Instructions to use varma007ut/Indian_law_chat_minor_project with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use varma007ut/Indian_law_chat_minor_project with Transformers:

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

pipe = pipeline("text-generation", model="varma007ut/Indian_law_chat_minor_project")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("varma007ut/Indian_law_chat_minor_project")
model = AutoModelForCausalLM.from_pretrained("varma007ut/Indian_law_chat_minor_project")

llama-cpp-python

How to use varma007ut/Indian_law_chat_minor_project with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="varma007ut/Indian_law_chat_minor_project",
	filename="Indian_Law_chat.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Inference
Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use varma007ut/Indian_law_chat_minor_project with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf varma007ut/Indian_law_chat_minor_project
# Run inference directly in the terminal:
llama-cli -hf varma007ut/Indian_law_chat_minor_project

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf varma007ut/Indian_law_chat_minor_project
# Run inference directly in the terminal:
llama-cli -hf varma007ut/Indian_law_chat_minor_project

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf varma007ut/Indian_law_chat_minor_project
# Run inference directly in the terminal:
./llama-cli -hf varma007ut/Indian_law_chat_minor_project

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf varma007ut/Indian_law_chat_minor_project
# Run inference directly in the terminal:
./build/bin/llama-cli -hf varma007ut/Indian_law_chat_minor_project

Use Docker

docker model run hf.co/varma007ut/Indian_law_chat_minor_project

LM Studio
Jan

vLLM

How to use varma007ut/Indian_law_chat_minor_project with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "varma007ut/Indian_law_chat_minor_project"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "varma007ut/Indian_law_chat_minor_project",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/varma007ut/Indian_law_chat_minor_project

SGLang

How to use varma007ut/Indian_law_chat_minor_project 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 "varma007ut/Indian_law_chat_minor_project" \
    --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": "varma007ut/Indian_law_chat_minor_project",
		"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 "varma007ut/Indian_law_chat_minor_project" \
        --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": "varma007ut/Indian_law_chat_minor_project",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use varma007ut/Indian_law_chat_minor_project with Ollama:
```
ollama run hf.co/varma007ut/Indian_law_chat_minor_project
```

Unsloth Studio

How to use varma007ut/Indian_law_chat_minor_project with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for varma007ut/Indian_law_chat_minor_project to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for varma007ut/Indian_law_chat_minor_project to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for varma007ut/Indian_law_chat_minor_project to start chatting

Docker Model Runner
How to use varma007ut/Indian_law_chat_minor_project with Docker Model Runner:
```
docker model run hf.co/varma007ut/Indian_law_chat_minor_project
```

Lemonade

How to use varma007ut/Indian_law_chat_minor_project with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull varma007ut/Indian_law_chat_minor_project

Run and chat with the model

lemonade run user.Indian_law_chat_minor_project-{{QUANT_TAG}}

List all available models

lemonade list

Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

YAML Metadata Warning:The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

Model Card: Indian Law Chat Model

Model Details

Model Name: Indian Law Chat Model
Model Type: Fine-tuned Llama-2-7b for Indian Law [Minor Project]
Task: Conversational AI with a focus on Indian law
License: Apache 2.0

Faculty In Charge

Guide: Dr. Kalimuthu K
Project Coordinator: Dr. Sandeep Kumar P

Team Members

RA2111004010006: Ganesha Sai Varma
RA2111004010008: Surendra Reddy
RA2111004010055: Sampath Voona

Model Description

This model is a fine-tuned version of the Llama-2-7b model, designed to assist with questions related to Indian law. It has been fine-tuned on a dataset containing various aspects of Indian legal texts and cases to provide accurate and contextually relevant answers to legal inquiries.

Project Description

This project is a minor project for SRM University, completed as part of the 7th-semester curriculum by fourth-year students. The objective was to create an AI model capable of understanding and providing information on Indian law. The project leverages the power of the Llama-2 model, fine-tuned with a specialized dataset focused on Indian legal systems and documentation.

Training Data

The model was fine-tuned using a dataset comprising various Indian legal texts, including statutes, case laws, legal commentaries, and other relevant documents to ensure comprehensive coverage of the Indian legal system.

Intended Use

The primary use of this model is to assist users, including legal practitioners, students, and researchers, in obtaining information and clarifications on Indian law. It can be used in educational settings, legal research, and preliminary legal consultations.

Limitations

The model is trained on available legal texts and may not be up-to-date with the latest legal amendments.
It is not a substitute for professional legal advice.
Performance may vary based on the specificity and complexity of the questions asked.

Ethical Considerations

Users should verify the information provided by the model with up-to-date legal resources.
The model should not be used as the sole resource for making legal decisions.

How to Use

The model can be accessed via a web-based interface built with gradio. Users can input their legal queries, and the model will provide relevant responses based on its training data.

Acknowledgements

We extend our gratitude to SRM University for providing the resources and support necessary for completing this project. Special thanks to our faculty advisors for their guidance.

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