Instructions to use aitf-komdigi/KomdigiITS-8B-DFK-MultimodalClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries
PEFT
How to use aitf-komdigi/KomdigiITS-8B-DFK-MultimodalClassification with PEFT:
```
Task type is invalid.
```

How to use aitf-komdigi/KomdigiITS-8B-DFK-MultimodalClassification with Transformers:

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

pipe = pipeline("text-generation", model="aitf-komdigi/KomdigiITS-8B-DFK-MultimodalClassification")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("aitf-komdigi/KomdigiITS-8B-DFK-MultimodalClassification")
model = AutoModelForMultimodalLM.from_pretrained("aitf-komdigi/KomdigiITS-8B-DFK-MultimodalClassification")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use aitf-komdigi/KomdigiITS-8B-DFK-MultimodalClassification with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "aitf-komdigi/KomdigiITS-8B-DFK-MultimodalClassification"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "aitf-komdigi/KomdigiITS-8B-DFK-MultimodalClassification",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/aitf-komdigi/KomdigiITS-8B-DFK-MultimodalClassification

SGLang

How to use aitf-komdigi/KomdigiITS-8B-DFK-MultimodalClassification 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 "aitf-komdigi/KomdigiITS-8B-DFK-MultimodalClassification" \
    --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": "aitf-komdigi/KomdigiITS-8B-DFK-MultimodalClassification",
		"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 "aitf-komdigi/KomdigiITS-8B-DFK-MultimodalClassification" \
        --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": "aitf-komdigi/KomdigiITS-8B-DFK-MultimodalClassification",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use aitf-komdigi/KomdigiITS-8B-DFK-MultimodalClassification 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 aitf-komdigi/KomdigiITS-8B-DFK-MultimodalClassification 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 aitf-komdigi/KomdigiITS-8B-DFK-MultimodalClassification to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for aitf-komdigi/KomdigiITS-8B-DFK-MultimodalClassification to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="aitf-komdigi/KomdigiITS-8B-DFK-MultimodalClassification",
    max_seq_length=2048,
)

Docker Model Runner
How to use aitf-komdigi/KomdigiITS-8B-DFK-MultimodalClassification with Docker Model Runner:
```
docker model run hf.co/aitf-komdigi/KomdigiITS-8B-DFK-MultimodalClassification
```

KomdigiITS-8B-DFK-MultimodalClassification

File size: 1,116 Bytes

5b65e7f

{
  "cls_accuracy": 0.9426699426699426,
  "cls_precision_macro": 0.9207548385640022,
  "cls_recall_macro": 0.9124577987183564,
  "cls_f1_macro": 0.9163200926370609,
  "cls_precision_weighted": 0.9430469381412777,
  "cls_recall_weighted": 0.9426699426699426,
  "cls_f1_weighted": 0.9425727615998175,
  "cls_disinformasi_precision": 0.9460916442048517,
  "cls_disinformasi_recall": 0.8954081632653061,
  "cls_disinformasi_f1": 0.9200524246395806,
  "cls_disinformasi_support": 392,
  "cls_fitnah_precision": 0.8215962441314554,
  "cls_fitnah_recall": 0.8215962441314554,
  "cls_fitnah_f1": 0.8215962441314554,
  "cls_fitnah_support": 213,
  "cls_netral_precision": 0.9365079365079365,
  "cls_netral_recall": 0.9731958762886598,
  "cls_netral_f1": 0.9544994944388271,
  "cls_netral_support": 970,
  "cls_ujaran_kebencian_precision": 0.9788235294117648,
  "cls_ujaran_kebencian_recall": 0.9596309111880046,
  "cls_ujaran_kebencian_f1": 0.9691322073383809,
  "cls_ujaran_kebencian_support": 867,
  "bertscore_precision": 0.8043486475944519,
  "bertscore_recall": 0.8009513020515442,
  "bertscore_f1": 0.8023340702056885
}