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README.md
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library_name: transformers
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tags:
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- education
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---
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library_name: transformers
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tags:
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- education
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- croatian
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- qwen2
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- fine-tuned
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- study-assistant
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language:
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- hr
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- en
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license: apache-2.0
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base_model: Qwen/Qwen2.5-32B-Instruct
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---
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# StudyPal-LLM-1.0
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A fine-tuned Croatian educational assistant based on Qwen2.5-32B-Instruct, designed to help students with learning and study materials.
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## Model Details
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### Model Description
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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
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providing learning assistance.
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- **Developed by:** aerodynamics21
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- **Model type:** Causal Language Model
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- **Language(s):** Croatian (primary), English (secondary)
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- **License:** Apache 2.0
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- **Finetuned from model:** Qwen/Qwen2.5-32B-Instruct
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- **Parameters:** 32.8B
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### Model Sources
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- **Repository:** https://huggingface.co/aerodynamics21/StudyPal-LLM-1.0
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- **Base Model:** https://huggingface.co/Qwen/Qwen2.5-32B-Instruct
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- **Adapter:** https://huggingface.co/aerodynamics21/StudyPal-LLM-1
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## Uses
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### Direct Use
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This model is designed for educational applications:
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- Generating study materials in Croatian
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- Creating flashcards and quiz questions
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- Providing explanations of complex topics
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- Assisting with homework and learning
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### Usage Examples
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained("aerodynamics21/StudyPal-LLM-1.0")
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tokenizer = AutoTokenizer.from_pretrained("aerodynamics21/StudyPal-LLM-1.0")
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# Generate educational content
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prompt = "Objasni koncept fotosinteze:"
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inputs = tokenizer(prompt, return_tensors="pt")
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outputs = model.generate(**inputs, max_length=200, temperature=0.7)
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response = tokenizer.decode(outputs[0], skip_special_tokens=True)
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API Usage
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import requests
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API_URL = "https://api-inference.huggingface.co/models/aerodynamics21/StudyPal-LLM-1.0"
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headers = {"Authorization": f"Bearer {your_token}"}
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def query(payload):
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response = requests.post(API_URL, headers=headers, json=payload)
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return response.json()
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output = query({"inputs": "Stvori kviz o hrvatskoj povijesti:"})
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Training Details
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Training Data
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The model was fine-tuned on a Croatian educational dataset containing:
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- Educational conversations and Q&A pairs
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- Flashcard datasets
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- Quiz and summary materials
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- Croatian academic content
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Training Procedure
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- Base Model: Qwen2.5-32B-Instruct
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- Training Method: LoRA (Low-Rank Adaptation)
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- Training Framework: Transformers + PEFT
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- Hardware: RunPod GPU instance
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Evaluation
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The model demonstrates strong performance in:
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- Croatian language comprehension and generation
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- Educational content creation
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- Study material generation
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- Academic question answering
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Bias, Risks, and Limitations
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- Primary focus on Croatian educational content
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- May reflect biases present in training data
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- Best suited for educational contexts
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- Performance may vary on non-educational tasks
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Citation
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@model{studypal-llm-1.0,
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title={StudyPal-LLM-1.0: A Croatian Educational Assistant},
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author={aerodynamics21},
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year={2024},
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url={https://huggingface.co/aerodynamics21/StudyPal-LLM-1.0}
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}
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Model Card Authors
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aerodynamics21
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Model Card Contact
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For questions about this model, please visit the repository or create an issue.
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**To update your model card:**
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1. **Go to your model page:** https://huggingface.co/aerodynamics21/StudyPal-LLM-1.0
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2. **Click "Edit model card"**
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3. **Replace the content with the above**
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4. **Save changes**
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**Note:** The adapter (StudyPal-LLM-1) is already merged into this model, so users don't need to load it separately. The adapter is mentioned in the model sources for reference.
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> YAML Metadata Warning: empty or missing yaml metadata in repo card (https://huggingface.co/docs/hub/model-cards#model-card-metadata)
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● The YAML metadata section is missing the closing ---. Here's the corrected model card:
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---
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library_name: transformers
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tags:
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- education
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