Update README.md

README.md

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----
-library_name: transformers
-tags: []
----
-
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
+# 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
 
+---
 
-## Model Details
+## 📌 Overview
 
-### Model Description
+**Bakti-8B-Base** is an 8-billion-parameter Large Language Model (LLM) adapted specifically for Indonesia's strategic focus areas:
 
-<!-- Provide a longer summary of what this model is. -->
+* **Perlindungan Ruang Digital (PRD)** – Digital Space Protection
+* **Digital Talent Pool (DTP)** – Workforce and digital capability development
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+This model is built through **Continued Pre‑training (CPT)** on the **Qwen‑3‑8B** base model using a curated Indonesian dataset.
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+---
 
-### Model Sources [optional]
+## 🧠 Model Details
 
-<!-- Provide the basic links for the model. -->
+### Model Description
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+* **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)
 
-## Uses
+### 🎯 Goal
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+To create a sovereign, domain‑specialized Indonesian foundation model with strong understanding of:
 
-### Direct Use
+* Digital policies (UU PDP, UU ITE)
+* Digital workforce & skill landscape (DTP)
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+---
 
-[More Information Needed]
+## 📚 Dataset Composition
 
-### Downstream Use [optional]
+Total Dataset Size: **~214.2 Million Tokens**
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+| 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%**   |
 
-[More Information Needed]
+---
 
-### Out-of-Scope Use
+## 🧩 Intended Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+As a **Base Model**, Bakti‑8B outputs **text completions** and can be adapted into chat/instruct variants.
 
-[More Information Needed]
+### 1. PRD (Perlindungan Ruang Digital)
 
-## Bias, Risks, and Limitations
+* Policy sentiment analysis
+* Misinformation pattern detection
+* Understanding legal terminology (UU ITE, UU PDP)
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+### 2. DTP (Digital Talent Pool)
 
-[More Information Needed]
+* Skill gap analysis
+* Curriculum drafting assistance
+* Job description & talent understanding
 
-### Recommendations
+---
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+## 🚀 How to Get Started
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+Load the model using **HuggingFace Transformers**:
 
-## How to Get Started with the Model
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
 
-Use the code below to get started with the model.
+# 1. Configuration
+model_id = "YOUR_USERNAME/Bakti-8B-Base"  # Replace with your actual Hub ID
 
-[More Information Needed]
+# 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"
+)
 
-## Training Details
+# 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")
 
-### Training Data
+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))
+```
 
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+---
 
-[More Information Needed]
+## ⚙️ Training Details
 
 ### Training Procedure
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
+The model was continued‑pretrained with a **causal language modeling (CLM)** objective while preserving base reasoning capabilities.
 
-#### Metrics
+### Hardware & Environment
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+* **GPU:** NVIDIA A100 80GB (Colab Pro+)
+* **Training Duration:** ~36 hours
+* **Frameworks:** PyTorch, Transformers, Accelerate
 
-[More Information Needed]
+### 🔧 Hyperparameters (Highlights)
 
-### Results
+* Sequence Length: **4096**
+* Optimizer: **AdamW**
+* Scheduler: **Cosine Decay**
+* Precision: **bf16**
 
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+---
 
-[More Information Needed]
+## ⚠️ Limitations
 
-## More Information [optional]
+* **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.
 
-[More Information Needed]
+---
 
-## Model Card Authors [optional]
+## ✅ Recommendations
 
-[More Information Needed]
+For production use, it is recommended to:
 
-## Model Card Contact
+* Perform **Supervised Fine‑Tuning (SFT)** for PRD/DTP domains
+* Add **high‑quality instruction datasets**
+* Apply **evaluation benchmarks** before deployment
 
-[More Information Needed]
+---