SharathReddy/Indian-Legal-SFT-Dataset
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How to use SharathReddy/Vidhaan-72B-Legal with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="SharathReddy/Vidhaan-72B-Legal")
messages = [
{"role": "user", "content": "Who are you?"},
]
pipe(messages) # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("SharathReddy/Vidhaan-72B-Legal")
model = AutoModelForCausalLM.from_pretrained("SharathReddy/Vidhaan-72B-Legal")
messages = [
{"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
tokenize=True,
return_dict=True,
return_tensors="pt",
).to(model.device)
outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))How to use SharathReddy/Vidhaan-72B-Legal with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SharathReddy/Vidhaan-72B-Legal"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "SharathReddy/Vidhaan-72B-Legal",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'docker model run hf.co/SharathReddy/Vidhaan-72B-Legal
How to use SharathReddy/Vidhaan-72B-Legal with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "SharathReddy/Vidhaan-72B-Legal" \
--host 0.0.0.0 \
--port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "SharathReddy/Vidhaan-72B-Legal",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'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 "SharathReddy/Vidhaan-72B-Legal" \
--host 0.0.0.0 \
--port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "SharathReddy/Vidhaan-72B-Legal",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'How to use SharathReddy/Vidhaan-72B-Legal with Docker Model Runner:
docker model run hf.co/SharathReddy/Vidhaan-72B-Legal
Vidhaan is a fine-tuned version of Qwen2.5-72B, specifically optimized for the Indian Legal domain. It has been trained on over 20,000 rows of Indian Acts, statutes, and judicial inquiries.
Vidhaan is designed to provide detailed explanations of Indian Acts. It is intended for researchers, law students, and legal professionals as a reference tool.
The model was trained on a curated version of the Indian-Legal-SFT-Dataset, focusing on: