Update README.md

README.md

@@ -22,39 +22,39 @@ pipeline_tag: text-generation
 
 # 🧠 SALAMA LLM — Swahili Instruction-Tuned Text Generation Model
 
-**Developer:** AI4NNOV 
-**Authors:** AI4NNOV.  
-**Version:** v1.0  
-**License:** Apache 2.0  
-**Model Type:** Instruction-Tuned Large Language Model  
-**Base Model:** `Jacaranda/UlizaLlama`  
+**👨‍💻 Developer:** AI4NNOV  
+**✍️ Authors:** AI4NNOV  
+**📦 Version:** v1.0  
+**📜 License:** Apache 2.0  
+**🛠️ Model Type:** Instruction-Tuned Large Language Model  
+**🧩 Base Model:** `Jacaranda/UlizaLlama`
 
 ---
 
 ## 🌍 Overview
 
 **SALAMA LLM** is the **language understanding and generation engine** of the **SALAMA Framework** — a modular Speech-to-Speech (STS) AI pipeline built for African languages.  
-The model is fine-tuned on Swahili instruction datasets to enable natural, culturally relevant responses in text generation, summarization, question answering, and translation.  
+The model is fine-tuned on Swahili instruction datasets to enable natural, culturally relevant responses in text generation, summarization, question answering, and translation.
 
-This model represents a major step in bridging the linguistic digital divide by providing high-quality Swahili AI text generation capabilities within an open, scalable framework.
+This model represents a major step in bridging the linguistic digital divide by providing **high-quality Swahili AI text generation** capabilities within an open, scalable framework.
 
 ---
 
-## 🧱 Model Architecture
+## 🧱️ Model Architecture
 
 SALAMA LLM is based on **Jacaranda/UlizaLlama**, fine-tuned using **Parameter-Efficient Fine-Tuning (PEFT)** via **LoRA/QLoRA**.  
 The architecture supports mixed Swahili-English text inputs while focusing on fluent Swahili text generation for both casual and formal domains.
 
 | Parameter | Value |
 |------------|--------|
-| Base Model | `Jacaranda/UlizaLlama` |
-| Fine-Tuning | QLoRA / LoRA (PEFT) |
-| Precision | 4-bit quantization |
-| Optimizer | AdamW |
-| Learning Rate | 2e-5 |
-| Epochs | 3–5 |
-| Frameworks | Transformers, TRL, PEFT, Unsloth |
-| Languages | Swahili (sw), English (en) |
+| **Base Model** | `Jacaranda/UlizaLlama` |
+| **Fine-Tuning** | QLoRA / LoRA (PEFT) |
+| **Precision** | 4-bit quantization |
+| **Optimizer** | AdamW |
+| **Learning Rate** | 2e-5 |
+| **Epochs** | 3–5 |
+| **Frameworks** | Transformers, TRL, PEFT, Unsloth |
+| **Languages** | Swahili (sw), English (en) |
 
 ---
 
@@ -70,11 +70,11 @@ The architecture supports mixed Swahili-English text inputs while focusing on fl
 
 ## 🧠 Model Capabilities
 
-- Text generation in **Swahili and English**  
-- Instruction-following, summarization, and dialogue  
-- Question answering and translation (EN ↔ SW)  
-- Sentiment and named-entity recognition  
-- Contextually and culturally aligned text generation  
+✅ Text generation in **Swahili and English**  
+✅ Instruction-following, summarization, and dialogue  
+✅ Question answering and translation (EN ↔ SW)  
+✅ Sentiment and named-entity recognition  
+✅ Contextually and culturally aligned text generation  
 
 ---
 
@@ -120,11 +120,11 @@ outputs = model.generate(
 )
 
 print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
 
+**🦩 Example Output:**
 
-**Example Output:**
-
-> *“Elimu ni msingi wa maendeleo, humwezesha mtu kuelewa dunia na kuboresha maisha yake na jamii kwa ujumla.”*
+> “Elimu ni msingi wa maendeleo, humwezesha mtu kuelewa dunia na kuboresha maisha yake na jamii kwa ujumla.”
 
 ---
 
@@ -132,16 +132,16 @@ print(tokenizer.decode(outputs[0], skip_special_tokens=True))
 
 - 🧩 Optimized for African low-resource NLP contexts  
 - 💬 Instruction-following in Swahili and English  
-- ⚙️ Lightweight and efficient** (QLoRA-fine-tuned, runs on single 24 GB GPU)  
+- ⚙️ Lightweight and efficient (QLoRA fine-tuned; runs on single 24 GB GPU)  
 - 🌍 Culturally aligned text generation  
-- 🪶 Open-source and extendable to other African languages  
+- 🦶 Open-source and extendable to other African languages  
 
 ---
 
 ## 🚫 Limitations
 
 - ⚠️ May underperform with heavy code-switching (Swahili-English mix)  
-- 🗣️ Not yet optimized for rare dialects or poetic forms  
+- 👤 Not yet optimized for rare dialects or poetic forms  
 - 📚 Limited exposure to specialized (medical/legal) corpora  
 - 🔊 Relies on accurate STT transcription in end-to-end speech-to-speech use  
 
@@ -154,3 +154,23 @@ print(tokenizer.decode(outputs[0], skip_special_tokens=True))
 | [`EYEDOL/salama-stt`](https://huggingface.co/EYEDOL/salama-stt) | Swahili Speech-to-Text model (Whisper-small fine-tuned) |
 | [`EYEDOL/salama-tts`](https://huggingface.co/EYEDOL/salama-tts) | Swahili Text-to-Speech model (VITS architecture) |
 
+---
+
+## 🧾 Citation
+
+If you use **SALAMA LLM**, please cite:
+
+```bibtex
+@misc{salama_llm_2025,
+  title={SALAMA LLM: Swahili Instruction-Tuned Text Generation Model},
+  author={AI4NNOV},
+  year={2025},
+  publisher={Hugging Face},
+  howpublished={\url{https://huggingface.co/EYEDOL/salama-llm}}
+}
+```
+
+---
+
+**💡 “Elimu ni msingi wa maendeleo — Knowledge is the foundation of progress.”**
+