---
base_model: unsloth/gemma-3-270m-it
library_name: transformers
tags:
- text-generation-inference
- transformers
- unsloth
- gemma
- trl
- sft
- energyplus
- eppy
- building-simulation
license: apache-2.0
language:
- en
datasets:
- self-created
pipeline_tag: 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](https://huggingface.co/unsloth/gemma-3-270m-it)

This model was trained **2x faster with [Unsloth](https://github.com/unslothai/unsloth)** ⚡

[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

---

## 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
---

## 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 |

---
## 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
---
## Usage
### Basic Usage
```python
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
---
## Performance
- **Inference Speed:** ~50-100 tokens/sec (A10 GPU)
- **Memory Usage:** 3-4 GB VRAM (float16)
- **Context Window:** 8192 tokens
---
## Limitations
⚠️ Always validate generated EnergyPlus IDF files before running simulations. Model may occasionally generate incorrect syntax.

---
## License
Apache 2.0 License

---
**Created by:** meftah416