Update README.md

README.md

@@ -3,11 +3,11 @@ library_name: transformers
 tags: []
 ---
 
-# Model Card for Model ID
+# Model Card for AI-Manith/manith-gemma-sinhala-gpt
 
 <!-- Provide a quick summary of what the model is/does. -->
 
-
+This model is a fine-tuned version of the `google/gemma-2b` model for English-to-Sinhala translation.
 
 ## Model Details
 
@@ -15,23 +15,21 @@ tags: []
 
 <!-- Provide a longer summary of what this model is. -->
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+This model is a fine-tuned version of the `google/gemma-2b` model using the `Programmer-RD-AI/sinhala-english-singlish-translation` dataset from Hugging Face. It was fine-tuned using PEFT and QLoRA for efficient training on a single GPU.
 
-- **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:** Manith Jayaba
+- **Model type:** Causal Language Model (Fine-tuned for Translation)
+- **Language(s) (NLP):** English to Sinhala
+- **License:** Apache 2.0 (inherited from Gemma)
+- **Finetuned from model [optional]:** google/gemma-2b
 
 ### Model Sources [optional]
 
 <!-- Provide the basic links for the model. -->
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Repository:** [More Information Needed - You can add a link to your Hugging Face model repository here]
+- **Paper [optional]:** N/A
+- **Demo [optional]:** [More Information Needed - You can add a link to a demo if you create one]
 
 ## Uses
 
@@ -39,161 +37,97 @@ This is the model card of a 🤗 transformers model that has been pushed on the
 
 ### Direct Use
 
-<!-- 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]
-
-### Out-of-Scope Use
-
-<!-- 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]
-
-### 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]
-
+This model can be used for translating English text to Sinhala text.
 
-#### Training Hyperparameters
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+import torch
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+# Define the model ID on the Hugging Face Hub
+model_id = "google/gemma-2b"
+peft_model_id = "AI-Manith/manith-gemma-sinhala-gpt"
 
-#### Speeds, Sizes, Times [optional]
+# Load the base model
+base_model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+# Load the tokenizer
+tokenizer = AutoTokenizer.from_pretrained(model_id)
 
-[More Information Needed]
+# Load the LoRA adapters and merge them with the base model
+model = PeftModel.from_pretrained(base_model, peft_model_id)
+model = model.merge_and_unload() # Merge LoRA layers and unload the adapter
 
-## Evaluation
+# Ensure the model is in evaluation mode
+model.eval()
 
-<!-- This section describes the evaluation protocols and provides the results. -->
+# Define the translation function
+def translate_from_hub(english_text):
+    """This function takes an English sentence and returns the Sinhala translation using the model from the Hub."""
+    instruction = "Translate the following English text to Sinhala."
+    prompt_text = f"""### INSTRUCTION:
+{instruction}
 
-### Testing Data, Factors & Metrics
+### INPUT:
+{english_text}
 
-#### Testing Data
+### RESPONSE:
+"""
 
-<!-- This should link to a Dataset Card if possible. -->
+    # Tokenize the input
+    inputs = tokenizer(prompt_text, return_tensors="pt").to("cuda")
 
-[More Information Needed]
+    # Generate the response
+    with torch.no_grad(): # Disable gradient calculation for inference
+        outputs = model.generate(**inputs, max_new_tokens=100)
 
-#### Factors
+    # Decode the output and extract just the response part
+    decoded_output = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    response_part = decoded_output.split("### RESPONSE:")[1].strip()
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+    return response_part
 
-[More Information Needed]
+# --- Test Cases --- #
+print("\n--- Testing the Translator from Hub ---")
 
-#### Metrics
+test_sentence_1 = "How are you doing today?"
+translation_1 = translate_from_hub(test_sentence_1)
+print(f"English: {test_sentence_1}")
+print(f"Sinhala: {translation_1}")
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+print("---")
 
-[More Information Needed]
+test_sentence_2 = "Can you translate this sentence?"
+translation_2 = translate_from_hub(test_sentence_2)
+print(f"English: {test_sentence_2}")
+print(f"Sinhala: {translation_2}")
+```
 
-### 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]
+### Downstream Use [optional]
 
-## Glossary [optional]
+This model can be used as a component in larger applications requiring English-to-Sinhala translation.
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+### Out-of-Scope Use
 
-[More Information Needed]
+This model is not intended for:
+- Translating from Sinhala to English.
+- Translating between other language pairs.
+- Generating text in languages other than Sinhala based on English input.
 
-## More Information [optional]
+## Bias, Risks, and Limitations
 
-[More Information Needed]
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
 
-## Model Card Authors [optional]
+- The model's performance is dependent on the quality and coverage of the training data. It may not perform well on informal language, slang, or highly technical text not present in the dataset.
+- As with any translation model, there is a risk of perpetuating biases present in the training data.
+- The model may produce inaccurate or nonsensical translations for certain inputs.
 
-[More Information Needed]
+### Recommendations
 
-## Model Card Contact
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
 
-[More Information Needed]
+Users should be aware of the model's limitations and evaluate the quality of the translations for their specific use case. It is recommended to use the model for its intended purpose (English to Sinhala translation) and to be mindful of potential biases or inaccuracies.