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
```

StudyPal-LLM-1.0 / README.md

aerodynamics21

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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.