Instructions to use Telugu-LLM-Labs/Indic-gemma-7b-finetuned-sft-Navarasa-2.0-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Telugu-LLM-Labs/Indic-gemma-7b-finetuned-sft-Navarasa-2.0-gguf with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Telugu-LLM-Labs/Indic-gemma-7b-finetuned-sft-Navarasa-2.0-gguf")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Telugu-LLM-Labs/Indic-gemma-7b-finetuned-sft-Navarasa-2.0-gguf", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Telugu-LLM-Labs/Indic-gemma-7b-finetuned-sft-Navarasa-2.0-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 Telugu-LLM-Labs/Indic-gemma-7b-finetuned-sft-Navarasa-2.0-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Telugu-LLM-Labs/Indic-gemma-7b-finetuned-sft-Navarasa-2.0-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 Telugu-LLM-Labs/Indic-gemma-7b-finetuned-sft-Navarasa-2.0-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Telugu-LLM-Labs/Indic-gemma-7b-finetuned-sft-Navarasa-2.0-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 Telugu-LLM-Labs/Indic-gemma-7b-finetuned-sft-Navarasa-2.0-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Telugu-LLM-Labs/Indic-gemma-7b-finetuned-sft-Navarasa-2.0-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 Telugu-LLM-Labs/Indic-gemma-7b-finetuned-sft-Navarasa-2.0-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Telugu-LLM-Labs/Indic-gemma-7b-finetuned-sft-Navarasa-2.0-gguf:Q4_K_M

Use Docker

docker model run hf.co/Telugu-LLM-Labs/Indic-gemma-7b-finetuned-sft-Navarasa-2.0-gguf:Q4_K_M

LM Studio
Jan

vLLM

How to use Telugu-LLM-Labs/Indic-gemma-7b-finetuned-sft-Navarasa-2.0-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Telugu-LLM-Labs/Indic-gemma-7b-finetuned-sft-Navarasa-2.0-gguf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Telugu-LLM-Labs/Indic-gemma-7b-finetuned-sft-Navarasa-2.0-gguf",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Telugu-LLM-Labs/Indic-gemma-7b-finetuned-sft-Navarasa-2.0-gguf:Q4_K_M

SGLang

How to use Telugu-LLM-Labs/Indic-gemma-7b-finetuned-sft-Navarasa-2.0-gguf with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Telugu-LLM-Labs/Indic-gemma-7b-finetuned-sft-Navarasa-2.0-gguf" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Telugu-LLM-Labs/Indic-gemma-7b-finetuned-sft-Navarasa-2.0-gguf",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Telugu-LLM-Labs/Indic-gemma-7b-finetuned-sft-Navarasa-2.0-gguf" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Telugu-LLM-Labs/Indic-gemma-7b-finetuned-sft-Navarasa-2.0-gguf",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use Telugu-LLM-Labs/Indic-gemma-7b-finetuned-sft-Navarasa-2.0-gguf with Ollama:
```
ollama run hf.co/Telugu-LLM-Labs/Indic-gemma-7b-finetuned-sft-Navarasa-2.0-gguf:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use Telugu-LLM-Labs/Indic-gemma-7b-finetuned-sft-Navarasa-2.0-gguf with Docker Model Runner:
```
docker model run hf.co/Telugu-LLM-Labs/Indic-gemma-7b-finetuned-sft-Navarasa-2.0-gguf:Q4_K_M
```

Lemonade

How to use Telugu-LLM-Labs/Indic-gemma-7b-finetuned-sft-Navarasa-2.0-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Telugu-LLM-Labs/Indic-gemma-7b-finetuned-sft-Navarasa-2.0-gguf:Q4_K_M

Run and chat with the model

lemonade run user.Indic-gemma-7b-finetuned-sft-Navarasa-2.0-gguf-Q4_K_M

List all available models

lemonade list

Atomic Chat

Indic-gemma-7b-finetuned-sft-Navarasa-2.0-gguf

This is GGUF format model of Indic-gemma-7b-finetuned-sft-Navarasa-2.0 which can be used with LM-Studio and ollama.\

Developers:

The model is a collaborative effort by Ravi Theja and Ramsri Goutham. Feel free to DM either of us if you have any questions.

Downloads last month: 133

GGUF

Model size

9B params

Architecture

gemma

Hardware compatibility

4-bit

5-bit

View +1 variant

Model tree for Telugu-LLM-Labs/Indic-gemma-7b-finetuned-sft-Navarasa-2.0-gguf

Base model

google/gemma-7b

Quantized

(22)

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Navarasa 2.0 Models

Collection

Collection of models Navarasa 2.0 Models finetuned with Gemma on 15 Indian languages • 4 items • Updated Mar 2 • 28