gemma-eppy-270m/README.md

base_model, library_name, tags, license, language, datasets, pipeline_tag

base_model	library_name	tags	license	language	datasets	pipeline_tag
unsloth/gemma-3-270m-it	transformers	text-generation-inference transformers unsloth gemma trl sft energyplus eppy building-simulation	apache-2.0	en	self-created	text-generation

Gemma-3-270M Fine-tuned for EnergyPlus & eppy

Developed by: meftah416
Model License: apache-2.0
Base Model: unsloth/gemma-3-270m-it

This model was trained 2x faster with Unsloth ⚡

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Model Overview

A specialized Gemma-3-270M model fine-tuned on EnergyPlus building simulation and eppy Python API tasks. This model understands and generates text related to building energy simulation, HVAC systems, thermal zones, schedules, and eppy scripting.

Key Features

• ✨ Fine-tuned on 2,700 high-quality training examples
• ✅ Validated on 300 test examples
• 🚀 2x faster training with Unsloth
• 💾 Lightweight (270M parameters)
• 🎯 Domain-specific for EnergyPlus + eppy

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Training Details

Metric	Value
Base Model	unsloth/gemma-3-270m-it
Training Samples	2,700
Validation Samples	300
Total Data	3,000 examples
Data Source	Self-created
Domain	EnergyPlus IDF + eppy Python
Batch Size	2
Learning Rate	2e-5
Optimizer	adamw_8bit
Epochs	1
Max Sequence Length	2600
Training Framework	Unsloth + TRL (SFTTrainer)
Precision	float16

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Model Capabilities

This model is trained to handle:

EnergyPlus Tasks

• Generate EnergyPlus IDF snippets from descriptions
• Explain EnergyPlus object syntax
• Create thermal zone definitions
• Define HVAC system configurations
• Generate occupancy and schedule objects

eppy Tasks

• Generate eppy Python code for building simulations
• Explain eppy API usage
• Create building objects programmatically
• Manipulate IDF files with eppy

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Usage

Basic Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("meftah416/gemma-eppy-270m")
tokenizer = AutoTokenizer.from_pretrained("meftah416/gemma-eppy-270m")

# Create messages in correct format
messages = [
    {"role": "system", "content": "Set infiltration to 0.4 ACH"},
    {"role": "user", "content": ""},
]

# Apply chat template (IMPORTANT!)
prompt = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
).removeprefix('<bos>')

# Generate
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=2600)
result = tokenizer.decode(outputs[0])

Save to model README on Hub

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Performance

• Inference Speed: ~50-100 tokens/sec (A10 GPU)
• Memory Usage: 3-4 GB VRAM (float16)
• Context Window: 8192 tokens

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Limitations

⚠️ Always validate generated EnergyPlus IDF files before running simulations. Model may occasionally generate incorrect syntax.

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License

Apache 2.0 License

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Created by: meftah416