Instructions to use DeathBlade020/legal-llama-3b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DeathBlade020/legal-llama-3b with Transformers:

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

pipe = pipeline("text-generation", model="DeathBlade020/legal-llama-3b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("DeathBlade020/legal-llama-3b")
model = AutoModelForCausalLM.from_pretrained("DeathBlade020/legal-llama-3b")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use DeathBlade020/legal-llama-3b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DeathBlade020/legal-llama-3b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DeathBlade020/legal-llama-3b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/DeathBlade020/legal-llama-3b

SGLang

How to use DeathBlade020/legal-llama-3b 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 "DeathBlade020/legal-llama-3b" \
    --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": "DeathBlade020/legal-llama-3b",
		"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 "DeathBlade020/legal-llama-3b" \
        --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": "DeathBlade020/legal-llama-3b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use DeathBlade020/legal-llama-3b 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 DeathBlade020/legal-llama-3b 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 DeathBlade020/legal-llama-3b to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for DeathBlade020/legal-llama-3b to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="DeathBlade020/legal-llama-3b",
    max_seq_length=2048,
)

Docker Model Runner
How to use DeathBlade020/legal-llama-3b with Docker Model Runner:
```
docker model run hf.co/DeathBlade020/legal-llama-3b
```

⚖️ Legal-LLaMA-3B (Fine-tuned on Indian Legal QA)

Model Description

This is a LLaMA-3B model fine-tuned using LoRA (merged) with Unsloth on ~14.5K Indian legal question-answer pairs.
The model is designed to act as a legal assistant chatbot specialized in Indian law (contracts, consumer protection, family law, etc.).

Developed by: DeathBlade020
Model type: Causal LM (decoder-only)
Language(s): English (with Indian legal terminology)
Finetuned from: LLaMA-3B base
License: LLaMA license (Meta AI)

Uses

Direct Use

Educational / research purposes for Indian law Q&A
Chatbot-style applications in legal learning

Out-of-Scope Use

❌ Not a substitute for professional legal advice
❌ Not intended for real-world legal decision making

Training Details

Dataset: ~14,543 Indian legal QA pairs
Training split: ~13,815 train / ~728 validation
Method: LoRA fine-tuning with Unsloth
Epochs: 3
Max seq length: 2048
Optimizer: AdamW, lr=2e-4
Hardware: Google Colab T4 GPU(Free)

Installation

Make sure you have the required libraries installed:

pip install unsloth transformers accelerate torch

Example Usage


from unsloth import FastLanguageModel

model_id = "DeathBlade020/legal-llama-3b"
model, tokenizer = FastLanguageModel.from_pretrained(model_id, max_seq_length=2048)
FastLanguageModel.for_inference(model)

messages = [
    {"role": "system", "content": "You are a legal expert specializing in Indian law."},
    {"role": "user", "content": "What are the essential elements of a valid contract under Indian law?"},
]

inputs = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids=inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloads last month: 4

Safetensors

Model size

3B params

Tensor type

F32

F16