A fine-tuned Gemma 4 E2B model optimized for on-device AI tool use in the SnowClaw desktop agent. Achieves 100% tool use accuracy (7/7) on our evaluation set.

Model Details

Property	Value
Base Model	`google/gemma-4-E2B-it` (Gemma 4 E2B Instruct)
Method	LoRA (rank=64, alpha=64) via Unsloth
Training Data	2,000 synthetic tool-use examples
Epochs	15
Final Loss	0.040
Hardware	NVIDIA RTX 3090 (24GB VRAM)
Quantization	Q4_K_M (GGUF)
Vision	Multimodal — includes mmproj for image understanding

Files

File	Size	Description
`gemma-4-e2b-it.Q4_K_M.gguf`	3.2 GB	Main model (Q4_K_M quantized)
`gemma-4-e2b-it.BF16-mmproj.gguf`	942 MB	Vision projector (multimodal image encoder)
`Modelfile`	205 B	Ollama registration file

Intended Use

SnowClaw is a privacy-first desktop AI agent that runs entirely on-device. This model is fine-tuned for:

Tool Use: Executing system commands, browsing files, managing contacts/calendar
Code Generation: Writing and executing Python/AppleScript in a sandboxed environment
Screenshot Analysis: Understanding screen content via vision capabilities
Privacy: All processing stays local — zero data leaves the device

How to Use

With Ollama

bash

# Download the GGUF files, then register with Ollama:
ollama create snowclaw -f Modelfile

# Run
ollama run snowclaw

With llama.cpp

bash

# Text only
./llama-cli -m gemma-4-e2b-it.Q4_K_M.gguf -p "List files in my Downloads folder"

# With vision (multimodal)
./llama-mtmd-cli \
  -m gemma-4-e2b-it.Q4_K_M.gguf \
  --mmproj gemma-4-e2b-it.BF16-mmproj.gguf

Training Details

LoRA Configuration

json

{
  "r": 64,
  "lora_alpha": 64,
  "target_modules": ["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"],
  "lora_dropout": 0,
  "task_type": "CAUSAL_LM"
}

Training Curve

Step	Loss
10	8.147
20	1.074
30	0.469
100	0.099
500	0.043
1000	0.038
1875	0.040

Dataset

2,000 synthetic examples covering:

System tool invocations (file management, process control)
Contact and calendar queries
Device information retrieval
Multi-step task planning
Safety-aware refusals

Evaluation

Metric	Score
Tool Use Accuracy	7/7 (100%)
Correct Tool Selection	7/7
Parameter Extraction	7/7

Part of SnowClaw

SnowClaw is a privacy-first AI agent built for the Google Gemma Hackathon. It features:

On-device inference via bundled Ollama
Dual security modes: Paranoid (fully offline) / Smart Search (local + anonymous SearXNG)
E2E encrypted communication between desktop and mobile
Hardware-aware model selection (auto-detects CPU/GPU/RAM)

Author

Kennt Kim — Calida Lab

License

Apache 2.0 (following Gemma's license terms)

Kenntkim66/snowclaw-gemma4-e2b-ft-gguf

SnowClaw — Fine-tuned Gemma 4 E2B for Privacy-First Tool Use