Instructions to use autopilot-ai/Indic-sentence-completion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use autopilot-ai/Indic-sentence-completion with Transformers:

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

pipe = pipeline("text-generation", model="autopilot-ai/Indic-sentence-completion")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("autopilot-ai/Indic-sentence-completion")
model = AutoModelForCausalLM.from_pretrained("autopilot-ai/Indic-sentence-completion")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use autopilot-ai/Indic-sentence-completion with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "autopilot-ai/Indic-sentence-completion"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "autopilot-ai/Indic-sentence-completion",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/autopilot-ai/Indic-sentence-completion

SGLang

How to use autopilot-ai/Indic-sentence-completion 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 "autopilot-ai/Indic-sentence-completion" \
    --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": "autopilot-ai/Indic-sentence-completion",
		"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 "autopilot-ai/Indic-sentence-completion" \
        --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": "autopilot-ai/Indic-sentence-completion",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use autopilot-ai/Indic-sentence-completion with Docker Model Runner:
```
docker model run hf.co/autopilot-ai/Indic-sentence-completion
```

Indic-sentence-completion / pytorch_model.bin

Jayveersinh-Raj

Indic language support sentence completion language model

9a753fc almost 3 years ago

download

history blame contribute delete

6.01 GB

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:: b9ad1f8b5f8f513caa6cc5374ac9c5cb7a0fa0c4568186cb7b21f0ead8ab0e91
Size of remote file:: 6.01 GB
SHA256:: 7cae1c385f03f12a3e81969e486fd1a60ca512d4adfbc8a43443c4fb3618bc0f

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