DreamhubAI/Nova-e-mini
164
1---2base_model: google/gemma-3-1b-it3tags:4- text-generation5- finetune6- transformers7- unsloth8- gemma39- wall-e10- lightweight11- mobile-friendly12- local-ai13- multilingual14- coding-assistant15- text-summarization16license: apache-2.017language:18- en19- fa20- de21library_name: transformers22pipeline_tag: text-generation23---24 25[](https://huggingface.co/spaces/sinamsv0/WALL-E-DEMO)26[](https://github.com/unknownmsv/WALL-E)27[](LICENSE)28 29# 🤖 WALL•E — Lightweight Local AI Assistant (1B)30 31**WALL•E** is a fine-tuned, lightweight language model based on **Gemma 3 1B**, designed for **local, privacy-preserving AI usage**. 32It focuses on *practical tasks*, *fast responses*, and *real-world utility* rather than model size.33 34---35 36## 🎯 Why WALL•E?37 38Most modern AI models are either:39- Too large to run locally, or40- Too generic for everyday tasks41 42**WALL•E** is built to fill that gap.43 44✅ Runs entirely locally 45✅ No API keys or cloud services 46✅ Designed for low-resource environments 47✅ Open-source and transparent 48 49---50 51## ✨ Key Capabilities52 53### 🌐 Multilingual Support54- **English** – primary interaction language55- **فارسی (Persian)** – natural and fluent responses56- **Deutsch (German)** – conversational support57 58### 🛠 Practical Task Focus59- 📝 Text summarization (articles, notes, reports)60- 💻 Coding help (Python, JavaScript, Bash, shell)61- 🖥 Linux command explanations & troubleshooting62- 📚 Short factual answers and guidance63 64The model is optimized to handle **short and minimal prompts** naturally (e.g. *"Hi"*, *"Explain ls -la"*), avoiding over-generation.65 66---67 68## ⚙️ Technical Overview69 70| Component | Details |71|------------------|--------|72| Base Model | Google Gemma 3 1B |73| Fine-tuning | Supervised Fine-Tuning (SFT) |74| Framework | Unsloth |75| Context Length | 3200 tokens |76| Precision | BF16 |77| License | Apache 2.0 |78 79---80 81## 🚀 Quick Start (Transformers)82 83```python84from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline85 86model_id = "sinamsv0/WALL-E"87 88tokenizer = AutoTokenizer.from_pretrained(model_id)89model = AutoModelForCausalLM.from_pretrained(90 model_id,91 device_map="auto"92)93 94pipe = pipeline(95 "text-generation",96 model=model,97 tokenizer=tokenizer98)99 100response = pipe(101 "Summarize this text: Artificial intelligence is...",102 max_new_tokens=120103)104 105print(response[0]["generated_text"])106```107 108 109 110## 🧪 Training Summary111 112 113Method: Supervised Fine-Tuning (SFT)114 115Data: Custom multilingual datasets with safety-focused filtering116 117Hardware: Single consumer GPU118 119Goal: Improve instruction-following, multilingual responses, and short-prompt behavior120 121 122 123 124 125## 🛡 Safety & Limitations126 127- ✅ Trained with safety-aware data128- ✅ Avoids harmful or unethical requests129- ⚠️ Limited reasoning depth due to 1B parameter size130- ⚠️ Not intended for complex multi-step reasoning or creative writing131 132 133 134## 🌍 Ideal Use Cases135 136 137Local coding assistant138 139Study and document summarization140 141Privacy-focused users142 143Lightweight edge deployments144 145Research and experimentation with small LLMs146 147 148 149 150 151## 🤝 Community & Links152 153 154GitHub: https://github.com/unknownmsv/WALL-E155 156Hugging Face Model: https://huggingface.co/sinamsv0/WALL-E157 158Hugging Face Space: https://huggingface.co/spaces/sinamsv0/WALL-E-DEMO159 160 161 162 163 164## 🔮 Roadmap (Planned)165 166 167UI tools for local use168 169Optional voice interface170 171Extended language support172 173Performance benchmarking on edge devices174 175 176 177 178 179 180Small model, focused design.181WALL•E proves that useful AI doesn’t have to be huge.