Instructions to use matrixportalx/txgemma-9b-chat-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use matrixportalx/txgemma-9b-chat-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="matrixportalx/txgemma-9b-chat-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("matrixportalx/txgemma-9b-chat-GGUF", dtype="auto")

llama-cpp-python

How to use matrixportalx/txgemma-9b-chat-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="matrixportalx/txgemma-9b-chat-GGUF",
	filename="txgemma-9b-chat-f16.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use matrixportalx/txgemma-9b-chat-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 matrixportalx/txgemma-9b-chat-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf matrixportalx/txgemma-9b-chat-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 matrixportalx/txgemma-9b-chat-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf matrixportalx/txgemma-9b-chat-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 matrixportalx/txgemma-9b-chat-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf matrixportalx/txgemma-9b-chat-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 matrixportalx/txgemma-9b-chat-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf matrixportalx/txgemma-9b-chat-GGUF:Q4_K_M

Use Docker

docker model run hf.co/matrixportalx/txgemma-9b-chat-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use matrixportalx/txgemma-9b-chat-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "matrixportalx/txgemma-9b-chat-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": "matrixportalx/txgemma-9b-chat-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/matrixportalx/txgemma-9b-chat-GGUF:Q4_K_M

SGLang

How to use matrixportalx/txgemma-9b-chat-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "matrixportalx/txgemma-9b-chat-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "matrixportalx/txgemma-9b-chat-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "matrixportalx/txgemma-9b-chat-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "matrixportalx/txgemma-9b-chat-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use matrixportalx/txgemma-9b-chat-GGUF with Ollama:
```
ollama run hf.co/matrixportalx/txgemma-9b-chat-GGUF:Q4_K_M
```

Unsloth Studio

How to use matrixportalx/txgemma-9b-chat-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 matrixportalx/txgemma-9b-chat-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 matrixportalx/txgemma-9b-chat-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for matrixportalx/txgemma-9b-chat-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use matrixportalx/txgemma-9b-chat-GGUF with Docker Model Runner:
```
docker model run hf.co/matrixportalx/txgemma-9b-chat-GGUF:Q4_K_M
```

Lemonade

How to use matrixportalx/txgemma-9b-chat-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull matrixportalx/txgemma-9b-chat-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.txgemma-9b-chat-GGUF-Q4_K_M

List all available models

lemonade list

txgemma-9b-chat-GGUF / README.md

Matrix

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	---
	base_model: google/txgemma-9b-chat
	language:
	- en
	library_name: transformers
	license: other
	license_name: health-ai-developer-foundations
	license_link: https://developers.google.com/health-ai-developer-foundations/terms
	pipeline_tag: text-generation
	tags:
	- therapeutics
	- drug-development
	- llama-cpp
	- matrixportal
	extra_gated_heading: Access TxGemma on Hugging Face
	extra_gated_prompt: To access TxGemma on Hugging Face, you're required to review and
	agree to [Health AI Developer Foundation's terms of use](https://developers.google.com/health-ai-developer-foundations/terms).
	To do this, please ensure you're logged in to Hugging Face and click below. Requests
	are processed immediately.
	extra_gated_button_content: Acknowledge license
	---

	# matrixportal/txgemma-9b-chat-GGUF
	This model was converted to GGUF format from [`google/txgemma-9b-chat`](https://huggingface.co/google/txgemma-9b-chat) using llama.cpp via the ggml.ai's [all-gguf-same-where](https://huggingface.co/spaces/matrixportal/all-gguf-same-where) space.
	Refer to the [original model card](https://huggingface.co/google/txgemma-9b-chat) for more details on the model.

