Update README.md

README.md

@@ -1,202 +1,214 @@
----
-base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit
-library_name: peft
----
-
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
+# Model Card: Gemma-3 Indian Penal Code Legal Assistant
 
 ## Model Details
 
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-
-
-- **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]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
+**Model Description**
+* **Developed by:** Independent researcher
+* **Model type:** LoRA fine-tuned LLM for Indian legal assistance
+* **Language(s):** English with focus on Indian legal terminology
+* **License:** Apache 2.0 (same as base model)
+* **Finetuned from model:** unsloth/gemma-3-4b-it-unsloth-bnb-4bit
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+**Model Sources**
+* **Base model:** [Google's Gemma 3](https://ai.google.dev/gemma)
+* **Fine-tuning framework:** [Unsloth](https://github.com/unslothai/unsloth)
 
 ## 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. -->
-
-[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]
+**Direct Use**
+This model is designed to provide accurate information about the Indian Penal Code (IPC). It can be used by:
+- Legal professionals seeking quick reference to IPC sections
+- Law students studying Indian criminal law
+- Individuals seeking to understand their legal rights and responsibilities
+- Paralegals and legal assistants drafting documents
+
+**Downstream Use**
+- Integration into legal assistance chatbots
+- Incorporation into legal research tools
+- Development of educational materials on Indian criminal law
+
+**Out-of-Scope Use**
+This model should NOT be used for:
+- Providing definitive legal advice that replaces professional legal counsel
+- Making judicial decisions or determinations
+- Predicting case outcomes in specific legal scenarios
+- Generating legal documents without professional review
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+- The model may have incomplete knowledge of the most recent amendments to the Indian Penal Code
+- It may provide information that is technically correct but lacks important contextual nuance
+- The model cannot account for jurisdiction-specific interpretations or case law
+- Performance may vary when dealing with complex legal questions involving multiple sections
+- The model should not be relied upon for actual legal proceedings
 
-[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.
+**Recommendations**
+- Always verify information with official legal sources
+- Consult with qualified legal professionals before making legal decisions
+- Use the model as a supplementary tool, not a replacement for proper legal research
+- Be aware that the model may occasionally generate inaccurate or outdated information
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
-
-[More Information Needed]
+```python
+from unsloth import FastModel
+import torch
+from unsloth.chat_templates import get_chat_template
+
+# Load the base model first
+base_model_name = "unsloth/gemma-3-4b-it-unsloth-bnb-4bit"
+model, tokenizer = FastModel.from_pretrained(
+    model_name = base_model_name,
+    max_seq_length = 2048,
+    load_in_4bit = True,
+    load_in_8bit = False,
+)
+
+# Load the adapter separately
+from peft import PeftModel
+adapter_path = "username/gemma-3-indian-penal-code-model"  # Replace with actual path
+model = PeftModel.from_pretrained(model, adapter_path)
+
+# Set up the chat template
+tokenizer = get_chat_template(
+    tokenizer,
+    chat_template = "gemma-3",
+)
+
+# Function to generate responses
+def generate_response(question):
+    # Include system prompt in user message for Gemma 3
+    user_message = "You are an expert legal assistant providing accurate answers based on the Indian Penal Code (IPC). " + question
+    
+    messages = [
+        {"role": "user", "content": user_message}
+    ]
+    
+    text = tokenizer.apply_chat_template(
+        messages,
+        tokenize=False,
+        add_generation_prompt=True
+    )
+    
+    inputs = tokenizer([text], return_tensors="pt").to("cuda" if torch.cuda.is_available() else "cpu")
+    
+    with torch.no_grad():
+        outputs = model.generate(
+            **inputs,
+            max_new_tokens=512,
+            temperature=0.7,
+            top_p=0.95,
+            top_k=64,
+        )
+    
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+# Example usage
+query = "What is the punishment for theft under Section 379 of the IPC?"
+response = generate_response(query)
+print(response)
+```
 
