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

5d356b8 verified 5 months ago

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	---
	license: apache-2.0
	base_model: unsloth/Qwen3-1.7B
	language:
	- hi
	- en
	pipeline_tag: text-generation
	tags:
	- indian-languages
	- hinglish
	- reasoning
	- experimental
	- research
	- unsloth
	---

	# 🧪 Indica-1.7B: An Experimental Research Model 🇮🇳

	> NOTICE: This is an experimental model released for research and development purposes. It serves as a proof-of-concept for a 4-stage post-training pipeline on Small Language Models (SLMs).

	Indica-1.7B is a lightweight model developed by Prashant to explore the limits of persona-injection and cultural alignment in ultra-small parameter architectures (1.7B). Built on Qwen3-1.7B, this model was subjected to a rigorous training regime including SFT, GRPO (Reasoning), and DPO (Alignment).

	---

	## 🔬 The Research Objective
	The goal of this project was to test whether a 1.7B model could successfully balance three conflicting objectives:
	1. Domain Expertise: Knowledge of Indian Law (IPC/BNS) and Agriculture.
	2. Linguistic Persona: Natural, colloquial Hinglish/Hindi code-switching.
	3. Logic & Reasoning: Utilizing a native "Thinking" trace via Reinforcement Learning.

	## 🛠️ Post-Training Pipeline
	The model underwent a specialized four-stage alignment strategy:
	* Stage 1: SFT (Knowledge): Trained on Indian Law and Agriculture datasets.
	* Stage 2: GRPO (Reasoning): Reinforcement Learning to reward the use of `<think>` tags for logical tasks.
	* Stage 3: DPO (Persona): Preference alignment to craft a friendly "Indian AI Assistant" identity.
	* Stage 4: Optimization: Exported via Unsloth for high-efficiency inference.

	---

	## 📉 Known Limitations & Experimental Findings (The "Alignment Tax")
	As an experimental 1.7B model, Indica demonstrates several critical findings regarding Catastrophic Forgetting:

	* Factual Regression: Due to the limited parameter capacity, the final alignment (DPO) stage has caused the model to lose some precision in mathematical calculations and specific legal section numbering.
	* Persona Drift: The model prioritizes its "creative persona" over technical accuracy. It may identify itself as an "AI Zindagi Manager" or other creative identities.
	* Logic Bypassing: In some instances, the model may skip the internal `<think>` reasoning trace and provide direct, occasionally incorrect, answers.
	* Repetition Loops: Occasional gibberish or repetition loops may occur in conversational Hinglish.

	## 📦 Deployment for Testing
	This model is best used to study Hinglish conversational patterns or as a base for further fine-tuning experiments.

	### With Ollama
	```bash
	ollama run hf.co/prash616/Indica-1.7B-GGUF

	```

	## 🤝 Credits & Acknowledgements

	- Developer: Prashant (`prash616`)
	- Base Model: Alibaba Qwen Team
	- Training Framework: Unsloth AI

	### Disclaimer

	This model is intended solely for educational and research purposes.
	It should not be used as a substitute for professional advice, including but not limited to legal, agricultural, or mathematical decision-making.