	## ✅ Quantized Models Download List

	### 🔍 Recommended Quantizations
	- ✨ General CPU Use: [`Q4_K_M`](https://huggingface.co/matrixportal/txgemma-9b-chat-GGUF/resolve/main/txgemma-9b-chat-q4_k_m.gguf) (Best balance of speed/quality)
	- 📱 ARM Devices: [`Q4_0`](https://huggingface.co/matrixportal/txgemma-9b-chat-GGUF/resolve/main/txgemma-9b-chat-q4_0.gguf) (Optimized for ARM CPUs)
	- 🏆 Maximum Quality: [`Q8_0`](https://huggingface.co/matrixportal/txgemma-9b-chat-GGUF/resolve/main/txgemma-9b-chat-q8_0.gguf) (Near-original quality)

	### 📦 Full Quantization Options
	\| 🚀 Download \| 🔢 Type \| 📝 Notes \|
	\|:---------\|:-----\|:------\|
	\| [Download](https://huggingface.co/matrixportal/txgemma-9b-chat-GGUF/resolve/main/txgemma-9b-chat-q2_k.gguf) \| ![Q2_K](https://img.shields.io/badge/Q2_K-1A73E8) \| Basic quantization \|
	\| [Download](https://huggingface.co/matrixportal/txgemma-9b-chat-GGUF/resolve/main/txgemma-9b-chat-q3_k_s.gguf) \| ![Q3_K_S](https://img.shields.io/badge/Q3_K_S-34A853) \| Small size \|
	\| [Download](https://huggingface.co/matrixportal/txgemma-9b-chat-GGUF/resolve/main/txgemma-9b-chat-q3_k_m.gguf) \| ![Q3_K_M](https://img.shields.io/badge/Q3_K_M-FBBC05) \| Balanced quality \|
	\| [Download](https://huggingface.co/matrixportal/txgemma-9b-chat-GGUF/resolve/main/txgemma-9b-chat-q3_k_l.gguf) \| ![Q3_K_L](https://img.shields.io/badge/Q3_K_L-4285F4) \| Better quality \|
	\| [Download](https://huggingface.co/matrixportal/txgemma-9b-chat-GGUF/resolve/main/txgemma-9b-chat-q4_0.gguf) \| ![Q4_0](https://img.shields.io/badge/Q4_0-EA4335) \| Fast on ARM \|
	\| [Download](https://huggingface.co/matrixportal/txgemma-9b-chat-GGUF/resolve/main/txgemma-9b-chat-q4_k_s.gguf) \| ![Q4_K_S](https://img.shields.io/badge/Q4_K_S-673AB7) \| Fast, recommended \|
	\| [Download](https://huggingface.co/matrixportal/txgemma-9b-chat-GGUF/resolve/main/txgemma-9b-chat-q4_k_m.gguf) \| ![Q4_K_M](https://img.shields.io/badge/Q4_K_M-673AB7) ⭐ \| Best balance \|
	\| [Download](https://huggingface.co/matrixportal/txgemma-9b-chat-GGUF/resolve/main/txgemma-9b-chat-q5_0.gguf) \| ![Q5_0](https://img.shields.io/badge/Q5_0-FF6D01) \| Good quality \|
	\| [Download](https://huggingface.co/matrixportal/txgemma-9b-chat-GGUF/resolve/main/txgemma-9b-chat-q5_k_s.gguf) \| ![Q5_K_S](https://img.shields.io/badge/Q5_K_S-0F9D58) \| Balanced \|
	\| [Download](https://huggingface.co/matrixportal/txgemma-9b-chat-GGUF/resolve/main/txgemma-9b-chat-q5_k_m.gguf) \| ![Q5_K_M](https://img.shields.io/badge/Q5_K_M-0F9D58) \| High quality \|
	\| [Download](https://huggingface.co/matrixportal/txgemma-9b-chat-GGUF/resolve/main/txgemma-9b-chat-q6_k.gguf) \| ![Q6_K](https://img.shields.io/badge/Q6_K-4285F4) 🏆 \| Very good quality \|
	\| [Download](https://huggingface.co/matrixportal/txgemma-9b-chat-GGUF/resolve/main/txgemma-9b-chat-q8_0.gguf) \| ![Q8_0](https://img.shields.io/badge/Q8_0-EA4335) ⚡ \| Fast, best quality \|
	\| [Download](https://huggingface.co/matrixportal/txgemma-9b-chat-GGUF/resolve/main/txgemma-9b-chat-f16.gguf) \| ![F16](https://img.shields.io/badge/F16-000000) \| Maximum accuracy \|

