Truthseeker87/solarhive-e4b-ollama

SolarHive E4B Ollama — Edge Solar Energy Intelligence

LoRA fine-tuned Gemma 4 E4B (8B), merged to 16-bit safetensors for edge deployment via Ollama. Community energy data never leaves the neighborhood.

This repository contains merged safetensors (base model + LoRA adapters baked together). Ollama can import safetensors via ollama create --experimental — no GGUF conversion needed. For comparison, the official base model is also available as gemma4:e4b (GGUF, 9.6 GB).

Built for the Gemma 4 Good Hackathon (Google DeepMind x Kaggle).

Base Model	google/gemma-4-e4b-it
Architecture	Dense + PLE — 8B total, 4.5B effective
Fine-Tuning	LoRA via Unsloth (BF16)
Training Data	1,029 community solar energy examples
Converged Loss	0.952
Benchmark	7/8 (5/5 domain Q&A + 2/3 tool calling)
Training Time	282 seconds (~4.7 minutes)
Compute	Google Colab Pro
License	MIT (adapters) / Gemma Terms (base model)

---

Model Overview

SolarHive E4B is the edge companion to SolarHive 26B A4B. While the 26B model powers cloud inference with full multimodal VQA, the E4B model is optimized for local deployment via Ollama on consumer hardware.

Privacy-first: Running Gemma 4 locally means community energy data never leaves the neighborhood. No cloud dependency, no internet requirement, no data privacy concerns. A village in rural India, a suburb in Michigan, and a coastal town recovering from a hurricane all get the same intelligence.

This repository contains the fully merged model (base + LoRA baked together) — no separate base model download needed.

---

Training Details

Parameter	Value
Method	LoRA via Unsloth FastVisionModel (BF16, RTX PRO 6000 96 GB)
LoRA rank	16
LoRA alpha	16
LoRA dropout	0
Target modules	All linear layers
Learning rate	2e-4
Optimizer	AdamW 8-bit
Warmup steps	5
Epochs	3
Max sequence length	2048
Precision	BF16
Seed	3407
Trainable parameters	41.2M / 8.0B (0.51%)

Training Loss

Metric	Value
Converged loss (last 20 steps)	0.952
Final step loss	1.088
Minimum loss	0.455
Total steps	195
Training time	282 seconds

Training Data

Same 1,029 examples as the 26B A4B model:

• 413 hand-crafted examples spanning 15+ US cities and 9 energy domains
• 516 API-grounded examples from live Open-Meteo, PVWatts, OWM, and EIA data
• 100 tool-calling examples (50 with tools, 50 without)

See the SolarHive Dataset for full documentation.

Hardware

• GPU: NVIDIA RTX PRO 6000 Blackwell Server Edition (96 GB GDDR7)
• Platform: Google Colab Pro (G4 VM)

---

Benchmark Results

Domain Q&A (5/5)

Question	Result
Solar production when humidity exceeds 80%?	Correct
Battery SOC threshold for grid export?	Correct
Home #3 underperforming 22% — diagnostic checklist?	Correct
Winter snow on panels — prioritize actions?	Correct
Grid frequency 59.8 Hz — microgrid implications?	Correct

Tool Calling (2/3)

Question	Expected	Called	Status
Current battery state?	get_battery_state	get_battery_status	Fail
Solar production in Seattle?	get_solar_production or get_weather	get_solar_production	Pass
General panel maintenance tips?	None	None	Pass

---

How to Use

Loading with transformers

from transformers import AutoModelForCausalLM, AutoProcessor
import torch

model = AutoModelForCausalLM.from_pretrained(
    "Truthseeker87/solarhive-e4b-ollama",  # This repo (merged safetensors)
    dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True,
)
processor = AutoProcessor.from_pretrained(
    "Truthseeker87/solarhive-e4b-ollama",
    trust_remote_code=True,
)

Edge Deployment via Ollama

Ollama supports Gemma 4 safetensors import via the --experimental flag. No GGUF conversion needed.

# 1. Clone or download this repo
git clone https://huggingface.co/Truthseeker87/solarhive-e4b-ollama
cd solarhive-e4b-ollama

# 2. Create a Modelfile
cat > Modelfile << 'EOF'
FROM .
SYSTEM "You are SolarHive, an AI energy advisor for a community of 12 homes with rooftop solar and shared battery storage in Ann Arbor, Michigan. Use the available tools to get real-time data before answering. Be specific, reference actual data, and keep responses concise (3-5 sentences)."
PARAMETER temperature 1.0
PARAMETER top_p 0.95
PARAMETER top_k 64
PARAMETER num_ctx 4096
EOF

# 3. Import the model (--experimental required for Gemma 4 safetensors)
ollama create solarhive --experimental -f Modelfile

# 4. Run it
ollama run solarhive "What's the best time to run my dishwasher today?"

Note: The official base (non-fine-tuned) E4B is also available as a pre-built GGUF on ollama.com/library/gemma4:e4b (9.6 GB, Q4_K_M). This fine-tuned version adds 1,029 examples of community solar domain expertise.

---

Companion Repositories

Model	Repository	Purpose
SolarHive 26B A4B LoRA	solarhive-26b-a4b-lora	Cloud inference with full multimodal + function calling
SolarHive E4B Ollama	This repo	Edge deployment via Ollama (merged safetensors)
SolarHive Dataset	solarhive-community-solar-1k	1,029 training examples
GitHub	the-gemma4-good-hackathon-solarhive	Full source code, notebooks, data principles

---

Links

• GitHub: youshen-lim/the-gemma4-good-hackathon-solarhive
• Kaggle: The Gemma 4 Good Hackathon

Built with Gemma 4 in Ann Arbor, Michigan. April 2026.

Gemma is a trademark of Google LLC.