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

Libraries
PEFT
How to use aitf-komdigi/KomdigiUB-8B-Base with PEFT:
```
Task type is invalid.
```

How to use aitf-komdigi/KomdigiUB-8B-Base with Transformers:

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

pipe = pipeline("text-generation", model="aitf-komdigi/KomdigiUB-8B-Base")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("aitf-komdigi/KomdigiUB-8B-Base")
model = AutoModelForCausalLM.from_pretrained("aitf-komdigi/KomdigiUB-8B-Base")
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 aitf-komdigi/KomdigiUB-8B-Base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "aitf-komdigi/KomdigiUB-8B-Base"
# 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/KomdigiUB-8B-Base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/aitf-komdigi/KomdigiUB-8B-Base

SGLang

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

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

KomdigiUB-8B-Base / README.md

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Bakti-8B-Base

library_name: transformers
base_model: Qwen/Qwen3-8B
tags: qwen, qwen3, causal-lm, continued-pretraining, indonesian, id, prd, dtp
license: apache-2.0
language: id, en

📌 Overview

Bakti-8B-Base is an 8-billion-parameter Large Language Model (LLM) adapted specifically for Indonesia's strategic focus areas:

Perlindungan Ruang Digital (PRD) – Digital Space Protection
Digital Talent Pool (DTP) – Workforce and digital capability development

This model is built through Continued Pre‑training (CPT) on the Qwen‑3‑8B base model using a curated Indonesian dataset.

🧠 Model Details

Model Description

Developed by: AITF Indonesia
Model Type: Causal Language Model (Base)
Base Model: Qwen/Qwen3-8B
Language: Indonesian (Primary), English (Secondary)
License: Apache 2.0
Training Method: Continued Pre‑training (CPT)

🎯 Goal

To create a sovereign, domain‑specialized Indonesian foundation model with strong understanding of:

Digital policies (UU PDP, UU ITE)
Digital workforce & skill landscape (DTP)

📚 Dataset Composition

Total Dataset Size: ~214.2 Million Tokens

Category	Description	Token Count (M)	Percentage
DTP	Digital HR, tech syllabi, certifications, job trends	94.0	~43.9%
PRD	Cybersecurity, PDP Law, content moderation, hoax prevention	92.0	~42.9%
Wikipedia ID	General knowledge anchor & grammar stability	28.2	~13.2%
Total	—	214.2	100%

🧩 Intended Use

As a Base Model, Bakti‑8B outputs text completions and can be adapted into chat/instruct variants.

1. PRD (Perlindungan Ruang Digital)

Policy sentiment analysis
Misinformation pattern detection
Understanding legal terminology (UU ITE, UU PDP)

2. DTP (Digital Talent Pool)

Skill gap analysis
Curriculum drafting assistance
Job description & talent understanding

🚀 How to Get Started

Load the model using HuggingFace Transformers:

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# 1. Configuration
model_id = "YOUR_USERNAME/Bakti-8B-Base"  # Replace with your actual Hub ID

# 2. Load Model
# Use bfloat16 for A100/A10G, float16 for T4
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# 3. Inference Example (Completion)
input_text = "Strategi utama untuk mengurangi gap talenta digital di Indonesia adalah"
inputs = tokenizer(input_text, return_tensors="pt").to("cuda")

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=100,
        do_sample=True,
        temperature=0.7
    )
    print(tokenizer.decode(outputs[0], skip_special_tokens=True))

⚙️ Training Details

Training Procedure

The model was continued‑pretrained with a causal language modeling (CLM) objective while preserving base reasoning capabilities.

Hardware & Environment

GPU: NVIDIA A100 80GB (Colab Pro+)
Training Duration: ~36 hours
Frameworks: PyTorch, Transformers, Accelerate

🔧 Hyperparameters (Highlights)

Sequence Length: 4096
Optimizer: AdamW
Scheduler: Cosine Decay
Precision: bf16

⚠️ Limitations

Base Model: No SFT or RLHF; few‑shot prompting may be required.
Web Data Bias: May inherit biases from Indonesian web sources.
Hallucinations: Possible incorrect factual output.

✅ Recommendations

For production use, it is recommended to:

Perform Supervised Fine‑Tuning (SFT) for PRD/DTP domains
Add high‑quality instruction datasets
Apply evaluation benchmarks before deployment