Updated the card

README.md

@@ -1,202 +1,169 @@
----
 base_model: meta-llama/Meta-Llama-3-8B
 library_name: peft
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
+# Model Card for SinLlama
 
+SinLlama is the first large language model specifically extended for Sinhala. It is based on Meta-Llama-3-8B and adapted through tokenizer vocabulary extension and continual pretraining on a 10M sentence Sinhala corpus. SinLlama significantly improves coverage and performance for Sinhala NLP tasks compared to base and instruct versions of Llama-3-8B.
 
+---
 
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
-
+SinLlama is a decoder-based large language model designed to improve NLP performance for Sinhala, a low-resource Indo-Aryan language spoken by ~20 million people in Sri Lanka. The model was developed by enhancing the Llama-3-8B tokenizer with Sinhala-specific vocabulary and performing continual pretraining on a cleaned and diverse 10.7M-sentence Sinhala corpus.  
 
+Subsequent fine-tuning on Sinhala classification datasets (news categorization, sentiment analysis, and writing style classification) shows significant improvements over baseline Llama-3-8B models.
 
-- **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]
+- **Developed by:** H.W.K. Aravinda, Rashad Sirajudeen, Samith Karunathilake, Nisansa de Silva, Rishemjit Kaur, Surangika Ranathunga:contentReference[oaicite:1]{index=1}  
+- **Funded by:** CSIR - Central Scientific Instruments Organization (India), Emojot (Pvt) Ltd:contentReference[oaicite:2]{index=2}  
+- **Shared by:** Polyglots team  
+- **Model type:** Decoder-only autoregressive transformer LLM  
+- **Language(s) (NLP):** Sinhala (සිංහල)  
+- **License:** Same as base model (Meta Llama 3 license)  
+- **Finetuned from model:** meta-llama/Meta-Llama-3-8B  
 
-### Model Sources [optional]
+### Model Sources
 
-<!-- Provide the basic links for the model. -->
+- **Repository:** [Hugging Face - SinLlama v01](https://huggingface.co/polyglots/SinLlama_v01)  
+- **Paper:** [SinLlama: A Large Language Model for Sinhala](https://arxiv.org/abs/2508.09115v2)  
+- **Dataset:** [MADLAD+CulturaX (cleaned Sinhala subset)](https://huggingface.co/datasets/polyglots/MADLAD_CulturaX_cleaned)  
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+---
 
 ## 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
+- Sinhala text generation
+- Sinhala text classification
+- Sentiment analysis, news categorization, and writing style classification
 
-<!-- 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]
+### Downstream Use
+- Instruction tuning for Sinhala dialogue systems
+- Cross-lingual applications involving Sinhala
+- Educational and research applications in low-resource NLP
 
 ### Out-of-Scope Use
+- Applications requiring high accuracy in non-Sinhala languages (performance may degrade due to adaptation focus on Sinhala)
+- Sensitive domains (e.g., healthcare, legal) without rigorous validation
+- Malicious generation (hate speech, disinformation)
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+---
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+- **Bias:** Sinhala corpora may reflect sociocultural biases (e.g., political, gender, religious biases).  
+- **Limitations:** Model may underperform in complex reasoning tasks or in languages other than Sinhala. Writing-style classification is observed as particularly challenging.  
+- **Risk:** Misuse in spreading misinformation or biased outputs in Sinhala.  
 
 ### Recommendations
+Users should carefully evaluate outputs before deployment, especially in sensitive or safety-critical applications. Fine-tuning with task/domain-specific Sinhala data is recommended for robustness.
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-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.
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
 
-[More Information Needed]
+model_name = "polyglots/SinLlama_v01"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+text = "සිංහල නවතම තාක්‍ෂණ විකාශනය පිළිබඳ පුවතක්"
+inputs = tokenizer(text, return_tensors="pt")
+outputs = model.generate(**inputs, max_length=100)
+print(tokenizer.decode(outputs[0]))
 
