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tags:
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<!-- Provide a longer summary of what this model is. -->
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- **Developed by:** [More Information Needed]
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- **Funded by [optional]:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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### Model Sources [optional]
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- **Repository:** [More Information Needed]
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- **Paper [optional]:** [More Information Needed]
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## Uses
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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### Direct Use
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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[More Information Needed]
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### Downstream Use [optional]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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[More Information Needed]
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### Out-of-Scope Use
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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[More Information Needed]
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## Bias, Risks, and Limitations
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[More Information Needed]
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### Recommendations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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## How to Get Started with the Model
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Use the code below to get started with the model.
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[More Information Needed]
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## Training Details
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### Training Data
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[More Information Needed]
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### Training Procedure
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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#### Preprocessing [optional]
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[More Information Needed]
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#### Training Hyperparameters
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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#### Speeds, Sizes, Times [optional]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
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## Evaluation
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<!-- This section describes the evaluation protocols and provides the results. -->
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##
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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##
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### Framework versions
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---
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language:
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- bg
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- en
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license: mit
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base_model: Qwen/Qwen2.5-Coder-7B-Instruct
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tags:
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- code
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- bulgarian
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- lora
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- peft
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- vitosha-gpt-code
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- slm
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- offline
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---
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<div align="center">
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# 🏔️ Vitosha-GPT-Code
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### *The coding assistant that speaks Bulgarian — and runs anywhere.*
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[](https://opensource.org/licenses/MIT)
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[](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct)
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[](https://huggingface.co/kyleparrratt/Vitosha-GPT-Code)
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[](https://huggingface.co/kyleparrratt/Vitosha-GPT-Code)
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[](https://huggingface.co/kyleparrratt/Vitosha-GPT-Code)
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[](https://huggingface.co/kyleparrratt/Vitosha-GPT-Code)
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</div>
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---
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## 🇧🇬 Why This Exists
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> *"Every Bulgarian has the right to AI."*
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Right now, AI is a luxury. You need fast internet. You need expensive hardware. You need a subscription. If you're in a remote province on a 10-year-old PC, the 21st century is locked behind a paywall.
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**That's wrong. And Vitosha-GPT-Code is the answer.**
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---
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## 🌍 The Problem We're Solving
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```
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❌ Remote area? → No cloud access
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❌ Old PC / low RAM? → Runs too slow
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❌ No fiber optic? → Can't stream tokens
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❌ No credit card? → Locked out of ChatGPT
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✅ Vitosha-GPT-Code → Works offline. 4GB RAM. Free. Forever.
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```
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A kid in a remote Bulgarian province deserves the **same coding tools** as a developer in Sofia. Whether it's building a website for the family business or learning to program for the first time — **hardware should never be a barrier to entry**.
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---
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## 🛠️ What It Does
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Vitosha-GPT-Code is a **Bulgarian-first coding assistant** — it writes code, explains concepts, and answers technical questions **in Bulgarian by default**.
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| Capability | Example |
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|---|---|
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| 🐍 Write Python functions | "Напиши функция за проверка на просто число" |
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| 🌐 Build web projects | "Направи уебсайт за малък бизнес" |
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| 🔁 Multi-turn coding chat | Remembers context across follow-up questions |
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| 📖 Explain algorithms | "Обясни как работи binary search" |
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| 🗃️ SQL queries | "Извлечи всички потребители от таблица users" |
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| 🔧 Debug code | Spot errors and suggest fixes in Bulgarian |
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### 🗣️ Real Example Output
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```
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Потребител: Напиши функция на Python, която проверява дали число е просто.
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Виtoша: Ето една проста функция на Python, която проверява дали даденото
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число е просто:
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def is_prime(n):
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if n <= 1:
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return False
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for i in range(2, int(n**0.5) + 1):
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if n % i == 0:
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return False
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return True
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Тази функция работи така: ако числото е по-малко или равно на 1, не е
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просто. За всички останали — проверяваме всеки делител до корена на
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числото...
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```
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✅ **Correct code. Bulgarian explanation. Zero internet. Zero cost.**
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---
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## ⚡ Quick Start
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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from peft import PeftModel
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import torch
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base = "Qwen/Qwen2.5-Coder-7B-Instruct"
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adapter = "kyleparrratt/Vitosha-GPT-Code"
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tokenizer = AutoTokenizer.from_pretrained(base, trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained(
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base,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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)
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model = PeftModel.from_pretrained(model, adapter, is_trainable=False)
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messages = [
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{"role": "system", "content": "Ти си полезен асистент за програмиране. Отговаряш на български."},
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{"role": "user", "content": "Напиши функция на Python за проверка на просто число и обясни на български."},
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]
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text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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inputs = tokenizer(text, return_tensors="pt").to(model.device)
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out = model.generate(**inputs, max_new_tokens=512, do_sample=False, pad_token_id=tokenizer.eos_token_id)
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print(tokenizer.decode(out[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))
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```
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---
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## 🔬 How It Was Built
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This isn't just a wrapper with a Bulgarian flag slapped on it. It's purpose-trained from the ground up to think and respond in Bulgarian.
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| Component | Detail |
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|---|---|
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| 🧠 **Base Model** | Qwen2.5-Coder-7B-Instruct — one of the strongest open code models available |
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| 📚 **Training Data** | 5,000 Bulgarian coding examples from evol-codealpaca-v1 |
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| 🌍 **Translation** | OPUS-MT (opus-mt-tc-big-en-bg) — dedicated EN→BG model, GPU-accelerated |
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| ⚙️ **Fine-tuning** | LoRA (r=16) with Unsloth, efficient parameter training |
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| 🚫 **No tricks** | No prompt poisoning, no phrase-stuffing — clean Bulgarian training targets |
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| 🔒 **Privacy** | Designed for 100% local inference — your data never leaves your machine |
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---
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## 🗺️ Roadmap
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```
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[✅] V0.1 — LoRA adapter: Bulgarian code explanations & generation
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[ ] V0.2 — 5,000-sample OPUS-translated training run + re-train
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[ ] V0.3 — GGUF export: run with llama.cpp on 4GB RAM
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[ ] V0.4 — Windows installer for offline use with no tech knowledge
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[ ] V1.0 — Full offline Bulgarian coding assistant for every Bulgarian
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```
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---
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## ⚠️ Current Limitations
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- Occasional slip into English on complex explanations. Add *"Отговори на български."* to the user prompt to stay consistent.
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- Code identifiers, API names, and variable names remain in English (as they should).
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- V0.1 is trained on 400 samples; V0.2 will use 5,000.
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---
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## 🏔️ The Name
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**Vitosha** is the mountain that watches over Sofia — visible from the capital, unchanging, accessible to everyone. It doesn't care if you're a professor or a student, if you have a new laptop or an old one. You can walk up Vitosha for free.
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That's what this model is.
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---
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## 📄 License
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MIT. Free to use, modify, and deploy. Kept free on purpose.
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+
---
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| 176 |
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<div align="center">
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**Built solo. Kept free. For every Bulgarian. 🇧🇬**
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*"От Витоша, за всички."*
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</div>
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