Instructions to use meftah416/gemma-eppy-270m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use meftah416/gemma-eppy-270m with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="meftah416/gemma-eppy-270m")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("meftah416/gemma-eppy-270m")
model = AutoModelForCausalLM.from_pretrained("meftah416/gemma-eppy-270m")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use meftah416/gemma-eppy-270m with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "meftah416/gemma-eppy-270m"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "meftah416/gemma-eppy-270m",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/meftah416/gemma-eppy-270m

SGLang

How to use meftah416/gemma-eppy-270m with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "meftah416/gemma-eppy-270m" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "meftah416/gemma-eppy-270m",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "meftah416/gemma-eppy-270m" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "meftah416/gemma-eppy-270m",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use meftah416/gemma-eppy-270m with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for meftah416/gemma-eppy-270m to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for meftah416/gemma-eppy-270m to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for meftah416/gemma-eppy-270m to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="meftah416/gemma-eppy-270m",
    max_seq_length=2048,
)

Docker Model Runner
How to use meftah416/gemma-eppy-270m with Docker Model Runner:
```
docker model run hf.co/meftah416/gemma-eppy-270m
```

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 ⚡

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

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

Downloads last month: 171

Safetensors

Model size

0.3B params

Tensor type

F32

BF16

Model tree for meftah416/gemma-eppy-270m

Base model

google/gemma-3-270m

Finetuned

google/gemma-3-270m-it

Finetuned

unsloth/gemma-3-270m-it

Finetuned

(415)

this model