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

c0752f1

verified ·

1 Parent(s): 10629fd

Update README.md

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@@ -21,50 +21,96 @@ tags:
 - 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:

 - agriculture
 ---
+# Indica-1.7B-GGUF: Post-Tuned for the Indian Context 🇮🇳
+**Indica-1.7B** is a lightweight, high-performance reasoning model specifically post-trained to navigate the linguistic and cultural nuances of India. Built on the **Qwen3-1.7B** architecture, this model utilizes a specialized four-stage alignment pipeline to excel in Hindi, Hinglish, and regional dialects while providing domain expertise in Indian law, agriculture, and education.
+This repository provides optimized **GGUF** weights, enabling private, local inference on consumer-grade hardware via **Ollama**, **llama.cpp**, and **LM Studio**.
+---
+## 🚀 Model Overview
+* **Model Type:** Causal Language Model
+* **Architecture:** Dense Transformer (Qwen3-1.7B base)
+* **Parameters:** 1.7 Billion
+* **Primary Languages:** Hindi, English, Hinglish
+* **Secondary Languages:** Bengali, Tamil, Telugu, Marathi, Gujarati
+* **Context Window:** 2048 tokens
+* **Thinking Mode:** Native `<think>` tag support for Chain-of-Thought reasoning
+---
+## 🛠️ Training Details
+The model was developed through a rigorous multi-stage post-training regime designed to maximize factual groundedness and reasoning depth.
+### 1. Dataset Composition
+The training utilized a diverse mix of specialized Indian datasets:
+| Stage | Dataset | Purpose |
+| :--- | :--- | :--- |
+| **Pre-train** | `wikimedia/wikipedia` (Hindi) | Foundational linguistic and cultural grounding. |
+| **SFT** | `ai4bharat/indic-instruct-data-v0.1` | Core instruction following in Indian languages. |
+| **SFT** | `Abhishekcr448/Hinglish-Everyday-Conversations` | Mastery of urban code-switching (Hinglish). |
+| **SFT** | `viber1/indian-law-dataset` | Expertise in IPC, CRPC, and the Constitution. |
+| **GRPO** | `openai/gsm8k` | Reasoning and Chain-of-Thought (CoT) alignment. |
+| **DPO** | `HuggingFaceH4/ultrafeedback_binarized` | Behavioral polish and preference alignment. |
+### 2. Hyper-parameters
+The following parameters were utilized during the final alignment stages to ensure stability and reasoning quality:
+#### **Stage 3: GRPO (Reinforcement Learning)**
+* **Learning Rate:** 5e-6
+* **Group Size (num_generations):** 6
+* **Max Prompt Length:** 256 tokens
+* **Max Completion Length:** 200 tokens
+* **Optimizer:** AdamW (8-bit)
+* **LR Scheduler:** Cosine
+#### **Stage 4: DPO (Preference Alignment)**
+* **Learning Rate:** 5e-5
+* **Beta:** 0.1
+* **Batch Size:** 1 per device
+* **Gradient Accumulation:** 4
+* **Max Length:** 1024 tokens
+---
+## 📦 Quantization & Deployment
+These GGUF files were exported via the Unsloth library using `llama.cpp`.
+| Quant Method | File Size | Recommended For |
+| :--- | :--- | :--- |
+| **Q4_K_M** | ~1.1 GB | General use, mobile devices, and high-speed local inference. |
+| **Q8_0** | ~1.8 GB | High-precision tasks requiring maximum accuracy. |
+### Local Inference with Ollama
 ```bash
 ollama run hf.co/prash616/Indica-1.7B-GGUF
+```
+## ⚖️ Capabilities & Limitations
+### Capabilities
+- **Reasoning:** Capable of step-by-step logical reasoning using native `<think>` tags.
+- **Identity Awareness:** Firmly recognizes its persona as **Indica**, built and maintained by **Prashant**.
+- **Cultural Context:** Demonstrates strong understanding of Indian legal systems (IPC/BNS), agriculture, and national-level competitive examinations.
+### Limitations
+- **Parameter Constraints:** As a 1.7B-parameter model, it may exhibit hallucinations during extremely complex technical analysis or detailed legal drafting.
+- **Repetition:** In certain generation contexts, the model may enter repetition loops.
+  - *Mitigation:* Increasing `repetition_penalty` to **1.1–1.2** is recommended.
+---
+## 🤝 Acknowledgements
+- **Base Weights:** Alibaba Qwen Team
+- **Optimization & GGUF Export:** Unsloth AI
+- **Developer:** Prashant (`prash616`)