Instructions to use deadbydawn101/gemma-4-E4B-Agentic-Sol-Fable-Reasoning-GeminiCLI-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use deadbydawn101/gemma-4-E4B-Agentic-Sol-Fable-Reasoning-GeminiCLI-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="deadbydawn101/gemma-4-E4B-Agentic-Sol-Fable-Reasoning-GeminiCLI-GGUF",
	filename="gemma-4-E4B-v2-Sol-Fable-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 deadbydawn101/gemma-4-E4B-Agentic-Sol-Fable-Reasoning-GeminiCLI-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 deadbydawn101/gemma-4-E4B-Agentic-Sol-Fable-Reasoning-GeminiCLI-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf deadbydawn101/gemma-4-E4B-Agentic-Sol-Fable-Reasoning-GeminiCLI-GGUF:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf deadbydawn101/gemma-4-E4B-Agentic-Sol-Fable-Reasoning-GeminiCLI-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf deadbydawn101/gemma-4-E4B-Agentic-Sol-Fable-Reasoning-GeminiCLI-GGUF:F16

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 deadbydawn101/gemma-4-E4B-Agentic-Sol-Fable-Reasoning-GeminiCLI-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf deadbydawn101/gemma-4-E4B-Agentic-Sol-Fable-Reasoning-GeminiCLI-GGUF:F16

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 deadbydawn101/gemma-4-E4B-Agentic-Sol-Fable-Reasoning-GeminiCLI-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf deadbydawn101/gemma-4-E4B-Agentic-Sol-Fable-Reasoning-GeminiCLI-GGUF:F16

Use Docker

docker model run hf.co/deadbydawn101/gemma-4-E4B-Agentic-Sol-Fable-Reasoning-GeminiCLI-GGUF:F16

LM Studio
Jan

vLLM

How to use deadbydawn101/gemma-4-E4B-Agentic-Sol-Fable-Reasoning-GeminiCLI-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "deadbydawn101/gemma-4-E4B-Agentic-Sol-Fable-Reasoning-GeminiCLI-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": "deadbydawn101/gemma-4-E4B-Agentic-Sol-Fable-Reasoning-GeminiCLI-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/deadbydawn101/gemma-4-E4B-Agentic-Sol-Fable-Reasoning-GeminiCLI-GGUF:F16

Ollama
How to use deadbydawn101/gemma-4-E4B-Agentic-Sol-Fable-Reasoning-GeminiCLI-GGUF with Ollama:
```
ollama run hf.co/deadbydawn101/gemma-4-E4B-Agentic-Sol-Fable-Reasoning-GeminiCLI-GGUF:F16
```

Unsloth Studio

How to use deadbydawn101/gemma-4-E4B-Agentic-Sol-Fable-Reasoning-GeminiCLI-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 deadbydawn101/gemma-4-E4B-Agentic-Sol-Fable-Reasoning-GeminiCLI-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 deadbydawn101/gemma-4-E4B-Agentic-Sol-Fable-Reasoning-GeminiCLI-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for deadbydawn101/gemma-4-E4B-Agentic-Sol-Fable-Reasoning-GeminiCLI-GGUF to start chatting

How to use deadbydawn101/gemma-4-E4B-Agentic-Sol-Fable-Reasoning-GeminiCLI-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf deadbydawn101/gemma-4-E4B-Agentic-Sol-Fable-Reasoning-GeminiCLI-GGUF:F16

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": "deadbydawn101/gemma-4-E4B-Agentic-Sol-Fable-Reasoning-GeminiCLI-GGUF:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use deadbydawn101/gemma-4-E4B-Agentic-Sol-Fable-Reasoning-GeminiCLI-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 deadbydawn101/gemma-4-E4B-Agentic-Sol-Fable-Reasoning-GeminiCLI-GGUF:F16

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 deadbydawn101/gemma-4-E4B-Agentic-Sol-Fable-Reasoning-GeminiCLI-GGUF:F16

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use deadbydawn101/gemma-4-E4B-Agentic-Sol-Fable-Reasoning-GeminiCLI-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf deadbydawn101/gemma-4-E4B-Agentic-Sol-Fable-Reasoning-GeminiCLI-GGUF:F16

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 "deadbydawn101/gemma-4-E4B-Agentic-Sol-Fable-Reasoning-GeminiCLI-GGUF:F16" \
  --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 deadbydawn101/gemma-4-E4B-Agentic-Sol-Fable-Reasoning-GeminiCLI-GGUF with Docker Model Runner:
```
docker model run hf.co/deadbydawn101/gemma-4-E4B-Agentic-Sol-Fable-Reasoning-GeminiCLI-GGUF:F16
```

Lemonade

How to use deadbydawn101/gemma-4-E4B-Agentic-Sol-Fable-Reasoning-GeminiCLI-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull deadbydawn101/gemma-4-E4B-Agentic-Sol-Fable-Reasoning-GeminiCLI-GGUF:F16

Run and chat with the model

lemonade run user.gemma-4-E4B-Agentic-Sol-Fable-Reasoning-GeminiCLI-GGUF-F16

List all available models

lemonade list

Gemma 4 E4B v2 — Sol + FABLE.5 + Opus Reasoning + Claude Code | 22K Examples | No Adapter Needed | Tool Calling ✅ | OpenHarness ✅ | OpenClaw ✅ | Hermes Agent ✅ | Reasoning Baked In GGUF

Thank you for 140,000+ downloads on v1. We were the first to train Gemma 4 at the weights. We will continue to innovate and simply do what others can't.

