Instructions to use devoffeed/gemma-cvantic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use devoffeed/gemma-cvantic 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 devoffeed/gemma-cvantic:BF16
# Run inference directly in the terminal:
llama cli -hf devoffeed/gemma-cvantic:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf devoffeed/gemma-cvantic:BF16
# Run inference directly in the terminal:
llama cli -hf devoffeed/gemma-cvantic:BF16

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 devoffeed/gemma-cvantic:BF16
# Run inference directly in the terminal:
./llama-cli -hf devoffeed/gemma-cvantic:BF16

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 devoffeed/gemma-cvantic:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf devoffeed/gemma-cvantic:BF16

Use Docker

docker model run hf.co/devoffeed/gemma-cvantic:BF16

Llama new
LM Studio
Jan

vLLM

How to use devoffeed/gemma-cvantic with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "devoffeed/gemma-cvantic"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "devoffeed/gemma-cvantic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/devoffeed/gemma-cvantic:BF16

Ollama
How to use devoffeed/gemma-cvantic with Ollama:
```
ollama run hf.co/devoffeed/gemma-cvantic:BF16
```
Unsloth Desktop

How to use devoffeed/gemma-cvantic with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf devoffeed/gemma-cvantic:BF16

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "devoffeed/gemma-cvantic:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use devoffeed/gemma-cvantic with Docker Model Runner:
```
docker model run hf.co/devoffeed/gemma-cvantic:BF16
```

Lemonade

How to use devoffeed/gemma-cvantic with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull devoffeed/gemma-cvantic:BF16

Run and chat with the model

lemonade run user.gemma-cvantic-BF16

List all available models

lemonade list

Hermes Agent

How to use devoffeed/gemma-cvantic with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf devoffeed/gemma-cvantic:BF16

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 devoffeed/gemma-cvantic:BF16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use devoffeed/gemma-cvantic with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf devoffeed/gemma-cvantic:BF16

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 "devoffeed/gemma-cvantic:BF16" \
  --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"

gemma-cvantic

multimodal SFT fine-tune of Google's gemma-4-E4B-it · GGUF

What is this?

An experimental instruction fine-tune of google/gemma-4-E4B-it — the 4.5B-effective-parameter omni-modal Gemma 4 model (128K context, text + image + audio input).

Trained with SFT (13,000 examples) to strengthen math, coding, physics/astronomy reasoning and Russian-language instructions. Quantized to GGUF with llama.cpp for fast local inference.

Quantization lineup

File	Quant	Size	Quality / speed
`gemma-cvantic.Q8_0.gguf`	Q8_0	~7.6 GB	near-lossless, best quality
`gemma-cvantic.Q5_K_S.gguf`	Q5_K_S	~5.4 GB	great balance ★ recommended
`gemma-cvantic.IQ4_XS.gguf`	iQ4_XS (imatrix)	~4.8 GB	smallest, fast, slightly less accurate
`gemma-cvantic.BF16-mmproj.gguf`	vision projector	~0.9 GB	required for image input

mmproj is the multimodal (vision) projector — pass it with --mmproj to enable image understanding. All quants are BF16/FP16 conversions of the same merged weights, so any main-file + mmproj combo works.

Training recipe

Parameter	Value
Base model	`google/gemma-4-E4B-it`
Method	SFT (DoRA / LoRA-style adapter, then merged)
Trainable params	~36.7M (0.61%)
Examples	13,000
Context	128K (inherited)

Dataset mix (MIT / Apache-2.0 only):

Dataset	Split	Rows	Domain
`HuggingFaceH4/ultrachat_200k`	train_sft	5,000	general chat
`theblackcat102/evol-codealpaca-v1`	train	3,000	coding
`qwedsacf/competition_math` (MATH)	train	2,000	math
`HuggingFaceTB/cosmopedia`	openstax · physics/astronomy	2,000	physics & astronomy
`openai/gsm8k`	main/train	1,000	grade-school math

Quick start

llama.cpp (text)

llama-cli \
  -m gemma-cvantic.Q5_K_S.gguf \
  -p "Реши задачу: если цена товара выросла на 20% и составила 480 руб., какой была исходная цена?"

Multimodal (vision)

llama-cli \
  -m gemma-cvantic.Q5_K_S.gguf \
  --mmproj gemma-cvantic.BF16-mmproj.gguf \
  -i

> what's in this photo?

llama-server (OpenAI-compatible API)

llama-server \
  -m gemma-cvantic.Q8_0.gguf \
  --mmproj gemma-cvantic.BF16-mmproj.gguf \
  --port 8080

import openai

client = openai.OpenAI(base_url="http://localhost:8080/v1", api_key="local")
resp = client.chat.completions.create(
    model="gemma-cvantic",
    messages=[{"role": "user", "content": "Расскажи про эффект Доплера на пальцах"}],
)
print(resp.choices[0].message.content)

Picking a file

CPU-only, want quality → Q5_K_S (fits ~8 GB RAM/VRAM, sweet spot)
16 GB+ / strong GPU → Q8_0
Tiny footprint / speed first → iQ4_XS (fits ~6 GB)
Vision → always add the BF16-mmproj

Sizes are approximate; VRAM usage depends on context length.

Notes

Working-name "cvantic" — experimental build, quality varies per domain. Think of it as a field test of Gemma 4 E4B fine-tuning.
Text-only SFT; vision/audio behavior inherited from the base model and not specifically tuned.
Base model: Google DeepMind · License: Apache 2.0 · Gemma 4 docs

Quantized with llama.cpp (BF16 base + imatrix for iQ4_XS).

Downloads last month: 62

GGUF

Model size

8B params

Architecture

gemma4

Hardware compatibility

4-bit

5-bit

8-bit

Model tree for devoffeed/gemma-cvantic

Base model

google/gemma-4-E4B

Finetuned

google/gemma-4-E4B-it

Quantized

(420)

this model