Instructions to use joyboseroy/nyayasaar-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use joyboseroy/nyayasaar-lora with Transformers:

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

pipe = pipeline("text-generation", model="joyboseroy/nyayasaar-lora")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("joyboseroy/nyayasaar-lora", dtype="auto")

PEFT
How to use joyboseroy/nyayasaar-lora with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use joyboseroy/nyayasaar-lora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "joyboseroy/nyayasaar-lora"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "joyboseroy/nyayasaar-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/joyboseroy/nyayasaar-lora

SGLang

How to use joyboseroy/nyayasaar-lora 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 "joyboseroy/nyayasaar-lora" \
    --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": "joyboseroy/nyayasaar-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "joyboseroy/nyayasaar-lora" \
        --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": "joyboseroy/nyayasaar-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use joyboseroy/nyayasaar-lora 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 joyboseroy/nyayasaar-lora 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 joyboseroy/nyayasaar-lora to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for joyboseroy/nyayasaar-lora to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="joyboseroy/nyayasaar-lora",
    max_seq_length=2048,
)

Docker Model Runner
How to use joyboseroy/nyayasaar-lora with Docker Model Runner:
```
docker model run hf.co/joyboseroy/nyayasaar-lora
```

joyboseroy commited on 9 days ago

Commit

3fc0665

verified ·

1 Parent(s): 169e801

Update README.md

Browse files

Files changed (1) hide show

README.md +62 -1

README.md CHANGED Viewed

@@ -1,3 +1,64 @@
 ---
-license: mit
 ---

 ---
+license: apache-2.0
+base_model: unsloth/Qwen2.5-0.5B-Instruct-bnb-4bit
+tags:
+- legal-ai
+- lora
+- peft
+- qlora
+- indian-law
+- legal-simplification
+- accessibility
+- irac
+- transformers
+- unsloth
+language:
+- en
+pipeline_tag: text-generation
+datasets:
+- joyboseroy/inIRAC
+library_name: transformers
 ---
+# NyayaSaar-LoRA
+NyayaSaar-LoRA is a lightweight LoRA adapter fine-tuned to simplify structured Indian legal reasoning into plain English.
+The project focuses on improving accessibility to legal reasoning for non-lawyers interacting with the Indian legal system.
+This model was trained using parameter-efficient fine-tuning (LoRA/QLoRA) on structured IRAC-style legal reasoning examples derived from the inIRAC dataset.
+---
+# Motivation
+Indian legal documents are often difficult for ordinary citizens to understand because of:
+- archaic terminology,
+- procedural complexity,
+- dense sentence structures,
+- and formal legal phrasing.
+NyayaSaar-LoRA explores whether small language models can learn to preserve legal meaning while simplifying language and improving readability.
+The project prioritizes:
+- accessibility,
+- explainability,
+- low-resource adaptation,
+- and efficient fine-tuning.
+---
+# Example
+## Input
+```text
+Issue: Whether the detention order violates Article 22.
+Rule: Preventive detention laws require procedural safeguards.
+Application: The petitioner argued safeguards were not followed.
+Conclusion: The detention order is quashed.