Instructions to use Telugu-LLM-Labs/Indic-gemma-7b-finetuned-sft-Navarasa 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 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")

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Telugu-LLM-Labs/Indic-gemma-7b-finetuned-sft-Navarasa 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"
# 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",
		"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

SGLang

How to use Telugu-LLM-Labs/Indic-gemma-7b-finetuned-sft-Navarasa 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" \
    --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",
		"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" \
        --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",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Telugu-LLM-Labs/Indic-gemma-7b-finetuned-sft-Navarasa with Docker Model Runner:
```
docker model run hf.co/Telugu-LLM-Labs/Indic-gemma-7b-finetuned-sft-Navarasa
```

Indic-gemma-7b-finetuned-sft-Navarasa / adapter_model.safetensors

Telugu-LLM-Labs

Upload model trained with Unsloth

5eb19ac verified over 2 years ago

Download

History Blame Contribute Delete

800 MB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 834160f50a2191da417d89402df9c3bf36c785afb9567c0ce6b2ad2c006c1cec
Size of remote file:: 800 MB
SHA256:: f24191570da7c78c4f0abfefcedfdc29892627586c860d1ee12ab68b51fdc88d

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.