Update README.md

README.md

@@ -1,210 +1,99 @@
 ---
 base_model: aitfindonesia/Bakti-8B-Base
 library_name: peft
+license: apache-2.0
 pipeline_tag: text-generation
 tags:
 - base_model:adapter:aitfindonesia/Bakti-8B-Base
 - lora
 - sft
 - transformers
-- trl
 - unsloth
+- multi-turn
+- chatbot
+- indonesian
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
+# Model Card for SFT-Bakti-8B-Base-MultiTurn-Chatbot
 
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
-
-
+This model is a fine-tuned version of **[aitfindonesia/Bakti-8B-Base]** designed specifically for **multi-turn conversational capabilities** in the Indonesian language. It was trained using the **Unsloth** library for faster and memory-efficient training, utilizing LoRA (Low-Rank Adaptation).
 
-- **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]
+The model is optimized to handle context retention across multiple turns of conversation, making it suitable for interview simulations, customer support, and general-purpose Indonesian assistants.
 
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Developed by:** DTP Fine Tuning Team
+- **Model type:** Causal Language Model (Fine-tuned Qwen2/3 architecture)
+- **Language(s) (NLP):** Indonesian
+- **License:** Apache 2.0
+- **Finetuned from model:** aitfindonesia/Bakti-8B-Base
 
 ## Uses
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
 ### Direct Use
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
+The model is designed for:
+- Multi-turn chat interactions in Indonesian.
+- Question Answering (QA) requiring context from previous turns.
+- Roleplay interactions (e.g., interview scenarios).
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
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-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
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-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
+- The model should not be used for generating factually accurate data without RAG (Retrieval Augmented Generation) as hallucinations are possible.
+- Not intended for code generation tasks.
 
 ## Training Details
 
 ### Training Data
 
-<!-- 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]
+**Dataset:** `dtp-fine-tuning/dtp-multiturn-interview-valid-15k`
+- **Split:** Train (90%) / Test (10%)
+- **Format:** Multi-turn conversation format.
+- **Max Length:** 2048 tokens
 
 ### Training Procedure
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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-#### Preprocessing [optional]
-
-[More Information Needed]
-
+The model was fine-tuned using **Unsloth** on a single NVIDIA A100 (80GB) GPU. It utilizes 4-bit quantization (NF4) to reduce memory usage while maintaining performance via QLoRA.
 
 #### 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]
+- **Training regime:** QLoRA (4-bit quantization with FP16 precision)
+- **Optimizer:** AdamW 8-bit
+- **Learning Rate:** $2 \times 10^{-5}$
+- **Scheduler:** Linear with 5% warmup
+- **Batch Size:** 8 per device (Gradient Accumulation: 4)
+- **Epochs:** 2
+- **LoRA Config:**
+    - Rank ($r$): 16
+    - Alpha ($\alpha$): 32
+    - Dropout: 0.05
+    - Target Modules: `q_proj`, `k_proj`, `v_proj`, `o_proj`, `gate_proj`, `up_proj`, `down_proj`
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
+#### Hardware
+- **GPU:** NVIDIA A100 80GB PCIe
+- **VRAM Usage:** Peak allocation approx. 19GB (23% utilization) due to 4-bit loading.
 
 ## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
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-### Testing Data, Factors & Metrics
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-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
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-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
 ### Results
 
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
+The model demonstrates strong convergence on the multi-turn dataset.
+- **Final Train Loss:** $\approx 0.42$
+- **Final Eval Loss:** $\approx 0.41$
 
-[More Information Needed]
+*Note: The model outperforms the standard Qwen3-8B baseline on this specific Indonesian dataset, achieving lower loss values faster.*
 
 ## 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]
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-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
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-#### Hardware
-
-[More Information Needed]
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-#### Software
-
-[More Information Needed]
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-## 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:**
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-[More Information Needed]
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-**APA:**
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-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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-[More Information Needed]
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-## More Information [optional]
-
-[More Information Needed]
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-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
+- **Hardware Type:** NVIDIA A100 80GB
+- **Compute Region:** asia-east1
+- **Carbon Emitted:** 0.31
 
-[More Information Needed]
-### Framework versions
+## Framework Versions
 
-- PEFT 0.18.0
+- Unsloth
+- PEFT
+- Transformers
+- TRL