aitfindonesia
/

KomdigiUB-8B-Instruct-PRD2

@@ -1,199 +1,182 @@
 ---
-library_name: transformers
-tags: []
----
-# Model Card for Model ID
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 ---
+base_model: aitfindonesia/KomdigiUB-8B-Base
+library_name: peft
+pipeline_tag: text-generation
+anguage:
+  - id
+tags:
+  - base_model:Qwen/Qwen3-8B
+  - lora
+  - sft
+  - transformers
+  - trl
+  - lm-eval
+  - bakat
+  - indonesian
+license: apache-2.0
+datasets:
+  - internal-curated
+---
+# Bakat-8B-Base
 ## Model Details
 ### Model Description
+**KomdigiUB-8B-Instruct-PRD2** adalah instruct tuned model LLM bahasa Indonesia yang dirancang untuk **Supervised Fine Tuning (CPT)** dan ** Alignment ** pada domain kebijakan dan pengawasan ruang digital. Model ini difine tune dari model dasar **KomdigiUB-8B-Base**, dengan pendekatan **LoRA (Low-Rank Adaptation)** dan untuk efisiensi pemilihan parameter yang dipelajari ulang.
+* **Developed by**: Tim 2 PRD 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 & Bahasa Daerah Seluruh Indonesia                                                    | 28.2             | 13.2%      |
+| **Total**        | –                                                                                                     | **214.2**        | **100%**   |
+---
+## Intended Use
+### Direct Use (Recommended)
+Model ini **ditujukan untuk Instruct Fine Tuning**, 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**:
+```python
+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
+---
 ## Evaluation
 ### Results
+* **Final Training Loss**: ~1.2685
+* **Final Validation Loss**: ~1.264
+* **Training Perplexity**: ~3.56
+* **Validation Perplexity**: ~3.55
+### Benchmark (General)
+* **MMLU**: ~74.20
+* **IndoMMLU**: ~65.66
+* **XCOPA-ID**: ~75.80
+---
 ## Environmental Impact
+Estimasi emisi karbon mengikuti metodologi Lacoste et al. (2019).
+* **Hardware**: NVIDIA A100 80GB
+* **Training time**: ~36 jam
+* **Compute region**: Indonesia
+* **Infrastructure**: University / Private Server
+---
+## Framework Versions
+* Transformers: 4.x
+* PyTorch: 2.x
+* Datasets: 2.x
+* Tokenizers: 0.x