update

README.md

@@ -7,200 +7,65 @@ language:
 - mr
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
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+# Model Card for Gemma2 7B - English to Marathi Translation
 
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
-
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+This model is a fine-tuned variant of **Unsloth's Gemma2 7B**, trained for high-quality English-to-Marathi translations. Built on a robust transformer architecture, the model handles complex translations, idiomatic expressions, and long-context paragraphs effectively. It is optimized for efficient inference using 4-bit quantization. 
 
-- **Developed by:** Devavrat Samak
-- **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:** Devavrat Samak  
+- **Model type:** Causal Language Model, fine-tuned for translation tasks.  
+- **Language(s) (NLP):** English (en), Marathi (mr)  
+- **License:** Apache-2.0  
+- **Finetuned from model:** unsloth/gemma-2-9b-bnb-4bit  
 
-### Model Sources [optional]
+### Model Sources
 
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Repository:** [https://github.com/Devsam2898/Gemma2-Marathi]   
 
 ## 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. -->
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 ### Direct Use
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
+The model can be directly used for English-to-Marathi translations, including handling long-context paragraphs, noisy inputs, and code-mixed sentences.
 
-### Downstream Use [optional]
+### Downstream Use
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
+The model can be integrated into applications for:
+- Chatbots with multilingual support.
+- Translating historical texts for research.
+- Localization of content for Marathi-speaking audiences.
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+- The model is not designed for real-time, high-speed translation in latency-critical systems.
+- It may not generalize well for highly domain-specific jargon without additional fine-tuning.
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+- The model's translations might occasionally lose nuance or context in culturally significant expressions.
+- Performance may degrade for noisy data or highly informal text.
 
 ### Recommendations
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+- Users should validate translations in sensitive domains to ensure accuracy.
+- Consider additional fine-tuning for domain-specific tasks.
 
 ## 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. -->
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-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
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-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
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-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
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-### Testing Data, Factors & Metrics
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-#### 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. -->
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-[More Information Needed]
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-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
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-### Results
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-[More Information Needed]
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-#### Summary
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-
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-## 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]
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-### Compute Infrastructure
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-[More Information Needed]
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-#### Hardware
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-[More Information Needed]
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-#### Software
-
-[More Information Needed]
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-## 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. -->
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-**BibTeX:**
-
-[More Information Needed]
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-**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]
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-## More Information [optional]
-
-[More Information Needed]
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-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
+```python
+from transformers import AutoTokenizer
+from unsloth import Gemma2
 
-[More Information Needed]
-### Framework versions
+# Load model and tokenizer
+model = Gemma2.from_pretrained("unsloth/gemma-2-9b-bnb-4bit")
+tokenizer = AutoTokenizer.from_pretrained("unsloth/gemma-2-9b-bnb-4bit")
 
-- PEFT 0.14.0
+# Input and inference
+input_text = "The golden age of the Peshwas brought cultural and political prosperity to Maharashtra."
+inputs = tokenizer(input_text, return_tensors="pt")
+outputs = model.generate(inputs["input_ids"], max_length=128)
+translated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(translated_text)