Instructions to use fpadovani/ind-latn-10mb-ppt-Dp-10mb_seed10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use fpadovani/ind-latn-10mb-ppt-Dp-10mb_seed10 with Transformers:

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

pipe = pipeline("text-generation", model="fpadovani/ind-latn-10mb-ppt-Dp-10mb_seed10")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("fpadovani/ind-latn-10mb-ppt-Dp-10mb_seed10")
model = AutoModelForCausalLM.from_pretrained("fpadovani/ind-latn-10mb-ppt-Dp-10mb_seed10")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use fpadovani/ind-latn-10mb-ppt-Dp-10mb_seed10 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "fpadovani/ind-latn-10mb-ppt-Dp-10mb_seed10"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "fpadovani/ind-latn-10mb-ppt-Dp-10mb_seed10",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/fpadovani/ind-latn-10mb-ppt-Dp-10mb_seed10

SGLang

How to use fpadovani/ind-latn-10mb-ppt-Dp-10mb_seed10 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 "fpadovani/ind-latn-10mb-ppt-Dp-10mb_seed10" \
    --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": "fpadovani/ind-latn-10mb-ppt-Dp-10mb_seed10",
		"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 "fpadovani/ind-latn-10mb-ppt-Dp-10mb_seed10" \
        --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": "fpadovani/ind-latn-10mb-ppt-Dp-10mb_seed10",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use fpadovani/ind-latn-10mb-ppt-Dp-10mb_seed10 with Docker Model Runner:
```
docker model run hf.co/fpadovani/ind-latn-10mb-ppt-Dp-10mb_seed10
```

ind-latn-10mb-ppt-Dp-10mb_seed10 / checkpoint-2500 /model.safetensors

fpadovani

Training checkpoint: step 2500

0098f62 verified 14 days ago

Download

History Blame

78.2 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:: 22326aa5171f52d72bfac2c166ccb2e4517782699652a2d2b8a1b43cfbe95a62
Size of remote file:: 78.2 MB
SHA256:: cf614f31ff27aa7000b1fa018ad5f6f65f582e76eec4c6783e2d1684b0e869e8

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