Instructions to use aitf-its-tim3-dfk/KomdigiITS-0.8B-DFK-MultimodalClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries
PEFT
How to use aitf-its-tim3-dfk/KomdigiITS-0.8B-DFK-MultimodalClassification with PEFT:
```
Task type is invalid.
```

How to use aitf-its-tim3-dfk/KomdigiITS-0.8B-DFK-MultimodalClassification with Transformers:

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

pipe = pipeline("text-generation", model="aitf-its-tim3-dfk/KomdigiITS-0.8B-DFK-MultimodalClassification")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("aitf-its-tim3-dfk/KomdigiITS-0.8B-DFK-MultimodalClassification")
model = AutoModelForCausalLM.from_pretrained("aitf-its-tim3-dfk/KomdigiITS-0.8B-DFK-MultimodalClassification")
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 Settings

vLLM

How to use aitf-its-tim3-dfk/KomdigiITS-0.8B-DFK-MultimodalClassification with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "aitf-its-tim3-dfk/KomdigiITS-0.8B-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-its-tim3-dfk/KomdigiITS-0.8B-DFK-MultimodalClassification",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/aitf-its-tim3-dfk/KomdigiITS-0.8B-DFK-MultimodalClassification

SGLang

How to use aitf-its-tim3-dfk/KomdigiITS-0.8B-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-its-tim3-dfk/KomdigiITS-0.8B-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-its-tim3-dfk/KomdigiITS-0.8B-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-its-tim3-dfk/KomdigiITS-0.8B-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-its-tim3-dfk/KomdigiITS-0.8B-DFK-MultimodalClassification",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use aitf-its-tim3-dfk/KomdigiITS-0.8B-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-its-tim3-dfk/KomdigiITS-0.8B-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-its-tim3-dfk/KomdigiITS-0.8B-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-its-tim3-dfk/KomdigiITS-0.8B-DFK-MultimodalClassification to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="aitf-its-tim3-dfk/KomdigiITS-0.8B-DFK-MultimodalClassification",
    max_seq_length=2048,
)

Docker Model Runner
How to use aitf-its-tim3-dfk/KomdigiITS-0.8B-DFK-MultimodalClassification with Docker Model Runner:
```
docker model run hf.co/aitf-its-tim3-dfk/KomdigiITS-0.8B-DFK-MultimodalClassification
```

KomdigiITS-0.8B-DFK-MultimodalClassification / metrics.json

spuuntries

feat: model

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	{
	"cls_accuracy": 0.9250614250614251,
	"cls_precision_macro": 0.8824223687695829,
	"cls_recall_macro": 0.9150107778401206,
	"cls_f1_macro": 0.8932637021913903,
	"cls_precision_weighted": 0.9356606985037156,
	"cls_recall_weighted": 0.9250614250614251,
	"cls_f1_weighted": 0.9281938508621982,
	"cls_disinformasi_precision": 0.943089430894309,
	"cls_disinformasi_recall": 0.8877551020408163,
	"cls_disinformasi_f1": 0.9145860709592641,
	"cls_disinformasi_support": 392,
	"cls_fitnah_precision": 0.6508474576271186,
	"cls_fitnah_recall": 0.9014084507042254,
	"cls_fitnah_f1": 0.7559055118110236,
	"cls_fitnah_support": 213,
	"cls_netral_precision": 0.9540229885057471,
	"cls_netral_recall": 0.9412371134020618,
	"cls_netral_f1": 0.9475869226777374,
	"cls_netral_support": 970,
	"cls_ujaran_kebencian_precision": 0.9817295980511571,
	"cls_ujaran_kebencian_recall": 0.9296424452133795,
	"cls_ujaran_kebencian_f1": 0.9549763033175356,
	"cls_ujaran_kebencian_support": 867,
	"bertscore_precision": 0.7968013882637024,
	"bertscore_recall": 0.7934213280677795,
	"bertscore_f1": 0.794790506362915
	}