Instructions to use zackliqcom/gemma4-E2B-Q40-custom with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zackliqcom/gemma4-E2B-Q40-custom with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("zackliqcom/gemma4-E2B-Q40-custom", dtype="auto")

llama-cpp-python

How to use zackliqcom/gemma4-E2B-Q40-custom with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="zackliqcom/gemma4-E2B-Q40-custom",
	filename="gemma-4-E2B-it-Q4_0.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 zackliqcom/gemma4-E2B-Q40-custom 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 zackliqcom/gemma4-E2B-Q40-custom:Q4_0
# Run inference directly in the terminal:
llama cli -hf zackliqcom/gemma4-E2B-Q40-custom:Q4_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf zackliqcom/gemma4-E2B-Q40-custom:Q4_0
# Run inference directly in the terminal:
llama cli -hf zackliqcom/gemma4-E2B-Q40-custom:Q4_0

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 zackliqcom/gemma4-E2B-Q40-custom:Q4_0
# Run inference directly in the terminal:
./llama-cli -hf zackliqcom/gemma4-E2B-Q40-custom:Q4_0

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 zackliqcom/gemma4-E2B-Q40-custom:Q4_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf zackliqcom/gemma4-E2B-Q40-custom:Q4_0

Use Docker

docker model run hf.co/zackliqcom/gemma4-E2B-Q40-custom:Q4_0

LM Studio
Jan
Ollama
How to use zackliqcom/gemma4-E2B-Q40-custom with Ollama:
```
ollama run hf.co/zackliqcom/gemma4-E2B-Q40-custom:Q4_0
```

Unsloth Studio

How to use zackliqcom/gemma4-E2B-Q40-custom 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 zackliqcom/gemma4-E2B-Q40-custom 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 zackliqcom/gemma4-E2B-Q40-custom to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for zackliqcom/gemma4-E2B-Q40-custom to start chatting

How to use zackliqcom/gemma4-E2B-Q40-custom with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf zackliqcom/gemma4-E2B-Q40-custom:Q4_0

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": "zackliqcom/gemma4-E2B-Q40-custom:Q4_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use zackliqcom/gemma4-E2B-Q40-custom with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf zackliqcom/gemma4-E2B-Q40-custom:Q4_0

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 zackliqcom/gemma4-E2B-Q40-custom:Q4_0

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use zackliqcom/gemma4-E2B-Q40-custom with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf zackliqcom/gemma4-E2B-Q40-custom:Q4_0

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 "zackliqcom/gemma4-E2B-Q40-custom:Q4_0" \
  --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 zackliqcom/gemma4-E2B-Q40-custom with Docker Model Runner:
```
docker model run hf.co/zackliqcom/gemma4-E2B-Q40-custom:Q4_0
```

Lemonade

How to use zackliqcom/gemma4-E2B-Q40-custom with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull zackliqcom/gemma4-E2B-Q40-custom:Q4_0

Run and chat with the model

lemonade run user.gemma4-E2B-Q40-custom-Q4_0

List all available models

lemonade list

Hugging Face | GitHub | Launch Blog | Documentation
License: Apache 2.0 | Authors: Google DeepMind

Gemma 4-E2B

This is a custom quantized version of the Gemma 4-E2B model, quantized to Q4_0 with custom OVERRIDE file. It is designed to achieve fast inference on Qualcomm Hexagon NPU while maintaining adequate accuracy.

how model is generated

Built with llama.cpp commit 7c158fb.

Three steps, run from the repo root.

Step 1 — download the unquantized HF model

hf download google/gemma-4-E2B-it-qat-q4_0-unquantized --local-dir ./hf-model

Step 2 — convert HF → F16 GGUF

convert_hf_to_gguf.py ./hf-model --outfile model-f16.gguf --outtype f16

Step 3 — follow the OVERRIDE file and quantize to Q4_0

build/bin/llama-quantize --tensor-type-file <override-file> \
    model-f16.gguf model-q4_0-override.gguf q4_0

Performance Measurement Commands

CPU uses --device none -ngl 0; HTP uses --device HTP0 -ngl 99. For each (model, backend, CTX ∈ {512, 1024, 4096}) two llama-bench runs were issued — one for prefill, one for decode:

# environment on device
export LD_LIBRARY_PATH=./lib
export ADSP_LIBRARY_PATH=./lib

# Prefill (Prefill tok/s; TTFT = CTX / Prefill × 1000)
./bin/llama-bench --device <none|HTP0> -m <model.gguf> \
  --poll 1000 -t 6 --cpu-mask 0xfc --cpu-strict 1 --ubatch-size 1024 -fa on \
  -ngl <0|99> -p <CTX> -n 0

# Decode at depth = CTX (Decode tok/s)
./bin/llama-bench --device <none|HTP0> -m <model.gguf> \
  --poll 1000 -t 6 --cpu-mask 0xfc --cpu-strict 1 --ubatch-size 1024 -fa on \
  -ngl <0|99> -p 0 -n 128 -d <CTX>

Performance Metrics

Performance on IQ9 (QCS9075M)

Compute	CTX	Unsloth PTQ GGUF	Google QAT GGUF	Ours
CPU	512	82.2 / 22.25	107.2 / 22.51	98.9 / 23.78
CPU	1024	76.6 / 21.65	97.8 / 21.94	90.8 / 23.16
CPU	4096	61.3 / 18.09	76.5 / 18.26	72.3 / 19.10
HTP	512	273.2 / 19.22	582.5 / 16.40	582.4 / 18.33
HTP	1024	266.9 / 18.93	552.3 / 16.45	552.2 / 18.17
HTP	4096	254.9 / 18.30	502.5 / 15.87	501.8 / 17.53

Accuracy Metrics

The MMLU-Pro is measured:

Subject	Unsloth PTQ GGUF	Google QAT GGUF	Ours
mmlu_pro	0.4084	0.4178	0.4131
biology	0.6248 ± 0.0181	0.6262	0.6248 ± 0.0181
business	0.4956 ± 0.0178	0.5019	0.4740 ± 0.0178
chemistry	0.3021 ± 0.0137	0.3127	0.3004 ± 0.0136
computer_science	0.4683 ± 0.0247	0.4854	0.4683 ± 0.0247
economics	0.4538 ± 0.0171	0.4976	0.4431 ± 0.0171
engineering	0.2859 ± 0.0145	0.3375	0.2931 ± 0.0146
health	0.3961 ± 0.0171	0.3423	0.3985 ± 0.0171
history	0.2730 ± 0.0229	0.2310	0.3097 ± 0.0237
law	0.2416 ± 0.0129	0.2289	0.2480 ± 0.0130
math	0.6366 ± 0.0131	0.6366	0.6373 ± 0.0131
other	0.3561 ± 0.0158	0.3528	0.3431 ± 0.0156
philosophy	0.3387 ± 0.0212	0.3848	0.3487 ± 0.0214
physics	0.3580 ± 0.0133	0.4003	0.3926 ± 0.0136
psychology	0.4875 ± 0.0177	0.5113	0.5013 ± 0.0177