	💡 Tip: Use `F16` for maximum precision when quality is critical

	# GGUF Model Quantization & Usage Guide with llama.cpp

	## What is GGUF and Quantization?

	GGUF (GPT-Generated Unified Format) is an efficient model file format developed by the `llama.cpp` team that:
	- Supports multiple quantization levels
	- Works cross-platform
	- Enables fast loading and inference

	Quantization converts model weights to lower precision data types (e.g., 4-bit integers instead of 32-bit floats) to:
	- Reduce model size
	- Decrease memory usage
	- Speed up inference
	- (With minor accuracy trade-offs)

	## Step-by-Step Guide

	### 1. Prerequisites

	```bash
	# System updates
	sudo apt update && sudo apt upgrade -y

	# Dependencies
	sudo apt install -y build-essential cmake python3-pip

	# Clone and build llama.cpp
	git clone https://github.com/ggerganov/llama.cpp
	cd llama.cpp
	make -j4
	```

	### 2. Using Quantized Models from Hugging Face

	My automated quantization script produces models in this format:
	```
	https://huggingface.co/matrixportal/txgemma-9b-chat-GGUF/resolve/main/txgemma-9b-chat-q4_k_m.gguf
	```

	Download your quantized model directly:

	```bash
	wget https://huggingface.co/matrixportal/txgemma-9b-chat-GGUF/resolve/main/txgemma-9b-chat-q4_k_m.gguf
	```

	### 3. Running the Quantized Model

	Basic usage:
	```bash
	./main -m txgemma-9b-chat-q4_k_m.gguf -p "Your prompt here" -n 128
	```

	Example with a creative writing prompt:
	```bash
	./main -m txgemma-9b-chat-q4_k_m.gguf -p "[INST] Write a short poem about AI quantization in the style of Shakespeare [/INST]" -n 256 -c 2048 -t 8 --temp 0.7
	```

	Advanced parameters:
	```bash
	./main -m txgemma-9b-chat-q4_k_m.gguf -p "Question: What is the GGUF format?
	Answer:" -n 256 -c 2048 -t 8 --temp 0.7 --top-k 40 --top-p 0.9
	```

	### 4. Python Integration

	Install the Python package:
	```bash
	pip install llama-cpp-python
	```

	Example script:
	```python
	from llama_cpp import Llama

	# Initialize the model
	llm = Llama(
	model_path="txgemma-9b-chat-q4_k_m.gguf",
	n_ctx=2048,
	n_threads=8
	)

	# Run inference
	response = llm(
	"[INST] Explain GGUF quantization to a beginner [/INST]",
	max_tokens=256,
	temperature=0.7,
	top_p=0.9
	)

	print(response["choices"][0]["text"])
	```

	## Performance Tips

	1. Hardware Utilization:
	- Set thread count with `-t` (typically CPU core count)
	- Compile with CUDA/OpenCL for GPU support

	2. Memory Optimization:
	- Lower quantization (like q4_k_m) uses less RAM
	- Adjust context size with `-c` parameter

	3. Speed/Accuracy Balance:
	- Higher bit quantization is slower but more accurate
	- Reduce randomness with `--temp 0` for consistent results

	## FAQ

	Q: What quantization levels are available?
	A: Common options include q4_0, q4_k_m, q5_0, q5_k_m, q8_0

	Q: How much performance loss occurs with q4_k_m?
	A: Typically 2-5% accuracy reduction but 4x smaller size

	Q: How to enable GPU support?
	A: Build with `make LLAMA_CUBLAS=1` for NVIDIA GPUs

	## Useful Resources

	1. [llama.cpp GitHub](https://github.com/ggerganov/llama.cpp)
	2. [GGUF Format Specs](https://github.com/ggerganov/ggml/blob/master/docs/gguf.md)
	3. [Hugging Face Model Hub](https://huggingface.co/models)