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

mradermacher commited on 13 days ago

Commit

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@@ -1,3 +1,71 @@
 <!-- ### quantize_version: 2 -->
 <!-- ### output_tensor_quantised: 1 -->
 <!-- ### convert_type: hf -->
@@ -7,3 +75,53 @@
 <!-- ### quants_skip:  -->
 <!-- ### skip_mmproj:  -->
 static quants of https://huggingface.co/ValiantLabs/gemma-4-12B-it-Esper4

+---
+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 -->
 <!-- ### 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 seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
+## 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.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.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 -->