Instructions to use coderop12/gemma2b-nirf-lookup-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use coderop12/gemma2b-nirf-lookup-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="coderop12/gemma2b-nirf-lookup-gguf",
	filename="gemma2b-nirf-lookup-f16.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use coderop12/gemma2b-nirf-lookup-gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf coderop12/gemma2b-nirf-lookup-gguf:F16
# Run inference directly in the terminal:
llama-cli -hf coderop12/gemma2b-nirf-lookup-gguf:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf coderop12/gemma2b-nirf-lookup-gguf:F16
# Run inference directly in the terminal:
llama-cli -hf coderop12/gemma2b-nirf-lookup-gguf:F16

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 coderop12/gemma2b-nirf-lookup-gguf:F16
# Run inference directly in the terminal:
./llama-cli -hf coderop12/gemma2b-nirf-lookup-gguf:F16

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 coderop12/gemma2b-nirf-lookup-gguf:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf coderop12/gemma2b-nirf-lookup-gguf:F16

Use Docker

docker model run hf.co/coderop12/gemma2b-nirf-lookup-gguf:F16

LM Studio
Jan

vLLM

How to use coderop12/gemma2b-nirf-lookup-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "coderop12/gemma2b-nirf-lookup-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": "coderop12/gemma2b-nirf-lookup-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/coderop12/gemma2b-nirf-lookup-gguf:F16

Ollama
How to use coderop12/gemma2b-nirf-lookup-gguf with Ollama:
```
ollama run hf.co/coderop12/gemma2b-nirf-lookup-gguf:F16
```

Unsloth Studio new

How to use coderop12/gemma2b-nirf-lookup-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 coderop12/gemma2b-nirf-lookup-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 coderop12/gemma2b-nirf-lookup-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for coderop12/gemma2b-nirf-lookup-gguf to start chatting

Docker Model Runner
How to use coderop12/gemma2b-nirf-lookup-gguf with Docker Model Runner:
```
docker model run hf.co/coderop12/gemma2b-nirf-lookup-gguf:F16
```

Lemonade

How to use coderop12/gemma2b-nirf-lookup-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull coderop12/gemma2b-nirf-lookup-gguf:F16

Run and chat with the model

lemonade run user.gemma2b-nirf-lookup-gguf-F16

List all available models

lemonade list

gemma2b-nirf-lookup-gguf

This is a GGUF conversion of coderop12/gemma2b-nirf-lookup-2025.

Model Details

Original Model: coderop12/gemma2b-nirf-lookup-2025
Format: GGUF (F16 precision)
File Size: ~4.9 GB
Architecture: Gemma 2B
Specialization: NIRF (National Institutional Ranking Framework) lookup and ranking queries

Usage

With llama.cpp

./llama-cli -m gemma2b-nirf-lookup-gguf.gguf -p "What is the NIRF ranking methodology?"

With Python (llama-cpp-python)

from llama_cpp import Llama

llm = Llama(model_path="gemma2b-nirf-lookup-gguf.gguf")
response = llm("What are the top NIRF ranked engineering colleges?")
print(response['choices'][0]['text'])

With Ollama

# First, create a Modelfile
echo 'FROM ./gemma2b-nirf-lookup-gguf.gguf' > Modelfile
ollama create gemma2b-nirf-lookup-gguf -f Modelfile
ollama run gemma2b-nirf-lookup-gguf "Explain NIRF ranking parameters"

Model Capabilities

This model is specifically fine-tuned for:

NIRF ranking information and queries
Indian higher education institutional data
University and college ranking explanations
Educational policy and framework questions

Technical Details

Quantization: F16 (16-bit floating point)
Context Length: 2048 tokens
License: Follow original model license terms
Converted using: llama.cpp conversion tools

Original Model License

Please refer to the original model repository for license information.

Downloads last month: 13

GGUF

Model size

3B params

Architecture

gemma2

Hardware compatibility

16-bit

Model tree for coderop12/gemma2b-nirf-lookup-gguf

Base model

google/gemma-2-2b

Finetuned

google/gemma-2-2b-it

Finetuned

coderop12/gemma2b-nirf-lookup-2025

Quantized

(1)

this model