 ## 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]
+**Training Data**
+- The model was fine-tuned on a curated dataset of Indian Penal Code sections, explanations, and legal interpretations
+- Training data was structured in a conversational format in the file "IPC_cleaned_fine_tuning.json"
+- The dataset covers various sections of the IPC with specific focus on commonly referenced sections
+
+**Training Procedure**
+
+**Preprocessing**
+- Legal texts were structured into conversational format for instruction fine-tuning
+- Data was formatted with appropriate role tags (user/system/model)
+- The Gemma 3 chat template was applied to format the conversations
+
+**Training Hyperparameters**
+* **Training regime:** LoRA (Low-Rank Adaptation) fine-tuning with Unsloth optimization
+* **Rank (r):** 8
+* **Alpha:** 8
+* **Dropout:** 0
+* **Learning rate:** 3e-4
+* **Per device batch size:** 2
+* **Gradient accumulation steps:** 4
+* **Training steps:** 100
+* **Warmup steps:** 2
+* **Optimizer:** AdamW 8-bit
+* **Weight decay:** 0.01
+* **LR scheduler:** Linear
+* **Seed:** 3407
+* **Model bits:** 4-bit quantization (QLoRA)
+* **Max sequence length:** 2048
+
+**Training Configuration**
+- Fine-tuned attention and MLP modules
+- Used response-only training to focus on improving the model's outputs
+- Utilized 4-bit quantization for memory efficiency
+
+**Speeds, Sizes, Times**
+- Training was completed in Google Colab environment
+- Total training time was approximately 2-3 hours on a single GPU
+- The adapter size is approximately 20MB
+- Memory usage was optimized through 4-bit quantization
 
 ## 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]
+**Testing Data, Factors & Metrics**
+
+**Testing Data**
+- A set of 8 test questions covering various aspects of the Indian Penal Code:
+  1. Punishment for theft
+  2. Difference between culpable homicide and murder
+  3. Concept of 'mens rea' in Indian criminal law
+  4. Punishments for different degrees of hurt
+  5. Criminal conspiracy under Section 120A
+  6. Self-defense as a valid defense
+  7. Legal definition of 'dowry death' under Section 304B
+  8. Concept of 'abetment' under the IPC
+
+**Factors**
+- Accuracy of legal information provided
+- Completeness of responses
+- Adherence to the specific sections of the IPC
+- Handling of complex legal concepts
+
+**Metrics**
+- Accuracy of section citations
+- Correctness of punishment terms
+- Completeness of legal explanations
+- Consistency with official IPC text
+
+**Results**
+- The model demonstrates strong understanding of core IPC concepts
+- It accurately cites relevant sections and their corresponding punishments
+- Legal explanations are comprehensive and contextually appropriate
+- The model can differentiate between related but distinct legal concepts
 
 ## 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
+* **Hardware Type:** GPU (Google Colab)
+* **Hours used:** Approximately 2-3 hours for fine-tuning
+* **Cloud Provider:** Google Colab
+* **Carbon Emitted:** Minimal due to short training time and efficient fine-tuning approach
 
-[More Information Needed]
+## Technical Specifications
 
-### Compute Infrastructure
+**Model Architecture and Objective**
+- Base model: Gemma 3 4B instruction-tuned model
+- Fine-tuning method: LoRA adapters with Unsloth optimization
+- Objective: Provide accurate information about the Indian Penal Code while maintaining the base model's general capabilities
 
-[More Information Needed]
+**Compute Infrastructure**
+- Fine-tuning performed on Google Colab
+- Used 4-bit quantization for memory efficiency
 
-#### 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]
+**Software**
+- Python 3.x
+- PyTorch
+- Transformers (v4.49.0-Gemma-3)
+- PEFT
+- Unsloth library for optimization
+- TRL for fine-tuning
 
 ## Model Card Contact
-
-[More Information Needed]
-### Framework versions
-
-- PEFT 0.14.0
+For questions or feedback about this model, please contact the author through the Hugging Face model repository.