Instructions to use prash616/Indica-1.7B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prash616/Indica-1.7B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="prash616/Indica-1.7B-GGUF",
	filename="qwen3-1.7b.Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use prash616/Indica-1.7B-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf prash616/Indica-1.7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf prash616/Indica-1.7B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf prash616/Indica-1.7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf prash616/Indica-1.7B-GGUF:Q4_K_M

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 prash616/Indica-1.7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf prash616/Indica-1.7B-GGUF:Q4_K_M

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 prash616/Indica-1.7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf prash616/Indica-1.7B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/prash616/Indica-1.7B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use prash616/Indica-1.7B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prash616/Indica-1.7B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prash616/Indica-1.7B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/prash616/Indica-1.7B-GGUF:Q4_K_M

Ollama
How to use prash616/Indica-1.7B-GGUF with Ollama:
```
ollama run hf.co/prash616/Indica-1.7B-GGUF:Q4_K_M
```

Unsloth Studio

How to use prash616/Indica-1.7B-GGUF 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 prash616/Indica-1.7B-GGUF 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 prash616/Indica-1.7B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for prash616/Indica-1.7B-GGUF to start chatting

How to use prash616/Indica-1.7B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf prash616/Indica-1.7B-GGUF:Q4_K_M

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": "prash616/Indica-1.7B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use prash616/Indica-1.7B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf prash616/Indica-1.7B-GGUF:Q4_K_M

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 prash616/Indica-1.7B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use prash616/Indica-1.7B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf prash616/Indica-1.7B-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "prash616/Indica-1.7B-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use prash616/Indica-1.7B-GGUF with Docker Model Runner:
```
docker model run hf.co/prash616/Indica-1.7B-GGUF:Q4_K_M
```

Lemonade

How to use prash616/Indica-1.7B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull prash616/Indica-1.7B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Indica-1.7B-GGUF-Q4_K_M

List all available models

lemonade list

Indica-1.7B-GGUF / README.md

prash616

Update README.md

10629fd verified 5 months ago

3.57 kB

license: apache-2.0
base_model: unsloth/Qwen3-1.7B
language:
  - hi
  - en
  - bn
  - ta
  - te
  - mr
  - gu
pipeline_tag: text-generation
tags:
  - indian-languages
  - hinglish
  - reasoning
  - gguf
  - quantization
  - unsloth
  - legal
  - agriculture

Indica-1.7B-GGUF 🇮🇳

Indica-1.7B is a lightweight, high-performance SLM (Small Language Model) specifically post-trained for the Indian context. Built upon the Qwen3-1.7B dense transformer architecture, this model has undergone a rigorous multi-stage alignment (fine-tuning for behavior and preference) process to excel in Hindi, Hinglish, and various regional dialects while maintaining strong CoT (Chain-of-Thought) reasoning capabilities.

This repository provides the model in GGUF format, optimized for local inference (the process of generating text) on consumer hardware using tools like Ollama, llama.cpp, and LM Studio.

🚀 Model Details

Architecture: 1.7 Billion parameters (the internal variables a neural network learns), utilizing a dense causal transformer design.
Specialized Domains: Tailored for Indian Law (IPC/BNS), Agriculture (MSP/PM-Kisan), and National Examinations (UPSC/JEE).
Multilingual Mastery: Fluent in Hindi-English code-switching (Hinglish) and supports multiple regional Indian languages.
Thinking Paradigm: Utilizes a native "thinking mode" via <think> tags for complex reasoning tasks before outputting a final answer.
Context Length: Supports up to 2048 tokens natively.
Developer: Prashant (prash616).

🛠 Training Pipeline

The model was developed through a specialized four-stage alignment strategy:

Foundational Pre-training: Fine-tuned on Hindi Wikipedia (wikimedia/wikipedia) to establish deep linguistic roots and vocabulary density.
Supervised Fine-Tuning (SFT): Trained on high-quality instruction datasets (ai4bharat/indic-instruct-data-v0.1, viber1/indian-law-dataset) covering Indian law, agriculture, and everyday Hinglish conversations.
GRPO (Group Relative Policy Optimization): Aligned using Reinforcement Learning to reward logical reasoning and the use of internal thinking tags using GSM8K datasets.
DPO (Direct Preference Optimization): Final behavioral polish using HuggingFaceH4/ultrafeedback_binarized to ensure a helpful, polite, and culturally aware persona.

📦 Quantization Details

These GGUF files were created using llama.cpp through the Unsloth library. Quantization (the process of reducing the precision of the model's numbers) allows the model to run on machines with limited VRAM (Video Random Access Memory).

Filename	Bit-Size	File Size	Use Case
`Indica-1.7B-Q4_K_M.gguf`	4-bit	~1.1 GB	Recommended. Balanced quality and extreme speed. Ideal for standard laptops, MacBooks, and low-RAM devices.
`Indica-1.7B-Q8_0.gguf`	8-bit	~1.8 GB	Maximum Quality. Retains near-perfect precision from the 16-bit model. Recommended for technical legal or mathematical queries.

💻 How to Use

1. With Ollama (Easiest)

Ensure you have Ollama installed, then run:

ollama run hf.co/prash616/Indica-1.7B-GGUF




## 2. With LM Studio
Download LM Studio.
Search for prash616/Indica-1.7B-GGUF in the search bar.
Download the Q4_K_M file and load it into the local server.

##3. Chat Template (For Developers)
If you are writing custom Python inference scripts, the model uses the standard qwen-3 chat template. Ensure your system prompt is set correctly: