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

Libraries

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

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

pipe = pipeline("image-text-to-text", model="aitf-komdigi/KomdigiITS-8B-DFK-TextClassification-Final")
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-TextClassification-Final")
model = AutoModelForMultimodalLM.from_pretrained("aitf-komdigi/KomdigiITS-8B-DFK-TextClassification-Final", device_map="auto")
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-TextClassification-Final 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-TextClassification-Final"
# 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-TextClassification-Final",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

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

SGLang

How to use aitf-komdigi/KomdigiITS-8B-DFK-TextClassification-Final 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-TextClassification-Final" \
    --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-TextClassification-Final",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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-TextClassification-Final" \
        --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-TextClassification-Final",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Unsloth Studio

How to use aitf-komdigi/KomdigiITS-8B-DFK-TextClassification-Final 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-TextClassification-Final 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-TextClassification-Final 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-TextClassification-Final 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-TextClassification-Final",
    max_seq_length=2048,
)

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

hnuka commited on Jun 29

Commit

fa33afc

verified ·

1 Parent(s): 8f7c7d6

Update README

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README.md +47 -0

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@@ -13,6 +13,53 @@ datasets:
 - Brodip/datasetSFT_tlr
 ---
 # Uploaded  model
 - **Developed by:** hnuka

 - Brodip/datasetSFT_tlr
 ---
+## Dataset Details
+### Tasks & Capabilities
+* **Text Classification DFK** (Disinformasi, Fitnah, Ujaran Kebencian)
+### Dataset Splits
+* **Train Samples:** 20,790
+* **Validation Samples:** 2,599
+* **Testing Samples:** 2,599
+### Target Labels
+1. `DISINFORMASI`
+2. `FITNAH`
+3. `UJARAN KEBENCIAN`
+4. `FAKTA`
+5. `BUKAN DFK`
+## Training Configurations
+### Supervised Fine-Tuning (SFT) Pipeline Parameters
+| Parameter | Value |
+| :--- | :--- |
+| **Max Sequence Length** | 2048 tokens |
+| **Batch Size** | 2 |
+| **Gradient Accumulation Steps** | 16 |
+| **Effective Batch Size** | 32 |
+| **Learning Rate (LR)** | 2e-4 |
+| **LR Scheduler** | Linear |
+| **Optimizer** | AdamW 8-bit |
+| **Number of Epochs** | 1 |
+| **Warmup Steps** | 20 |
+| **Weight Decay** | 0.01 |
+| **Max Gradient Norm** | 1.0 |
+| **Evaluation Steps** | Every 200 steps |
+### LoRA (PEFT) Hyperparameters
+| Parameter | Value |
+| :--- | :--- |
+| **r (Rank)** | 16 |
+| **lora_alpha** | 32 |
+| **lora_dropout** | 0 |
+| **bias** | none |
+| **use_rslora** | False |
+| **Gradient Checkpointing** | unsloth |
+| **Target Modules** | `q_proj`, `k_proj`, `v_proj`, `o_proj`, `gate_proj`, `down_proj`, `up_proj` |
 # Uploaded  model
 - **Developed by:** hnuka