Instructions to use kaushik-harsh-99/Qwen-IndianLegal-Instruct-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kaushik-harsh-99/Qwen-IndianLegal-Instruct-v1 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="kaushik-harsh-99/Qwen-IndianLegal-Instruct-v1",
	filename="Qwen-IndianLegal-Instruct-v1.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use kaushik-harsh-99/Qwen-IndianLegal-Instruct-v1 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf kaushik-harsh-99/Qwen-IndianLegal-Instruct-v1
# Run inference directly in the terminal:
llama-cli -hf kaushik-harsh-99/Qwen-IndianLegal-Instruct-v1

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf kaushik-harsh-99/Qwen-IndianLegal-Instruct-v1
# Run inference directly in the terminal:
llama-cli -hf kaushik-harsh-99/Qwen-IndianLegal-Instruct-v1

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 kaushik-harsh-99/Qwen-IndianLegal-Instruct-v1
# Run inference directly in the terminal:
./llama-cli -hf kaushik-harsh-99/Qwen-IndianLegal-Instruct-v1

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 kaushik-harsh-99/Qwen-IndianLegal-Instruct-v1
# Run inference directly in the terminal:
./build/bin/llama-cli -hf kaushik-harsh-99/Qwen-IndianLegal-Instruct-v1

Use Docker

docker model run hf.co/kaushik-harsh-99/Qwen-IndianLegal-Instruct-v1

LM Studio
Jan
Ollama
How to use kaushik-harsh-99/Qwen-IndianLegal-Instruct-v1 with Ollama:
```
ollama run hf.co/kaushik-harsh-99/Qwen-IndianLegal-Instruct-v1
```

Unsloth Studio

How to use kaushik-harsh-99/Qwen-IndianLegal-Instruct-v1 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 kaushik-harsh-99/Qwen-IndianLegal-Instruct-v1 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 kaushik-harsh-99/Qwen-IndianLegal-Instruct-v1 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for kaushik-harsh-99/Qwen-IndianLegal-Instruct-v1 to start chatting

How to use kaushik-harsh-99/Qwen-IndianLegal-Instruct-v1 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf kaushik-harsh-99/Qwen-IndianLegal-Instruct-v1

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "kaushik-harsh-99/Qwen-IndianLegal-Instruct-v1"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use kaushik-harsh-99/Qwen-IndianLegal-Instruct-v1 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf kaushik-harsh-99/Qwen-IndianLegal-Instruct-v1

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default kaushik-harsh-99/Qwen-IndianLegal-Instruct-v1

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use kaushik-harsh-99/Qwen-IndianLegal-Instruct-v1 with Docker Model Runner:
```
docker model run hf.co/kaushik-harsh-99/Qwen-IndianLegal-Instruct-v1
```

Lemonade

How to use kaushik-harsh-99/Qwen-IndianLegal-Instruct-v1 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull kaushik-harsh-99/Qwen-IndianLegal-Instruct-v1

Run and chat with the model

lemonade run user.Qwen-IndianLegal-Instruct-v1-{{QUANT_TAG}}

List all available models

lemonade list

🇮🇳 Indian Legal Instruction Fine-Tuning

Qwen 2.5 (0.5B) — Domain-Specific LLM

Overview

This project fine-tunes Qwen 2.5 (0.5B) on a custom-built Indian legal instruction dataset to improve structured reasoning, drafting, and legal Q&A.

Core idea: maximize small model capability using high-quality, domain-specific data.

📊 Training Metrics

Observations:

Smooth loss convergence
Stable gradient norm
Training stabilizes after early-stage fixes

🧪 Before vs After Fine-Tuning

Example 1

Example 2

Example 3

Overall Improvements:

Outputs become more structured and readable
Stronger legal terminology and accuracy
Better long-form coherence
Reduced vague / generic responses

🧠 Dataset

Domain: Indian Law (Acts, provisions, procedures)
Format: Instruction → Response
Language: English
Size: ~171K samples (v2)

Characteristics:

High information density
Structured legal reasoning
Long-form responses
Reduced noise and redundancy

📂 Data Format

{
  "instruction": "Question about a legal concept",
  "response": "Structured legal answer with explanation"
}

Downloads last month: 207

Safetensors

Model size

0.5B params

Tensor type

F16

Inference Providers NEW

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Model tree for kaushik-harsh-99/Qwen-IndianLegal-Instruct-v1

Quantizations

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Dataset used to train kaushik-harsh-99/Qwen-IndianLegal-Instruct-v1

Collection including kaushik-harsh-99/Qwen-IndianLegal-Instruct-v1

Indian Legal LLMs

Collection

Compact Indian Legal LLMs fine-tuned for Indian law knowledge, legal reasoning, legal document scanning, summarization, and hypothetical legal draft • 2 items • Updated 14 days ago