Instructions to use TaQuants/Tema_Q-R-4B-TaQuants-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 TaQuants/Tema_Q-R-4B-TaQuants-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 TaQuants/Tema_Q-R-4B-TaQuants-GGUF:IQ2_M
# Run inference directly in the terminal:
llama cli -hf TaQuants/Tema_Q-R-4B-TaQuants-GGUF:IQ2_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf TaQuants/Tema_Q-R-4B-TaQuants-GGUF:IQ2_M
# Run inference directly in the terminal:
llama cli -hf TaQuants/Tema_Q-R-4B-TaQuants-GGUF:IQ2_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 TaQuants/Tema_Q-R-4B-TaQuants-GGUF:IQ2_M
# Run inference directly in the terminal:
./llama-cli -hf TaQuants/Tema_Q-R-4B-TaQuants-GGUF:IQ2_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 TaQuants/Tema_Q-R-4B-TaQuants-GGUF:IQ2_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf TaQuants/Tema_Q-R-4B-TaQuants-GGUF:IQ2_M

Use Docker

docker model run hf.co/TaQuants/Tema_Q-R-4B-TaQuants-GGUF:IQ2_M

LM Studio
Jan

vLLM

How to use TaQuants/Tema_Q-R-4B-TaQuants-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TaQuants/Tema_Q-R-4B-TaQuants-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": "TaQuants/Tema_Q-R-4B-TaQuants-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/TaQuants/Tema_Q-R-4B-TaQuants-GGUF:IQ2_M

Ollama
How to use TaQuants/Tema_Q-R-4B-TaQuants-GGUF with Ollama:
```
ollama run hf.co/TaQuants/Tema_Q-R-4B-TaQuants-GGUF:IQ2_M
```
Unsloth Desktop
Docker Model Runner
How to use TaQuants/Tema_Q-R-4B-TaQuants-GGUF with Docker Model Runner:
```
docker model run hf.co/TaQuants/Tema_Q-R-4B-TaQuants-GGUF:IQ2_M
```

Lemonade

How to use TaQuants/Tema_Q-R-4B-TaQuants-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull TaQuants/Tema_Q-R-4B-TaQuants-GGUF:IQ2_M

Run and chat with the model

lemonade run user.Tema_Q-R-4B-TaQuants-GGUF-IQ2_M

List all available models

lemonade list

Atomic Chat

ek15072809 commited on Jul 3

Commit

9f2b15c

verified ·

1 Parent(s): dfc2de3

Update README.md

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@@ -10,7 +10,7 @@ tags:
 ---
 # Tema_Q-R-4B TaQuants
-[The Repository](https://github.com/ek15072809/TaQuants)
 [Technical Report](https://github.com/ek15072809/TaQuants/blob/main/docs/TaQuants_Technical_Report.pdf)
 The Tema_Q development team, team zenei, has developed a new importance matrix method called **TaQuants (Tensor-aware Adaptive Quantization)**.

 ---
 # Tema_Q-R-4B TaQuants
+[The Repository](https://github.com/ek15072809/TaQuants)
 [Technical Report](https://github.com/ek15072809/TaQuants/blob/main/docs/TaQuants_Technical_Report.pdf)
 The Tema_Q development team, team zenei, has developed a new importance matrix method called **TaQuants (Tensor-aware Adaptive Quantization)**.