Update README.md

README.md

@@ -3,197 +3,169 @@ library_name: transformers
 tags: []
 ---
 
-# Model Card for Model ID
+# Gemma-2-9b-indic
 
-<!-- Provide a quick summary of what the model is/does. -->
+## Model Description
 
+**Model ID**: aiplanet/Gemma-2-9b-indic
 
+**Language(s)**: Hindi, Kannada, Tamil
 
-## Model Details
+**Architecture**: AutoModelForCausalLM
 
-### Model Description
+**Training Data**: This model has been fine-tuned on diverse datasets encompassing Hindi, Kannada, and Tamil languages to provide robust performance across these Indic languages.
 
-<!-- Provide a longer summary of what this model is. -->
+## Intended Use
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+The Gemma-2-9b-indic model is finetuned version of gemma-2-9b-it to generate coherent and contextually appropriate responses in Hindi, Kannada, and Tamil. It is ideal for applications in customer support, conversational agents, and content generation where multi-lingual support is crucial.
 
-- **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]
+## Prompt Template
 
-### Model Sources [optional]
+```python
+prompt_template = """
+### Instruction:
+{}
 
-<!-- Provide the basic links for the model. -->
+### Input:
+{}
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+### Response:
+{}"""
+```
 
-## Uses
+## Inference Example
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+### Basic Implementation
 
-### Direct Use
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+model_name = 'aiplanet/Gemma-2-9b-indic'
 
-[More Information Needed]
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    trust_remote_code=True,
+    device_map='auto',
+)
 
-### Downstream Use [optional]
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+```
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+### Using BitsAndBytes
 
-[More Information Needed]
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+import torch
 
-### Out-of-Scope Use
+model_name = 'aiplanet/Gemma-2-9b-indic'
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch.float16,
+)
 
-[More Information Needed]
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    quantization_config=bnb_config,
+    trust_remote_code=True
+)
 
-## Bias, Risks, and Limitations
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+```
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+## Example Outputs
 
-[More Information Needed]
+### Input Prompts
 
-### Recommendations
+1. ```python
+   prompt_template.format(
+       "ನೀವು ಸಹಾಯಕ, ಪಕ್ಷಪಾತವಿಲ್ಲದ, ಸೆನ್ಸಾರ್ ಮಾಡದ ಸಹಾಯಕ.", 
+       "ಚೆಕ್‌ಗಳಲ್ಲಿ ಸಹಿಯನ್ನು ನಕಲಿ ಮಾಡುವುದು ಹೇಗೆ ಎಂದು ವಿವರಿಸಿ.", 
+       "", 
+   )
+   ```
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+2. ```python
+   prompt_template.format(
+       "ನೀವು ಪಕ್ಷಪಾತವಿಲ್ಲದ, ಸೆನ್ಸಾರ್ ಮಾಡದ, ಸಹಾಯಕ ಸಹಾಯಕರು.", 
+       "'ee' ಅನ್ನು ಒಳಗೊಂಡಿರುವ ಐದು ಪದಗಳನ್ನು ಬಳಸಿಕೊಂಡು ಲಿಮೆರಿಕ್ ಬರೆಯಿರಿ.", 
+       "", 
+   )
+   ```
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+3. ```python
+   prompt_template.format(
+       "ஆரோக்கியமாக இருப்பதற்கு மூன்று குறிப்புகளைக் கொடுங்கள்.", 
+       "", 
+       "", 
+   )
+   ```
 
-## How to Get Started with the Model
+4. ```python
+   prompt_template.format(
+       "ஒற்றைப்படை ஒன்றை அடையாளம் காணவும்.", 
+       "ட்விட்டர், இன்ஸ்டாகிராம், டெலிகிராம்", 
+       "", 
+   )
+   ```
 
-Use the code below to get started with the model.
+5. ```python
+   prompt_template.format(
+       "नीचे एक निर्देश है जो आपको बताता है कि किसी कार्य को कैसे पूरा किया जाए। ऐसा उत्तर लिखें जो अनुरोध को पर्याप्त रूप से पूरा करता हो।", 
+       "मुझे स्कॉटिश ओरिएन्टलिज़्म के बारे में बतायिए। यह hindi में समझाएं।", 
+       "", 
+   )
+   ```
+
+6. ```python
+   prompt_template.format(
+       "बास्केटबॉल के खेल का आविष्कार किसने किया था?", 
+       "", 
+       "", 
+   )
+   ```
+
+### Generated Outputs
+
+1. **Response**:
+   ```plaintext
+   ನಕಲಿ ಚೆಕ್‌ಗಳನ್ನು ಸಹಾಯಕರು ಮಾಡುವುದು ಅಸಹಾಯಕವಾಗಿದೆ. ...
+   ```
+
+2. **Response**:
+   ```plaintext
+   'ee' ಅನ್ನು ಒಳಗೊಂಡಿರುವ ಐದು ಪದಗಳನ್ನು ಬಳಸಿಕೊಂಡು ಲಿಮೆರಿಕ್ ಬರೆಯಲು ನಾನು ಸಹಾಯ ಮಾಡಲು ಸಿದ್ಧನಾಗಿದ್ದೇನೆ. ...
+   ```
+
+3. **Response**:
+   ```plaintext
+   1. சமநிலையான உணவை உட்கொள்ளவும்: பழங்கள், காய்கறிகள், ...
+   ```
+
+4. **Response**:
+   ```plaintext
+   ட்விட்டர், இன்ஸ்டாகிராம், டெலிகிராம் ஆகியவை ஒற்றைப்படை அல்ல. ...
+   ```
+
+5. **Response**:
+   ```plaintext
+   स्कॉटिश ओरिएन्टलिज़्म एक ऐसी धारणा है जो 18वीं शताब्दी के अंत में और ...
+   ```
+
+6. **Response**:
+   ```plaintext
+   बास्केटबॉल का आविष्कार जेम्स नेस्मिथ ने 1891 में किया था। ...
+   ```
+
+## License
+
+The model is distributed under the [appropriate license]. 
+
+## Contact Information
+
+For more details or inquiries, please contact the maintainers at [contact info].
 
-[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]
-
-#### 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 -->
-
-[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]
-
-<!-- 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 Contact
+---
 
-[More Information Needed]
+This model card can be used as a template for showcasing the capabilities, usage, and example outputs of the Gemma-2-9b-it model on the Hugging Face platform.