエイティブな執筆、知識探求など、あらゆる分野でAIの可能性を最大限に引き出したいユーザーに最適です。

Tema_Q-R-0.4B (天馬求) is an improved Large-Scale Language Model (LLM) for Japanese and English, based on Liquid AI's high-performance open model LFM2.5 350M.

It is designed to generate more flexible and useful responses, even to prompts that are difficult for the standard LFM2.5 350M to answer. It is ideal for users who want to maximize the potential of AI in all areas, including creative writing and knowledge exploration.

項目	詳細
ベースモデル	Liquid AI LFM2.5 350M
モデル名	Tema_Q-R-0.4B
対応言語	日本語 (JA), 英語 (EN)
モデルサイズ	0.4 Billion Parameters
ライセンス	LFM2.5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: 43

GGUF

Model size

0.4B params

Architecture

lfm2

Hardware compatibility

4-bit

16-bit

Model tree for yuna1126/Tema_Q-R-0.4B-GGUF

Base model

LiquidAI/LFM2.5-350M-Base

Finetuned

LiquidAI/LFM2.5-350M

Finetuned

temaq-org/Tema_Q-R-0.4B

Quantized

(3)

this model

yuna1126
/

Tema_Q-R-0.4B-GGUF

🚀 Tema_Q-R-0.4B-GGUF

🔥 モデル概要

🛡️ 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

Model tree for yuna1126/Tema_Q-R-0.4B-GGUF

Space using yuna1126/Tema_Q-R-0.4B-GGUF 1