Modelpublic

CharlieBonito/clarity-guard-gemma4-7b

sourceHugging Faceapache-2.0updated 4mo agoView on Hugging Face

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Model Card

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	Q4KM
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

python

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

bash

# 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