Instructions to use dotzcode/gemma-2-indian-bare-acts with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries
PEFT
How to use dotzcode/gemma-2-indian-bare-acts with PEFT:
```
Task type is invalid.
```

How to use dotzcode/gemma-2-indian-bare-acts with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="dotzcode/gemma-2-indian-bare-acts")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("dotzcode/gemma-2-indian-bare-acts", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use dotzcode/gemma-2-indian-bare-acts with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dotzcode/gemma-2-indian-bare-acts"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dotzcode/gemma-2-indian-bare-acts",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/dotzcode/gemma-2-indian-bare-acts

SGLang

How to use dotzcode/gemma-2-indian-bare-acts with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "dotzcode/gemma-2-indian-bare-acts" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dotzcode/gemma-2-indian-bare-acts",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "dotzcode/gemma-2-indian-bare-acts" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dotzcode/gemma-2-indian-bare-acts",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use dotzcode/gemma-2-indian-bare-acts with Docker Model Runner:
```
docker model run hf.co/dotzcode/gemma-2-indian-bare-acts
```

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gemma-2-indian-bare-acts

This is a parameter-efficient fine-tuned (PEFT / LoRA) adapter for Google's Gemma-2-2b-it, trained specifically on central and statutory Indian Bare Acts.

Model Details

Model Description

dotzcode/gemma-2-indian-bare-acts adapts Google's Gemma 2 2B Instruct model for accurate statutory interpretation, legal querying, and understanding statutory provisions, definitions, and penalties under Indian jurisprudence.

Model Name: dotzcode/gemma-2-indian-bare-acts
Developed by: dotzcode
Model Type: PEFT LoRA Adapter (Causal Language Model)
Base Model: google/gemma-2-2b-it
Language(s) (NLP): English (Statutory text & legal terminology)
License: gemma
Finetuned from model: google/gemma-2-2b-it

Statutory Frameworks Covered

Reformed Criminal Codes: Bharatiya Nyaya Sanhita (BNS), Bharatiya Nagarik Suraksha Sanhita (BNSS), and Bharatiya Sakshya Adhiniyam (BSA).
Historic Codes & Cross-References: Indian Penal Code (IPC), Code of Criminal Procedure (CrPC), and Indian Evidence Act (IEA).
Constitutional Law: The Constitution of India (Articles, Schedules, and Fundamental Rights).
Civil & Commercial Acts: Indian Contract Act, Negotiable Instruments Act, Companies Act, and Code of Civil Procedure (CPC).

Uses

Direct Use

Querying specific legal sections, definitions, statutory exceptions, and punishable offenses under Indian law.
Cross-referencing transitional mappings between historic penal laws (IPC/CrPC/IEA) and reformed codes (BNS/BNSS/BSA).
Educational and assistive legal research.

Downstream Use

Integrating into Retrieval-Augmented Generation (RAG) pipelines for legal question answering and statutory document assistants.

Out-of-Scope Use

Automated formal legal counsel or unverified courtroom drafting.
High-stakes judicial decisions without qualified legal review.

Bias, Risks, and Limitations

Hallucinations: Language models may occasionally miscite subsections, procedural conditions, or penalty durations.
Statutory Revisions: Judicial precedents and statutory amendments evolve over time; answers should always be cross-referenced with official Gazette notifications.

Recommendations

All outputs should be treated as research assistance and verified against authoritative statutory texts and primary sources.

#Training Details

Training Data

Trained on instruction-tuned statutory datasets encompassing Indian Bare Acts, cross-code transition tables (IPC to BNS), and section-by-section legal provisions.

Training Procedure

Fine-Tuning Method: Low-Rank Adaptation (LoRA / PEFT)
Precision: Mixed precision (bfloat16)
Objective: Causal Language Modeling / Instruction Tuning

Legal Disclaimer

Disclaimer: This model is designed solely for educational, academic, and assistive research purposes. It does not constitute certified legal counsel or formal legal advice under the Advocates Act, 1961. Always consult a qualified advocate for actionable legal matters.

Framework versions

PEFT 0.16.0
Transformers
PyTorch

How to Get Started with the Model

import torch
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base_model_id = "google/gemma-2-2b-it"
adapter_id = "dotzcode/gemma-2-indian-bare-acts"

tokenizer = AutoTokenizer.from_pretrained(adapter_id)
base_model = AutoModelForCausalLM.from_pretrained(
    base_model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

model = PeftModel.from_pretrained(base_model, adapter_id)

messages = [
    {
        "role": "user",
        "content": "Explain the essential elements and punishment for theft under Section 303 of the Bharatiya Nyaya Sanhita (BNS)."
    }
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

outputs = model.generate(
    **inputs,
    max_new_tokens=350,
    temperature=0.2,
    top_p=0.9,
    do_sample=True
)

response = tokenizer.decode(outputs[0][inputs.input_ids.shape[-1]:], skip_special_tokens=True)
print(response)

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