Built by RavenX AI Labs — San Jose, CA

GGUF Downloads

Quant	Size	Use Case
Q4_K_M	5.07 GB	Best balance of quality and size — recommended
F16	15.0 GB	Full precision, maximum quality

For the MLX version (Apple Silicon native), see:

👉 gemma-4-E4B-Agentic-Sol-Fable-Reasoning-GeminiCLI-mlx-4bit

Quickstart

Ollama

ollama run hf.co/deadbydawn101/gemma-4-E4B-Agentic-Sol-Fable-Reasoning-GeminiCLI-GGUF

llama.cpp

llama-cli -m gemma-4-E4B-v2-Sol-Fable-Q4_K_M.gguf \
  -p "Write a Python function that implements binary search with error handling." \
  -n 2048

LM Studio

Download the Q4_K_M GGUF and load directly in LM Studio.

What's New in v2

This is the 10x update to the model that started it all. 22,389 training examples, up from 2,163.

	v1 (April 2026)	v2 (July 2026)
Training examples	2,163	22,389 (10x)
Data sources	Opus reasoning	Sol + FABLE.5 + Opus
Coding traces	0	17,939 (xhigh reasoning, tool use)
Thinking traces	0	4,450 (with `<think>` blocks)
Final training loss	—	1.8984
Adapter needed?	No	No

Data Sources

Dataset	Examples	What It Teaches
GPT-5.6 Sol Coding Traces	17,939	Production coding, debugging, tool use, acceptance-tested solutions
Complete FABLE.5 Traces	4,450	Deep reasoning with `<think>` blocks, context→completion
Opus 4.6 Reasoning	2,163	Claude-style structured reasoning (from v1)

Gym Benchmarks — 7B Model, Local Apple Silicon

Evaluated on 6 hard tasks across security, coding, and reasoning. All responses generated locally on M4 Max 128GB at 15.5 tokens/sec average.

Task	Category	Tokens	Speed	Result
RATH Security Report	Security	1,378	25.1 t/s	Full CVSS + CWE + MITRE ATT&CK report
Privilege Escalation	Security	385	24.1 t/s	Correctly refused unauthorized exploitation
Thread-Safe LRU+TTL Cache	Coding	4,270	15.3 t/s	Production Python with full test suite
CSV Data Pipeline	Agentic Coding	8,192	14.9 t/s	Hit max tokens — wanted to write MORE
Combinatorics Problem	Math Reasoning	3,072	14.9 t/s	Formal set theory with LaTeX notation
Distributed Rate Limiter	System Design	3,676	15.0 t/s	Complete Redis-backed implementation

Total: 20,973 tokens generated in 22 minutes. 5/6 production quality, 1/6 correct safety refusal.

The Story

On April 2, 2026, Google released Gemma 4. Its gemma4 architecture wasn't supported by any training framework.

On April 9, we shipped the first working fine-tune. Seven days. We built custom Gemma 4 support into our training framework and shipped before anyone else.

Unsloth published their Gemma 4 training guide on July 18 — three months later.

140,000+ people downloaded v1. Zero community issues. v2 is our thank you — 10x the training data.

All Formats

Format	Link
🆕 GGUF (this repo)	You're here
🆕 MLX 4-bit (v2)	Apple Silicon native
v2 LoRA adapters	Standalone adapters
v1 MLX (Opus only)	Original 140K download model
v1 GGUF (Opus only)	Original GGUF

The RavenX Gemma 4 Stack

Repo	What
unsloth-mlx	Training framework — we added Gemma 4 support
mlx-gemma4	Custom model implementation + converter
ravenx-mtp-drafter	Reverse-engineered Google's hidden MTP heads
ravenx-training-gym	Harbor-native security benchmark

About RavenX AI Labs

Security AI infrastructure company. San Jose, CA. 200K+ HF downloads. 26+ shipped models. 2 USPTO patents filed.

USPTO #64/087,357 — Soul Infusion (identity-framed training)
USPTO #64/104,760 — Sovereignty Chain (cryptographic model protection)

GitHub: @DeadByDawn101 | X: @RavenXllm

"We trained Gemma 4 in April. Unsloth published their guide in July. We simply do what others can't."

— RavenX AI Labs LLC, since June 2026

Downloads last month: 194

GGUF

Model size

8B params

Architecture

gemma4

Hardware compatibility

4-bit

16-bit

Model tree for deadbydawn101/gemma-4-E4B-Agentic-Sol-Fable-Reasoning-GeminiCLI-GGUF

Base model

google/gemma-4-E4B

Finetuned

google/gemma-4-E4B-it

Quantized

deadbydawn101/gemma-4-E4B-mlx-4bit

Finetuned

(5)

this model

deadbydawn101
/

gemma-4-E4B-Agentic-Sol-Fable-Reasoning-GeminiCLI-GGUF

Gemma 4 E4B v2 — Sol + FABLE.5 + Opus Reasoning + Claude Code | 22K Examples | No Adapter Needed | Tool Calling ✅ | OpenHarness ✅ | OpenClaw ✅ | Hermes Agent ✅ | Reasoning Baked In GGUF

Built by RavenX AI Labs — San Jose, CA

GGUF Downloads

For the MLX version (Apple Silicon native), see:

Quickstart

Ollama

llama.cpp

LM Studio

What's New in v2

Data Sources

Gym Benchmarks — 7B Model, Local Apple Silicon

The Story

All Formats

The RavenX Gemma 4 Stack

About RavenX AI Labs

Model tree for deadbydawn101/gemma-4-E4B-Agentic-Sol-Fable-Reasoning-GeminiCLI-GGUF

Datasets used to train deadbydawn101/gemma-4-E4B-Agentic-Sol-Fable-Reasoning-GeminiCLI-GGUF