Instructions to use iAkashPaul/Indic-gemma-2b-finetuned-sft-Navarasa-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use iAkashPaul/Indic-gemma-2b-finetuned-sft-Navarasa-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 iAkashPaul/Indic-gemma-2b-finetuned-sft-Navarasa-GGUF
# Run inference directly in the terminal:
llama cli -hf iAkashPaul/Indic-gemma-2b-finetuned-sft-Navarasa-GGUF

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf iAkashPaul/Indic-gemma-2b-finetuned-sft-Navarasa-GGUF
# Run inference directly in the terminal:
llama cli -hf iAkashPaul/Indic-gemma-2b-finetuned-sft-Navarasa-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 iAkashPaul/Indic-gemma-2b-finetuned-sft-Navarasa-GGUF
# Run inference directly in the terminal:
./llama-cli -hf iAkashPaul/Indic-gemma-2b-finetuned-sft-Navarasa-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 iAkashPaul/Indic-gemma-2b-finetuned-sft-Navarasa-GGUF
# Run inference directly in the terminal:
./build/bin/llama-cli -hf iAkashPaul/Indic-gemma-2b-finetuned-sft-Navarasa-GGUF

Use Docker

docker model run hf.co/iAkashPaul/Indic-gemma-2b-finetuned-sft-Navarasa-GGUF

LM Studio
Jan
Ollama
How to use iAkashPaul/Indic-gemma-2b-finetuned-sft-Navarasa-GGUF with Ollama:
```
ollama run hf.co/iAkashPaul/Indic-gemma-2b-finetuned-sft-Navarasa-GGUF
```

Unsloth Studio

How to use iAkashPaul/Indic-gemma-2b-finetuned-sft-Navarasa-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 iAkashPaul/Indic-gemma-2b-finetuned-sft-Navarasa-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 iAkashPaul/Indic-gemma-2b-finetuned-sft-Navarasa-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for iAkashPaul/Indic-gemma-2b-finetuned-sft-Navarasa-GGUF to start chatting

Docker Model Runner
How to use iAkashPaul/Indic-gemma-2b-finetuned-sft-Navarasa-GGUF with Docker Model Runner:
```
docker model run hf.co/iAkashPaul/Indic-gemma-2b-finetuned-sft-Navarasa-GGUF
```

Lemonade

How to use iAkashPaul/Indic-gemma-2b-finetuned-sft-Navarasa-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull iAkashPaul/Indic-gemma-2b-finetuned-sft-Navarasa-GGUF

Run and chat with the model

lemonade run user.Indic-gemma-2b-finetuned-sft-Navarasa-GGUF-{{QUANT_TAG}}

List all available models

lemonade list

Atomic Chat

GGUF for Indic-gemma-2b-finetuned-sft-Navarasa

This model from Telugu-LLM-Labs is based on google/gemma-2b and has been LoRA finetuned on 9 Indian languages and English instruction datasets

git clone https://huggingface.co/iAkashPaul/Indic-gemma-2b-finetuned-sft-Navarasa-GGUF # & cd into it, update paths accordingly

# build llama.cpp for your hardware https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#build

./main --file prompt.md --lora ./models/ggml-adapter-model.bin --lora-base ./models/indic-llm_Q8.gguf

./main --file prompt.md -m ./models/merged_indic_llm_Q8.gguf -ngl 99

Prompt template for Instruction adherence-

Save this to a file(ex. prompt.md) & load it with the main executable.

### Instruction: Translate following sentence to Kannada.

### Input: This model is developed by Telugu LLM Labs

## Response:

Performance

LORA+BASE (not merged)

./server --lora ./models/ggml-adapter-model.bin --lora-base ./models/indic-llm_Q8.gguf -m ./models/indic-llm_Q8.gguf

Merged model

./server -ngl 20  -m ./models/merged_indic_llm_Q8.gguf

Downloads last month: 57

GGUF

Model size

3B params

Architecture

gemma

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

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