Instructions to use aerodynamics21/StudyPal-LLM-1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use aerodynamics21/StudyPal-LLM-1.0 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="aerodynamics21/StudyPal-LLM-1.0")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("aerodynamics21/StudyPal-LLM-1.0")
model = AutoModelForCausalLM.from_pretrained("aerodynamics21/StudyPal-LLM-1.0")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use aerodynamics21/StudyPal-LLM-1.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "aerodynamics21/StudyPal-LLM-1.0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "aerodynamics21/StudyPal-LLM-1.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/aerodynamics21/StudyPal-LLM-1.0

SGLang

How to use aerodynamics21/StudyPal-LLM-1.0 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "aerodynamics21/StudyPal-LLM-1.0" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "aerodynamics21/StudyPal-LLM-1.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "aerodynamics21/StudyPal-LLM-1.0" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "aerodynamics21/StudyPal-LLM-1.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use aerodynamics21/StudyPal-LLM-1.0 with Docker Model Runner:
```
docker model run hf.co/aerodynamics21/StudyPal-LLM-1.0
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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

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://huggingface.co/static-proxy/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.

Downloads last month: 6

Safetensors

Model size

33B params

Tensor type

F32

Model tree for aerodynamics21/StudyPal-LLM-1.0

Base model

Qwen/Qwen2.5-32B

Finetuned

(121)

this model

Quantizations

2 models

aerodynamics21
/

StudyPal-LLM-1.0