Instructions to use mradermacher/gemma-4-12B-it-Esper4-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mradermacher/gemma-4-12B-it-Esper4-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("mradermacher/gemma-4-12B-it-Esper4-GGUF", device_map="auto")

llama-cpp-python

How to use mradermacher/gemma-4-12B-it-Esper4-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="mradermacher/gemma-4-12B-it-Esper4-GGUF",
	filename="gemma-4-12B-it-Esper4.IQ4_XS.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 mradermacher/gemma-4-12B-it-Esper4-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 mradermacher/gemma-4-12B-it-Esper4-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mradermacher/gemma-4-12B-it-Esper4-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 mradermacher/gemma-4-12B-it-Esper4-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mradermacher/gemma-4-12B-it-Esper4-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 mradermacher/gemma-4-12B-it-Esper4-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf mradermacher/gemma-4-12B-it-Esper4-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 mradermacher/gemma-4-12B-it-Esper4-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mradermacher/gemma-4-12B-it-Esper4-GGUF:Q4_K_M

Use Docker

docker model run hf.co/mradermacher/gemma-4-12B-it-Esper4-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use mradermacher/gemma-4-12B-it-Esper4-GGUF with Ollama:
```
ollama run hf.co/mradermacher/gemma-4-12B-it-Esper4-GGUF:Q4_K_M
```

Unsloth Studio

How to use mradermacher/gemma-4-12B-it-Esper4-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 mradermacher/gemma-4-12B-it-Esper4-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 mradermacher/gemma-4-12B-it-Esper4-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for mradermacher/gemma-4-12B-it-Esper4-GGUF to start chatting

How to use mradermacher/gemma-4-12B-it-Esper4-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mradermacher/gemma-4-12B-it-Esper4-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": "mradermacher/gemma-4-12B-it-Esper4-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use mradermacher/gemma-4-12B-it-Esper4-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 mradermacher/gemma-4-12B-it-Esper4-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 mradermacher/gemma-4-12B-it-Esper4-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use mradermacher/gemma-4-12B-it-Esper4-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mradermacher/gemma-4-12B-it-Esper4-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 "mradermacher/gemma-4-12B-it-Esper4-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 mradermacher/gemma-4-12B-it-Esper4-GGUF with Docker Model Runner:
```
docker model run hf.co/mradermacher/gemma-4-12B-it-Esper4-GGUF:Q4_K_M
```

Lemonade

How to use mradermacher/gemma-4-12B-it-Esper4-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mradermacher/gemma-4-12B-it-Esper4-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.gemma-4-12B-it-Esper4-GGUF-Q4_K_M

List all available models

lemonade list

gemma-4-12B-it-Esper4-GGUF

File size: 4,510 Bytes

---
base_model: ValiantLabs/gemma-4-12B-it-Esper4
datasets:
- sequelbox/Mitakihara2-DeepSeek-V4-Pro
- sequelbox/Tachibana4-DeepSeek-V4-Pro
- sequelbox/Titanium4-DeepSeek-V4-Pro
language:
- en
library_name: transformers
license: apache-2.0
mradermacher:
  readme_rev: 1
quantized_by: mradermacher
tags:
- esper
- esper-4
- valiant
- valiant-labs
- gemma
- gemma-4
- gemma-4-12b
- gemma-4-12b-it
- 12b
- reasoning
- code
- code-instruct
- python
- typescript
- javascript
- java
- c++
- c
- c#
- rust
- go
- haskell
- dev-ops
- jenkins
- terraform
- ansible
- docker
- jenkins
- kubernetes
- helm
- grafana
- prometheus
- shell
- bash
- azure
- aws
- gcp
- cloud
- scripting
- powershell
- problem-solving
- architect
- engineer
- developer
- creative
- analytical
- expert
- rationality
- conversational
- chat
- instruct
---
## About

<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type:  -->
<!-- ### tags:  -->
<!-- ### quants:  x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip:  -->
<!-- ### skip_mmproj:  -->
static quants of https://huggingface.co/ValiantLabs/gemma-4-12B-it-Esper4

<!-- provided-files -->

***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#gemma-4-12B-it-Esper4-GGUF).***

weighted/imatrix quants are available at https://huggingface.co/mradermacher/gemma-4-12B-it-Esper4-i1-GGUF
## Usage

If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.

## Provided Quants

(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)

| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/gemma-4-12B-it-Esper4-GGUF/resolve/main/gemma-4-12B-it-Esper4.mmproj-f16.gguf) | mmproj-f16 | 0.2 | multi-modal supplement |
| [GGUF](https://huggingface.co/mradermacher/gemma-4-12B-it-Esper4-GGUF/resolve/main/gemma-4-12B-it-Esper4.mmproj-Q8_0.gguf) | mmproj-Q8_0 | 0.3 | multi-modal supplement |
| [GGUF](https://huggingface.co/mradermacher/gemma-4-12B-it-Esper4-GGUF/resolve/main/gemma-4-12B-it-Esper4.Q2_K.gguf) | Q2_K | 4.9 |  |
| [GGUF](https://huggingface.co/mradermacher/gemma-4-12B-it-Esper4-GGUF/resolve/main/gemma-4-12B-it-Esper4.Q3_K_S.gguf) | Q3_K_S | 5.6 |  |
| [GGUF](https://huggingface.co/mradermacher/gemma-4-12B-it-Esper4-GGUF/resolve/main/gemma-4-12B-it-Esper4.Q3_K_M.gguf) | Q3_K_M | 6.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/gemma-4-12B-it-Esper4-GGUF/resolve/main/gemma-4-12B-it-Esper4.Q3_K_L.gguf) | Q3_K_L | 6.7 |  |
| [GGUF](https://huggingface.co/mradermacher/gemma-4-12B-it-Esper4-GGUF/resolve/main/gemma-4-12B-it-Esper4.IQ4_XS.gguf) | IQ4_XS | 6.8 |  |
| [GGUF](https://huggingface.co/mradermacher/gemma-4-12B-it-Esper4-GGUF/resolve/main/gemma-4-12B-it-Esper4.Q4_K_S.gguf) | Q4_K_S | 7.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/gemma-4-12B-it-Esper4-GGUF/resolve/main/gemma-4-12B-it-Esper4.Q4_K_M.gguf) | Q4_K_M | 7.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/gemma-4-12B-it-Esper4-GGUF/resolve/main/gemma-4-12B-it-Esper4.Q5_K_S.gguf) | Q5_K_S | 8.4 |  |
| [GGUF](https://huggingface.co/mradermacher/gemma-4-12B-it-Esper4-GGUF/resolve/main/gemma-4-12B-it-Esper4.Q5_K_M.gguf) | Q5_K_M | 8.6 |  |
| [GGUF](https://huggingface.co/mradermacher/gemma-4-12B-it-Esper4-GGUF/resolve/main/gemma-4-12B-it-Esper4.Q6_K.gguf) | Q6_K | 9.9 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/gemma-4-12B-it-Esper4-GGUF/resolve/main/gemma-4-12B-it-Esper4.Q8_0.gguf) | Q8_0 | 12.8 | fast, best quality |

Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):

![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png)

And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9

## FAQ / Model Request

See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.

## Thanks

I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.

<!-- end -->