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  ---
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- base_model: unsloth/Qwen2.5-Coder-7B-Instruct
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- library_name: peft
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- pipeline_tag: text-generation
 
 
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  tags:
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- - base_model:adapter:unsloth/Qwen2.5-Coder-7B-Instruct
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- - lora
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- - sft
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- - transformers
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- - trl
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- - unsloth
 
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  ---
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- # Model Card for Model ID
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- <!-- Provide a quick summary of what the model is/does. -->
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- ## Model Details
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- ### Model Description
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-
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- <!-- Provide a longer summary of what this model is. -->
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-
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-
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-
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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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-
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- ### Model Sources [optional]
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-
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- <!-- Provide the basic links for the model. -->
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-
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- - **Repository:** [More Information Needed]
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- - **Paper [optional]:** [More Information Needed]
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- - **Demo [optional]:** [More Information Needed]
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-
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- ## Uses
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-
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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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-
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- ### Direct Use
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-
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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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-
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- [More Information Needed]
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-
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- ### Downstream Use [optional]
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-
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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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-
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- [More Information Needed]
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-
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- ### Out-of-Scope Use
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-
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- <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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-
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- [More Information Needed]
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-
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- ## Bias, Risks, and Limitations
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-
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- <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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- [More Information Needed]
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-
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- ### Recommendations
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-
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- <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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-
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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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-
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- ## How to Get Started with the Model
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-
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- Use the code below to get started with the model.
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-
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- [More Information Needed]
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-
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- ## Training Details
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-
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- ### Training Data
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- <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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- [More Information Needed]
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-
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- ### Training Procedure
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-
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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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-
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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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-
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- #### Speeds, Sizes, Times [optional]
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-
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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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-
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- ## Evaluation
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-
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- <!-- This section describes the evaluation protocols and provides the results. -->
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- ### Testing Data, Factors & Metrics
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- #### Testing Data
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- <!-- This should link to a Dataset Card if possible. -->
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- [More Information Needed]
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- #### Factors
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- <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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- [More Information Needed]
 
 
 
 
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- #### Metrics
 
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- <!-- These are the evaluation metrics being used, ideally with a description of why. -->
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- [More Information Needed]
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- ### Results
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- [More Information Needed]
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- #### Summary
 
 
 
 
 
 
 
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- ## Model Examination [optional]
 
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- <!-- Relevant interpretability work for the model goes here -->
 
 
 
 
 
 
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- [More Information Needed]
 
 
 
 
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- ## Environmental Impact
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- <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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- Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
 
 
 
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- - **Hardware Type:** [More Information Needed]
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- - **Hours used:** [More Information Needed]
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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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- ## Technical Specifications [optional]
 
 
 
 
 
 
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- ### Model Architecture and Objective
 
 
 
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- [More Information Needed]
 
 
 
 
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- ### Compute Infrastructure
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- [More Information Needed]
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- #### Hardware
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- [More Information Needed]
 
 
 
 
 
 
 
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- #### Software
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- [More Information Needed]
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- ## Citation [optional]
 
 
 
 
 
 
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- <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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- **BibTeX:**
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- [More Information Needed]
 
 
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- **APA:**
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- [More Information Needed]
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- ## Glossary [optional]
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- <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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- [More Information Needed]
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- ## More Information [optional]
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- [More Information Needed]
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- ## Model Card Authors [optional]
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- [More Information Needed]
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- ## Model Card Contact
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- [More Information Needed]
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- ### Framework versions
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- - PEFT 0.18.1
 
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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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+ [![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)
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+ [![Base Model](https://img.shields.io/badge/Base-Qwen2.5--Coder--7B-blue)](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct)
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+ [![Language](https://img.shields.io/badge/Language-Bulgarian%20🇧🇬-red)](https://huggingface.co/kyleparrratt/Vitosha-GPT-Code)
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+ [![Offline](https://img.shields.io/badge/Runs-100%25%20Offline-orange)](https://huggingface.co/kyleparrratt/Vitosha-GPT-Code)
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+ [![RAM](https://img.shields.io/badge/Target-4GB%20RAM-purple)](https://huggingface.co/kyleparrratt/Vitosha-GPT-Code)
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+ [![Status](https://img.shields.io/badge/Status-V0.1%20in%20development-yellow)](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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+ <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>