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

prash616 commited on Feb 22

Commit

10629fd

verified ·

1 Parent(s): a66910a

Update README.md

Browse files

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README.md +29 -30

README.md CHANGED Viewed

@@ -9,9 +9,6 @@ language:
 - te
 - mr
 - gu
-- kn
-- ml
-- pa
 pipeline_tag: text-generation
 tags:
 - indian-languages
@@ -20,52 +17,54 @@ tags:
 - gguf
 - quantization
 - unsloth
 ---
-# Indica-1.7B-GGUF — Optimized for the Indian Context 🇮🇳
-Indica-1.7B is a lightweight, high-performance model specifically post-trained to serve the linguistic and cultural nuances of India. Built upon the **Qwen3-1.7B** architecture, this model has undergone a rigorous multi-stage alignment process to excel in Hindi, Hinglish, and various regional dialects while maintaining strong reasoning capabilities.
-This repository provides the model in **GGUF** format, optimized for local inference on consumer hardware using tools like **Ollama**, **llama.cpp**, and **LM Studio**.
-## 🚀 Model Highlights
-- **Specialized Post-Training:** 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" for complex reasoning tasks via Chain-of-Thought (CoT).
-- **Efficient Deployment:** The 1.7B parameter count ensures fast, private, and local execution with minimal RAM requirements.
 ## 🛠 Training Pipeline
 The model was developed through a specialized four-stage alignment strategy:
-1. **Foundational Pre-training:** Fine-tuned on Hindi Wikipedia to establish deep linguistic roots.
-2. **Supervised Fine-Tuning (SFT):** Trained on high-quality instruction datasets covering Indian law, agriculture, and everyday Hinglish conversations.
-3. **GRPO (Reinforcement Learning):** Aligned using Group Relative Policy Optimization to reward logical reasoning and the use of `<think>` tags.
-4. **DPO (Preference Alignment):** Final behavioral polish using Direct Preference Optimization to ensure a helpful, polite, and culturally aware persona.
-## 📊 Key Datasets
-- **Indic-Instruct & Aya:** For foundational instruction-following in Indian languages.
-- **Hinglish-Everyday-Conversations:** To master natural code-switching used in urban India.
-- **Viber1 Indian Law Dataset:** Specialized knowledge of the Indian Penal Code and Constitution.
-- **GSM8K:** For mathematical and logical reasoning alignment.
-- **UltraFeedback Binarized:** For preference alignment and behavioral safety.
 ## 📦 Quantization Details
-These GGUF files were created using `llama.cpp` through the Unsloth library.
-| File | Size | Optimization | Recommended Use |
 | :--- | :--- | :--- | :--- |
-| **Q4_K_M** | ~1.1 GB | Balanced | Best for general use on mobile or low-RAM devices. |
-| **Q8_0** | ~1.8 GB | High Precision | Recommended for technical tasks (Law/Math). |
 ## 💻 How to Use
-### With Ollama
-You can run this model directly via the Hugging Face URL:
 ```bash
 ollama run hf.co/prash616/Indica-1.7B-GGUF
- Limitations & Disclaimer
-While Indica-1.7B is highly optimized for the Indian context, it is a 1.7B parameter model. It may occasionally exhibit hallucinations or repetition loops in very long conversations. For technical or legal queries, it is recommended to verify the output against official documentation.
-Developed by: Prashant (prash616)

 - te
 - mr
 - gu
 pipeline_tag: text-generation
 tags:
 - indian-languages
 - 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:
+1. **Foundational Pre-training:** Fine-tuned on Hindi Wikipedia (`wikimedia/wikipedia`) to establish deep linguistic roots and vocabulary density.
+2. **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.
+3. **GRPO (Group Relative Policy Optimization):** Aligned using Reinforcement Learning to reward logical reasoning and the use of internal thinking tags using GSM8K datasets.
+4. **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](https://ollama.com/) installed, then run:
 ```bash
 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: