Upload model_card.yml with huggingface_hub

model_card.yml

@@ -0,0 +1,65 @@
+---
+license: apache-2.0
+datasets:
+- Pravesh390/country-capital-mixed
+language:
+- en
+library_name: transformers
+pipeline_tag: text2text-generation
+tags:
+- qlora
+- flan-t5
+- prompt-tuning
+- question-answering
+- hallucination
+- robust-qa
+- country-capital
+model-index:
+- name: flan-t5-qlora-countryqa-v1
+  results:
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      type: Pravesh390/country-capital-mixed
+      name: Country-Capital Mixed QA
+    metrics:
+    - type: bleu
+      value: 92.5
+    - type: rouge
+      value: 87.3
+---
+
+# 🧠 FLAN-T5 QLoRA (Prompt Tuned) - Country Capital QA
+
+This model is a fine-tuned version of `google/flan-t5-base` using **QLoRA** and **Prompt Tuning** on a hybrid QA dataset.
+
+## 📌 Highlights
+- 🔍 Correct & incorrect (hallucinated) QA pairs
+- ⚙️ Trained using 4-bit QLoRA with PEFT
+- 🔧 Prompt tuning enables parameter-efficient adaptation
+
+## 🏗️ Training
+- Base Model: `google/flan-t5-base`
+- Method: **QLoRA** + **Prompt Tuning** with PEFT
+- Quantization: 4-bit NF4
+- Frameworks: 🤗 Transformers, PEFT, Accelerate
+- Evaluation: BLEU = 92.5, ROUGE = 87.3
+
+## 📚 Dataset
+Mixture of 20 correct and 3 incorrect QA samples from `Pravesh390/country-capital-mixed`.
+
+## 📦 Usage
+```python
+from transformers import pipeline
+pipe = pipeline("text2text-generation", model="Pravesh390/flan-t5-qlora-countryqa-v1")
+pipe("What is the capital of Brazil?")
+```
+
+## 📈 Intended Use
+- Evaluate hallucinations in QA systems
+- Robust model development for real-world QA
+- Academic research or education
+
+## 🏷️ License
+Apache 2.0 — Free for research and commercial use.