 ## 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]
+- **Pretraining:** 10.7M Sinhala sentences (303.9M tokens) from MADLAD-400 and CulturaX, filtered for quality and cleaned:contentReference[oaicite:0]{index=0}.  
+- **Fine-tuning:**  
+  - Sentiment Analysis (~12.5K samples)  
+  - Writing Style Classification (~9K samples)  
+  - Sinhala News Category Classification (~3.3K samples)  
 
 ### 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]
-
+- **Tokenizer:** Extended Llama-3 tokenizer with Sinhala-specific tokens using `tiktoken`.  
+- **Continual Pretraining:** Using codebase from Chinese-Llama, block size reduced from 1024 → 512 for GPU compatibility.  
+- **Fine-tuning:** LoRA-based parameter-efficient finetuning with Alpaca-style prompts.  
 
 #### Training Hyperparameters
+- Mixed precision (fp16/bf16) training  
+- LoRA adapters for efficient fine-tuning  
 
-- **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. -->
+### Testing Data
+- Sinhala sentiment, writing style, and news categorization datasets.  
+- Splits: 80/10/10 with stratified sampling.  
 
-[More Information Needed]
-
-#### 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]
+### Metrics
+- Precision, Recall, F1-score  
 
 ### Results
 
-[More Information Needed]
-
-#### Summary
-
+| Model                  | Writing Style F1 | News F1 | Sentiment F1 |
+|-------------------------|-----------------|---------|--------------|
+| Llama-3-8B base         | 24.50           | 19.03   | 36.29        |
+| Llama-3-8B base finetuned | 49.45        | 61.14   | 59.35        |
+| Llama-3-8B instruct finetuned | 42.25   | 47.81   | 68.78        |
+| **SinLlama finetuned**  | **58.89**       | **86.40** | **72.47**    |
 
+**Summary:** SinLlama outperforms both base and instruct Llama-3-8B when fine-tuned, especially in news categorization and sentiment tasks:contentReference[oaicite:1]{index=1}.  
 
-## 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:** GPUs (not specified, likely A100-class)  
+- **Hours used:** Not reported  
+- **Cloud Provider:** CSIR & Emojot infrastructure:contentReference[oaicite:2]{index=2}  
+- **Compute Region:** India & Sri Lanka  
+- **Carbon Emitted:** Not reported  
 
-- **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]
+## Technical Specifications
 
 ### Model Architecture and Objective
-
-[More Information Needed]
+- Decoder-only transformer (Llama-3-8B backbone)  
+- Autoregressive pretraining objective  
+- Sinhala vocabulary-extended tokenizer  
 
 ### Compute Infrastructure
+- **Hardware:** GPUs provided by CSIR-CSIO and Emojot:contentReference[oaicite:3]{index=3}  
+- **Software:** Hugging Face `transformers`, PEFT, LoRA, `tiktoken`  
 
-[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. -->
+## Citation
 
 **BibTeX:**
-
-[More Information Needed]
+```bibtex
+@article{aravinda2025sinllama,
+  title={SinLlama -- A Large Language Model for Sinhala},
+  author={Aravinda, H.W.K. and Sirajudeen, Rashad and Karunathilake, Samith and de Silva, Nisansa and Kaur, Rishemjit and Ranathunga, Surangika},
+  journal={arXiv preprint arXiv:2508.09115},
+  year={2025}
+}
+```
 
 **APA:**
+Aravinda, H. W. K., Sirajudeen, R., Karunathilake, S., de Silva, N., Kaur, R., & Ranathunga, S. (2025). *SinLlama -- A Large Language Model for Sinhala*. arXiv preprint arXiv:2508.09115.  
 
-[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]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
+---
 
-[More Information Needed]
+## Model Card Authors
+- Based on information from the SinLlama authors:contentReference[oaicite:4]{index=4}
 
 ## Model Card Contact
+- [polyglots on Hugging Face](https://huggingface.co/polyglots)  
 
-[More Information Needed]
 ### Framework versions
-
-- PEFT 0.13.2
+- PEFT 0.13.2
+- Transformers (latest at time of release)