sinamsv0/WALL-E

🤖 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.

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🎯 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

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✨ 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.

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⚙️ 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

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🚀 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"])

🧪 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.