README.md

---
base_model: google/gemma-3-1b-it
tags:
- text-generation
- finetune
- transformers
- unsloth
- gemma3
- wall-e
- lightweight
- mobile-friendly
- local-ai
- multilingual
- coding-assistant
- text-summarization
license: apache-2.0
language:
- en
- fa
- de
library_name: transformers
pipeline_tag: text-generation
---

[![Open in HF Space](https://img.shields.io/badge/🤗%20Try%20Live%20Demo-FFD21E?style=for-the-badge&logo=huggingface)](https://huggingface.co/spaces/sinamsv0/WALL-E-DEMO)
[![GitHub](https://img.shields.io/badge/⭐%20GitHub-181717?style=for-the-badge&logo=github)](https://github.com/unknownmsv/WALL-E)
[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg?style=for-the-badge)](LICENSE)

# 🤖 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)

```python
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"])
```



## 🧪 Training Summary


Method: Supervised Fine-Tuning (SFT)

Data: Custom multilingual datasets with safety-focused filtering

Hardware: Single consumer GPU

Goal: Improve instruction-following, multilingual responses, and short-prompt behavior





## 🛡 Safety & Limitations

- ✅ Trained with safety-aware data
- ✅ Avoids harmful or unethical requests
- ⚠️ Limited reasoning depth due to 1B parameter size
- ⚠️ Not intended for complex multi-step reasoning or creative writing



## 🌍 Ideal Use Cases


Local coding assistant

Study and document summarization

Privacy-focused users

Lightweight edge deployments

Research and experimentation with small LLMs





## 🤝 Community & Links


GitHub: https://github.com/unknownmsv/WALL-E

Hugging Face Model: https://huggingface.co/sinamsv0/WALL-E

Hugging Face Space: https://huggingface.co/spaces/sinamsv0/WALL-E-DEMO





## 🔮 Roadmap (Planned)


UI tools for local use

Optional voice interface

Extended language support

Performance benchmarking on edge devices






Small model, focused design.
WALL•E proves that useful AI doesn’t have to be huge.