Update README.md

README.md

@@ -1,199 +1,107 @@
 ---
 library_name: transformers
-tags: []
+tags:
+- Tibetan
+- NLP
+- low-resource
+- LLM
+- language-model
+- multilingual
+language:
+- bo
+base_model:
+- buddhist-nlp/gemma2-mitra-base
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
+# Model Card for Monlam Melong preview
 
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
+Monlam Melong is a large language model (LLM)(Tibetan LLM) specifically designed to support and enhance Natural Language Processing (NLP) tasks for the Tibetan language, a traditionally low-resource language. The model can handle a range of NLP tasks, including machine translation, named entity recognition (NER), question answering, text generation, and sentence segmentation. It supports translation from over 200 languages into Tibetan and vice versa, making it one of the most comprehensive AI models for Tibetan language processing to date.
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+Monlam Melong was developed as part of Monlam AI's initiative to preserve and promote the Tibetan language and cultural heritage. By building and training a Tibetan-centric LLM, MonlamMelong addresses the technological gap that has historically excluded low-resource languages from mainstream AI development.
 
-- **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:** Monlam AI
+- **Model type:** Large Language Model (LLM)
+- **Language(s) (NLP):** Tibetan, with support for multilingual translation (200+ languages)
+- **License:** Open-Source License (details to be added)
+- **Finetuned from model [optional]:** Custom model architecture built on top of pre-trained models
 
 ### Model Sources [optional]
 
-<!-- Provide the basic links for the model. -->
+- **Repository:** [Link to model repository]
+- **Paper [optional]:** [Link to any relevant research paper, if applicable]
+- **Demo [optional]:** [Link to demo, if available]
 
-- **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
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+MonlamMelong can be used directly to support a wide range of NLP tasks in the Tibetan language, including:
 
-[More Information Needed]
+- **Translation:** Translation from Tibetan to 200+ languages and vice versa.
+- **Text-to-Text Generation:** Writing letters, essays, or educational materials in Tibetan.
+- **Content Creation:** Generation of Tibetan children's stories, educational content, and creative writing.
+- **Information Extraction:** Named Entity Recognition (NER) for historical, cultural, and linguistic research.
+- **Text Segmentation:** Sentence and paragraph segmentation for downstream NLP tasks.
 
 ### Downstream Use [optional]
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+MonlamMelong can be fine-tuned or adapted for specialized tasks such as:
 
-[More Information Needed]
+- **Linguistic Analysis:** Tools for researchers in linguistics or anthropology studying the Tibetan language.
+- **Education Apps:** Use in Tibetan language learning platforms and educational tools.
+- **Digital Libraries:** Use in text search, retrieval, and analysis for Tibetan digital archives.
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+- **Misuse for Generating Misinformation:** Users should refrain from using MonlamMelong to generate false or misleading content.
+- **Uncontrolled Autonomy:** The model should not be used in fully autonomous systems that make critical decisions without human oversight.
 
-[More Information Needed]
+---
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- 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.
-
-[More Information Needed]
-
-## 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]
-
-### 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]
+MonlamMelong inherits biases from its training data, as NLP models are often influenced by the language and perspectives present in the datasets. Special attention should be paid to the following issues:
 
-#### Factors
+- **Linguistic Bias:** Since MonlamMelong was primarily trained on Tibetan text, it may not perform as well on non-Tibetan NLP tasks. 
+- **Cultural Representation:** The model may reflect existing societal and cultural biases present in its training data, especially in sensitive or historical contexts.
+- **Translation Accuracy:** While MonlamMelong supports translation from 200+ languages, errors may arise due to differences in sentence structure and idiomatic expressions between languages.
+- **Data Limitations:** As a model for a low-resource language, the training data may not be as extensive as data available for high-resource languages like English or Mandarin.
 
-<!-- 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 -->
-
-[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]
+## Recommendations
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+- **Human Oversight:** Users should review the model's outputs, especially in educational or historical contexts, where precision and cultural sensitivity are crucial.
+- **Bias Audits:** Institutions using MonlamMelong for translation or information extraction should regularly audit for bias in its performance.
+- **Fine-Tuning for Specialized Tasks:** For specific academic or educational applications, fine-tuning the model may improve task-specific performance.
 
-[More Information Needed]
+---
 
-## More Information [optional]
+## How to Get Started with the Model
 
-[More Information Needed]
+To use Monlam Melong with the 🤗 Transformers library, you can load the model as follows:
 
-## Model Card Authors [optional]
+```python
+import torch
+from transformers import pipeline
 
-[More Information Needed]
+pipe = pipeline(
+    "text-generation",
+    model="TenzinGayche/Melong_preview",
+    model_kwargs={"torch_dtype": torch.bfloat16},
+    device="cuda",  # replace with "mps" to run on a Mac device
+)
 
-## Model Card Contact
+messages = [
+    {"role": "user", "content": "Please translate the following text into Tibetan: Hi how are you ? Translation: "},
+]
 
-[More Information Needed]
+outputs = pipe(messages, max_new_tokens=256)
+assistant_response = outputs[0]["generated_text"][-1]["content"].strip()
+print(assistant_response)