Instructions to use abhinavhuria/gemma-zerodha-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use abhinavhuria/gemma-zerodha-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="abhinavhuria/gemma-zerodha-gguf",
	filename="gemma-zerodha.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use abhinavhuria/gemma-zerodha-gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf abhinavhuria/gemma-zerodha-gguf
# Run inference directly in the terminal:
llama-cli -hf abhinavhuria/gemma-zerodha-gguf

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf abhinavhuria/gemma-zerodha-gguf
# Run inference directly in the terminal:
llama-cli -hf abhinavhuria/gemma-zerodha-gguf

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 abhinavhuria/gemma-zerodha-gguf
# Run inference directly in the terminal:
./llama-cli -hf abhinavhuria/gemma-zerodha-gguf

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 abhinavhuria/gemma-zerodha-gguf
# Run inference directly in the terminal:
./build/bin/llama-cli -hf abhinavhuria/gemma-zerodha-gguf

Use Docker

docker model run hf.co/abhinavhuria/gemma-zerodha-gguf

LM Studio
Jan
Ollama
How to use abhinavhuria/gemma-zerodha-gguf with Ollama:
```
ollama run hf.co/abhinavhuria/gemma-zerodha-gguf
```

Unsloth Studio new

How to use abhinavhuria/gemma-zerodha-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 abhinavhuria/gemma-zerodha-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 abhinavhuria/gemma-zerodha-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for abhinavhuria/gemma-zerodha-gguf to start chatting

Docker Model Runner
How to use abhinavhuria/gemma-zerodha-gguf with Docker Model Runner:
```
docker model run hf.co/abhinavhuria/gemma-zerodha-gguf
```

Lemonade

How to use abhinavhuria/gemma-zerodha-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull abhinavhuria/gemma-zerodha-gguf

Run and chat with the model

lemonade run user.gemma-zerodha-gguf-{{QUANT_TAG}}

List all available models

lemonade list

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Gemma Zerodha App Reviews (GGUF)

This is a GGUF quantized version of the Gemma model fine-tuned on Zerodha app reviews.

Usage with Ollama

Download the files and run:

ollama create gemma-zerodha -f Modelfile
ollama run gemma-zerodha

Model Details

Base Model: google/gemma-2-2b-it
Fine-tuned on: Zerodha app reviews dataset
Quantization: 8-bit (q8_0)
Format: GGUF (Ollama compatible)

Example

ollama run gemma-zerodha "Analyze this review: Great trading platform but needs better customer support"

Downloads last month: -

GGUF

Model size

1.0B params

Architecture

gemma3

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Inference Providers NEW

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Model tree for abhinavhuria/gemma-zerodha-gguf

Base model

google/gemma-2-2b

Finetuned

google/gemma-2-2b-it

Quantized

(177)

this model