klei1/bleta-logjike-27b-gguf

Bleta-Logjike 27B Albanian Logical Reasoning Model (GGUF)

Model Description

• Developed by: klei aliaj
• Model type: Bleta-Logjike 27B optimized for Albanian logical reasoning
• License: apache-2.0
• Format: GGUF 8-bit quantized for llama.cpp
• Language: Albanian
• Base architecture: Based on Gemma 3 27B

This model is a GGUF quantized version of the Bleta-Logjike 27B model, specifically optimized for logical reasoning tasks in the Albanian language. Bleta is an Albanian adaptation based on Google's Gemma 3 architecture, with this version focused on enhancing logical reasoning and problem-solving capabilities.

Capabilities & Features

Logical Reasoning Focus

This Albanian language model excels at:

1. Logical analysis and deduction in Albanian
2. Step-by-step problem solving
3. Structured reasoning for complex problems
4. Understanding logical relationships and dependencies
5. Mathematical reasoning for grade-school level problems

GGUF Quantization Benefits

• Efficient inference: Optimized for use with llama.cpp and similar frameworks
• Reduced memory usage: 8-bit quantization substantially reduces RAM requirements
• Faster inference: More efficient processing for consumer hardware
• Compatible with: llama.cpp, Jan AI, LM Studio, and other GGUF-compatible applications

Albanian Language Optimization

• Native support for Albanian grammar and vocabulary
• Understanding of Albanian cultural context
• Handling of Albanian-specific logical expressions and constructs

Training Methodology

GRPO Approach

This model was fine-tuned using Generative Rejection Policy Optimization (GRPO), a reinforcement learning technique that trains models to optimize for specific reward functions. GRPO allows the model to learn from feedback on its generated responses, improving reasoning quality over time by:

1. Generating multiple candidate responses
2. Evaluating responses against specific reward criteria
3. Learning to prefer high-quality reasoning patterns
4. Optimizing for step-by-step problem solving

GSM8K Dataset

The training utilized the GSM8K (Grade School Math 8K) dataset, which contains over 8,000 high-quality grade school math problems, requiring step-by-step reasoning to solve. The dataset provides:

• Diverse mathematical problem types
• Multi-step reasoning challenges
• Clear step-by-step solutions
• Grade-school level complexity

This dataset was adapted for Albanian language training to ensure the model can handle mathematical reasoning tasks in Albanian.

Technical Specifications

Model Architecture

• 27B parameters
• Based on Gemma 3 architecture with Albanian adaptations
• 128K context window
• QK normalization
• 5 sliding + 1 global attention pattern
• 1024 sliding window attention

Usage Requirements

• Recommended minimum 16GB RAM for inference
• Compatible with CPU inference but GPU recommended
• Works with llama.cpp and compatible UIs

Limitations

The current model is an 8-bit quantized version of the 27B parameter model. It can run on much lower specifications, but at the cost of some performance.

Acknowledgments

• Google for developing the Gemma 3 architecture
• llama.cpp team for the GGUF format and inference engine
• OpenAI for the GSM8K dataset
• Hugging Face for their TRL library and GRPO implementation