Instructions to use DimasMP3/Qwen2.5-Exam-GenAI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DimasMP3/Qwen2.5-Exam-GenAI with Transformers:

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

pipe = pipeline("text-generation", model="DimasMP3/Qwen2.5-Exam-GenAI")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("DimasMP3/Qwen2.5-Exam-GenAI")
model = AutoModelForCausalLM.from_pretrained("DimasMP3/Qwen2.5-Exam-GenAI")
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 DimasMP3/Qwen2.5-Exam-GenAI with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DimasMP3/Qwen2.5-Exam-GenAI"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DimasMP3/Qwen2.5-Exam-GenAI",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/DimasMP3/Qwen2.5-Exam-GenAI

SGLang

How to use DimasMP3/Qwen2.5-Exam-GenAI 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 "DimasMP3/Qwen2.5-Exam-GenAI" \
    --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": "DimasMP3/Qwen2.5-Exam-GenAI",
		"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 "DimasMP3/Qwen2.5-Exam-GenAI" \
        --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": "DimasMP3/Qwen2.5-Exam-GenAI",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use DimasMP3/Qwen2.5-Exam-GenAI with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for DimasMP3/Qwen2.5-Exam-GenAI to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for DimasMP3/Qwen2.5-Exam-GenAI to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for DimasMP3/Qwen2.5-Exam-GenAI to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="DimasMP3/Qwen2.5-Exam-GenAI",
    max_seq_length=2048,
)

Docker Model Runner
How to use DimasMP3/Qwen2.5-Exam-GenAI with Docker Model Runner:
```
docker model run hf.co/DimasMP3/Qwen2.5-Exam-GenAI
```

datasets: - DimasMP3/Indo-Elementary-School-Exams (custom) metrics: - eval_loss: 0.6869

🎓 Qwen2.5-7B-Indo-Exam-Generator-16bit

Developed by: Dimas Maulana Putra (DimasMP3)
Model Type: Specialized Fine-Tuned for Indonesian Elementary Education
Training Status: Optimal @ Step 500 (Eval Loss: 0.686)

🌟 Overview

Qwen2.5-Indo-Exam-Generator adalah model bahasa yang telah di-finetuning secara khusus untuk menjawab tantangan dunia pendidikan di Indonesia. Model ini dilatih menggunakan dataset berkualitas tinggi sebanyak 4.500+ soal sekolah dasar yang disesuaikan dengan Kurikulum Merdeka.

Model ini bukan sekadar AI umum; ia dirancang untuk berperan sebagai Guru Digital yang mampu menghasilkan soal ujian (pilihan ganda) lengkap dengan kunci jawaban dan pembahasan yang akurat.

✨ Key Features

🇮🇩 Native Indonesian Support: Memahami istilah pendidikan lokal (IPAS, HOTS, Kurikulum Merdeka).
📝 Structured Output: Konsisten dalam menghasilkan format Soal, Opsi (A-D), Kunci, dan Pembahasan.
🧠 RAG Ready: Dioptimalkan untuk bekerja dengan sistem Retrieval-Augmented Generation (pgvector/Drizzle).
🚀 High Precision: Fine-tuned dalam format 16-bit untuk akurasi logika yang tajam.

📊 Training Results (WandB Metrics)

Training dilakukan dengan pengawasan ketat terhadap Validation Loss untuk mencegah halusinasi:

Metric	Value
Best Step	500
Validation Loss	0.6869
Training Loss	0.3204
Epoch	2.43

Note: Berhenti otomatis via Early Stopping di Step 650 untuk memastikan bobot terbaik (Step 500) yang tersimpan.

🛠️ Tech Stack

Model ini lahir dari perpaduan teknologi mutakhir:

Base Model: unsloth/Qwen2.5-7B-Instruct
Fine-tuning Tool: Unsloth (2x faster training)
Framework: Huggingface TRL & Transformers
Optimization: LoRA (Rank 128)

📖 Cara Penggunaan (Inference)

from unsloth import FastLanguageModel
import torch

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "DimasMP3/Qwen2.5-7B-Indo-Exam-Generator-16bit",
    max_seq_length = 2048,
    load_in_4bit = True,
)

prompt = """<|im_start|>system
Anda adalah Guru SD yang ahli. Buatlah soal pilihan ganda berdasarkan konteks materi ini.<|im_end|>
<|im_start|>user
Topik: Ekosistem Laut
Konteks: Terumbu karang adalah tempat tinggal ikan.<|im_end|>
<|im_start|>assistant
"""

# Generate Output...

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Safetensors

Model size

8B params

Tensor type

BF16

Model tree for DimasMP3/Qwen2.5-Exam-GenAI

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