Instructions to use CharlieBonito/clarity-guard-gemma4-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CharlieBonito/clarity-guard-gemma4-7b with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="CharlieBonito/clarity-guard-gemma4-7b",
	filename="ClarityGuard-v2.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use CharlieBonito/clarity-guard-gemma4-7b with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf CharlieBonito/clarity-guard-gemma4-7b
# Run inference directly in the terminal:
llama-cli -hf CharlieBonito/clarity-guard-gemma4-7b

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf CharlieBonito/clarity-guard-gemma4-7b
# Run inference directly in the terminal:
llama-cli -hf CharlieBonito/clarity-guard-gemma4-7b

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 CharlieBonito/clarity-guard-gemma4-7b
# Run inference directly in the terminal:
./llama-cli -hf CharlieBonito/clarity-guard-gemma4-7b

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 CharlieBonito/clarity-guard-gemma4-7b
# Run inference directly in the terminal:
./build/bin/llama-cli -hf CharlieBonito/clarity-guard-gemma4-7b

Use Docker

docker model run hf.co/CharlieBonito/clarity-guard-gemma4-7b

LM Studio
Jan
Ollama
How to use CharlieBonito/clarity-guard-gemma4-7b with Ollama:
```
ollama run hf.co/CharlieBonito/clarity-guard-gemma4-7b
```

Unsloth Studio

How to use CharlieBonito/clarity-guard-gemma4-7b 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 CharlieBonito/clarity-guard-gemma4-7b 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 CharlieBonito/clarity-guard-gemma4-7b to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for CharlieBonito/clarity-guard-gemma4-7b to start chatting

How to use CharlieBonito/clarity-guard-gemma4-7b with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf CharlieBonito/clarity-guard-gemma4-7b

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": "CharlieBonito/clarity-guard-gemma4-7b"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use CharlieBonito/clarity-guard-gemma4-7b with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf CharlieBonito/clarity-guard-gemma4-7b

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 CharlieBonito/clarity-guard-gemma4-7b

Run Hermes

hermes

Docker Model Runner
How to use CharlieBonito/clarity-guard-gemma4-7b with Docker Model Runner:
```
docker model run hf.co/CharlieBonito/clarity-guard-gemma4-7b
```

Lemonade

How to use CharlieBonito/clarity-guard-gemma4-7b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull CharlieBonito/clarity-guard-gemma4-7b

Run and chat with the model

lemonade run user.clarity-guard-gemma4-7b-{{QUANT_TAG}}

List all available models

lemonade list

ClarityGuard Gemma 4 E4B

Fine-tuned Gemma 4 E4B model for ClarityGuard - a neuro-inclusive communication assistant that helps decode ambiguous workplace and personal messages.

Model Details

Property	Value
Base Model	Gemma 4 E4B (4-bit quantized)
Fine-tuning	Unsloth Studio
Quantization	Q4_K_M
Training Max Sequence Length	4096 tokens
Recommended llama.cpp Context	16384 tokens
Multimodal	Yes (via mmproj)
Training Checkpoint	750

Files

ClarityGuard-v2.gguf - Main model (~5GB)
mmproj-ClarityGuard-v2.gguf - Multimodal projection (~1GB)

Older checkpoint 375 GGUF names may appear in historical notes or previous demos. The active production files for this submission are the v2 files listed above.

Usage

With llama.cpp

from llama_cpp import Llama

llm = Llama(
    model_path="ClarityGuard-v2.gguf",
    mmproj="mmproj-ClarityGuard-v2.gguf",
    n_ctx=16384,
    n_gpu_layers=-1,  # Use all GPU layers
)

response = llm.create_chat_completion(
    messages=[
        {"role": "system", "content": "You are ClarityGuard..."},
        {"role": "user", "content": "Analyze this message: 'We need to fix that soon'"}
    ]
)

With Ollama

# Create Modelfile
echo 'FROM ./ClarityGuard-v2.gguf' > Modelfile
ollama create clarity-guard -f Modelfile
ollama run clarity-guard

Training Details

This model was fine-tuned using Unsloth QLoRA 4-bit on a local Linux/KachiOS workstation with an RTX 5070 Ti 16 GB GPU. The micro-batch was kept at 1 to avoid VRAM spikes, with 4 gradient accumulation steps.

Hyperparameter	Value
Adapter configuration	QLoRA adapter via Unsloth Studio; exact final r/alpha not independently verified
Load in 4-bit	True
Max sequence length	4096
Micro-batch / gradient accumulation	1 / 4
Learning rate	1.5e-4
Optimizer	adamw_8bit
Precision	bf16

Training Metric	Value
Initial loss	9.49
Final loss	0.72
Minimum loss	0.64 at step 364
Loss reduction	92.4%
Active checkpoint	750

The custom dataset was designed for:

Communication clarity analysis using the C.F.R.V.A. framework
Neurodivergent-friendly explanations
Workplace message decoding
Recognizing manipulation patterns and structural ambiguity

C.F.R.V.A. Framework

Factor	What It Detects
Context	Undeclared context or hidden assumptions
Framing	Undefined terms or missing criteria
Responsibility	Ghost "we" or unclear ownership
Validation	Approval conditioned on not asking
Ambiguity	Jargon, metaphors, or unwritten support

Intended Use

ClarityGuard helps neurodivergent individuals (autistic, ADHD, dyslexic) decode ambiguous workplace and personal messages by analyzing message structure - not the user's ability to understand.

Core principle: When a message lacks a clear subject, deadline, or measurable criterion, confusion is the logical response to incomplete input - not a cognitive error.

Competition

Built for the Gemma 4 Good Hackathon 2026:

Digital Equity & Inclusivity Track
Safety & Trust Track
Unsloth Special Track
llama.cpp Special Track

License

Apache 2.0

Acknowledgments

Google DeepMind for Gemma 4
Unsloth for fine-tuning tools
Hugging Face for model hosting

Built with ❤️ for the neurodivergent community

Downloads last month: 390

GGUF

Model size

8B params

Architecture

gemma4

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

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Model tree for CharlieBonito/clarity-guard-gemma4-7b

Base model

google/gemma-4-E4B

Finetuned

google/gemma-4-E4B-it

Quantized

(204)

this model

CharlieBonito
/

clarity-guard-gemma4-7b