Instructions to use DreamhubAI/Nova-e-mini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DreamhubAI/Nova-e-mini with Transformers:

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

pipe = pipeline("text-generation", model="DreamhubAI/Nova-e-mini")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("DreamhubAI/Nova-e-mini")
model = AutoModelForCausalLM.from_pretrained("DreamhubAI/Nova-e-mini")
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

vLLM

How to use DreamhubAI/Nova-e-mini with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/DreamhubAI/Nova-e-mini

SGLang

How to use DreamhubAI/Nova-e-mini 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 "DreamhubAI/Nova-e-mini" \
    --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": "DreamhubAI/Nova-e-mini",
		"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 "DreamhubAI/Nova-e-mini" \
        --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": "DreamhubAI/Nova-e-mini",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use DreamhubAI/Nova-e-mini 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 DreamhubAI/Nova-e-mini 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 DreamhubAI/Nova-e-mini to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for DreamhubAI/Nova-e-mini to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="DreamhubAI/Nova-e-mini",
    max_seq_length=2048,
)

Docker Model Runner
How to use DreamhubAI/Nova-e-mini with Docker Model Runner:
```
docker model run hf.co/DreamhubAI/Nova-e-mini
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

🤖 WALL•E — Lightweight Local AI Assistant (1B)

WALL•E is a fine-tuned, lightweight language model based on Gemma 3 1B, designed for local, privacy-preserving AI usage.
It focuses on practical tasks, fast responses, and real-world utility rather than model size.

🎯 Why WALL•E?

Most modern AI models are either:

Too large to run locally, or
Too generic for everyday tasks

WALL•E is built to fill that gap.

✅ Runs entirely locally
✅ No API keys or cloud services
✅ Designed for low-resource environments
✅ Open-source and transparent

✨ Key Capabilities

🌐 Multilingual Support

English – primary interaction language
فارسی (Persian) – natural and fluent responses
Deutsch (German) – conversational support

🛠 Practical Task Focus

📝 Text summarization (articles, notes, reports)
💻 Coding help (Python, JavaScript, Bash, shell)
🖥 Linux command explanations & troubleshooting
📚 Short factual answers and guidance

The model is optimized to handle short and minimal prompts naturally (e.g. "Hi", "Explain ls -la"), avoiding over-generation.

⚙️ Technical Overview

Component	Details
Base Model	Google Gemma 3 1B
Fine-tuning	Supervised Fine-Tuning (SFT)
Framework	Unsloth
Context Length	3200 tokens
Precision	BF16
License	Apache 2.0

🚀 Quick Start (Transformers)

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline

model_id = "sinamsv0/WALL-E"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto"
)

pipe = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer
)

response = pipe(
    "Summarize this text: Artificial intelligence is...",
    max_new_tokens=120
)

print(response[0]["generated_text"])