🤖 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)
1from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
2
3model_id = "sinamsv0/WALL-E"
4
5tokenizer = AutoTokenizer.from_pretrained(model_id)
6model = AutoModelForCausalLM.from_pretrained(
7 model_id,
8 device_map="auto"
9)
10
11pipe = pipeline(
12 "text-generation",
13 model=model,
14 tokenizer=tokenizer
15)
16
17response = pipe(
18 "Summarize this text: Artificial intelligence is...",
19 max_new_tokens=120
20)
21
22print(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
🔮 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.
