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
```

Bakat-8B-Base

Model Details

Model Description

Bakat-8B-Base adalah base model bahasa Indonesia yang dirancang untuk Continued Pre-Training (CPT) pada domain kebijakan dan pengawasan ruang digital. Model ini dibangun di atas arsitektur Qwen3-8B, dengan pendekatan LoRA (Low-Rank Adaptation) dan 4-bit quantization untuk efisiensi memori dan komputasi.

Developed by: Tim 1 AITF
Model type: Causal Language Model (LoRA Adapter)
Base architecture: Qwen3-8B
Primary language: Indonesian (id)
License: Apache-2.0

Training Data Composition

Kategori	Elemen	Jumlah Token (M)	Persentase
DTP	Okupasi PON TIK, Tren Pekerjaan, Kompetensi & SDM, Kebijakan & Regulasi DTP, Teknologi Digital Talent	94	43.9%
PRD	Judi Online, Hoax, Perlindungan Anak, Konten Edukasi, Kebijakan & Regulasi PRD, Kekerasan Masyarakat	92	42.9%
Wikipedia ID	Pengetahuan Umum Berbahasa Indonesia	28.2	13.2%
Total	–	214.2	100%

Intended Use

Direct Use (Recommended)

Model ini ditujukan untuk Continued Pre-Training, khususnya untuk:

Adaptasi domain kebijakan publik dan regulasi digital
Pengayaan pengetahuan spesifik Indonesia
Pre-adaptation sebelum Instruction Tuning atau SFT

Out-of-Scope Use

Long-context conversations (belum dioptimalkan)
High-stakes decision making (legal, medis, finansial)
Chat-oriented instruction following tanpa fine-tuning lanjutan

Bias, Risks, and Limitations

Dataset didominasi oleh domain kebijakan dan pengawasan ruang digital, sehingga bias topikal dapat muncul pada domain non-terkait.
Model belum melalui tahap preference alignment (RLHF/DPO).
Konten Wikipedia digunakan sebagai penyeimbang, namun tidak menjamin netralitas penuh.

Pengguna disarankan melakukan evaluasi tambahan sebelum penggunaan produksi.

Recommendations

Gunakan Qwen3 chat template untuk hasil generasi terbaik.
Lakukan Instruction Fine-Tuning atau Preference Tuning sebelum deployment ke end-user.
Verifikasi keluaran model untuk informasi kritikal.

How to Get Started

Load the model using HuggingFace Transformers:

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# 1. Configuration
model_id = "aitfindonesia/Bakat-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 Data

Total size: ~214M tokens
Domains: Digital Talent Policy (DTP), Pengawasan Ruang Digital (PRD), Wikipedia Indonesia
Split: Train (90%) / Validation (10%)

Training Procedure

Model dilatih menggunakan Continued Pre-Training (CPT) dengan LoRA pada HuggingFace Transformers.

Hyperparameters

Precision: bf16 (mixed precision)
Quantization: 4-bit (nf4)
LoRA Rank (r): 8
LoRA Alpha: 16
Target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Batch size: 4 / device
Gradient accumulation: 16 (effective batch size = 32)
Learning rate: 2e-4 (linear schedule)
Warmup ratio: 0.03
Epochs: 1
Optimizer: adamw_8bit