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---
license: apache-2.0
datasets:
- StudyPal/education
language:
- hr
- en
base_model:
- Qwen/Qwen2.5-32B
library_name: transformers
tags:
- education
- croatian
- qwen2
- fine-tuned
- study-assistant
---
# StudyPal-LLM-1.0
A fine-tuned Croatian educational assistant based on Qwen2.5-32B-Instruct, designed to help students with learning and study materials.
## Model Details
### Model Description
StudyPal-LLM-1.0 is a large language model fine-tuned specifically for educational purposes in Croatian. The model excels at generating educational content, answering study questions, creating flashcards, and
providing learning assistance.
- **Developed by:** aerodynamics21
- **Model type:** Causal Language Model
- **Language(s):** Croatian (primary), English (secondary)
- **License:** Apache 2.0
- **Finetuned from model:** Qwen/Qwen2.5-32B
- **Parameters:** 32.8B
### Model Sources
- **Repository:** https://huggingface.co/aerodynamics21/StudyPal-LLM-1.0
- **Base Model:** https://huggingface.co/Qwen/Qwen2.5-32B
- **Adapter:** https://huggingface.co/aerodynamics21/StudyPal-LLM-1
## Uses
### Direct Use
This model is designed for educational applications:
- Generating study materials in Croatian
- Creating flashcards and quiz questions
- Providing explanations of complex topics
- Assisting with homework and learning
### Usage Examples
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("aerodynamics21/StudyPal-LLM-1.0")
tokenizer = AutoTokenizer.from_pretrained("aerodynamics21/StudyPal-LLM-1.0")
# Generate educational content
prompt = "Objasni koncept fotosinteze:"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200, temperature=0.7)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
API Usage
import requests
API_URL = "https://api-inference.huggingface.co/models/aerodynamics21/StudyPal-LLM-1.0"
headers = {"Authorization": f"Bearer {your_token}"}
def query(payload):
response = requests.post(API_URL, headers=headers, json=payload)
return response.json()
output = query({"inputs": "Stvori kviz o hrvatskoj povijesti:"})
Training Details
Training Data
The model was fine-tuned on a Croatian educational dataset containing:
- Educational conversations and Q&A pairs
- Flashcard datasets
- Quiz and summary materials
- Croatian academic content
Training Procedure
- Base Model: Qwen2.5-32B
- Training Method: LoRA (Low-Rank Adaptation)
- Training Framework: Transformers + PEFT
- Hardware: RunPod GPU instance
Evaluation
The model demonstrates strong performance in:
- Croatian language comprehension and generation
- Educational content creation
- Study material generation
- Academic question answering
Bias, Risks, and Limitations
- Primary focus on Croatian educational content
- May reflect biases present in training data
- Best suited for educational contexts
- Performance may vary on non-educational tasks
Citation
@model{studypal-llm-1.0,
title={StudyPal-LLM-1.0: A Croatian Educational Assistant},
author={aerodynamics21},
year={2025},
url={https://huggingface.co/aerodynamics21/StudyPal-LLM-1.0}
}
Model Card Authors
aerodynamics21
Model Card Contact
For questions about this model, please visit the repository or create an issue. |