Instructions to use viswamaicoe/nanochat-telugu-560M-mid-v1-akshara with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use viswamaicoe/nanochat-telugu-560M-mid-v1-akshara with Transformers:

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

pipe = pipeline("text-generation", model="viswamaicoe/nanochat-telugu-560M-mid-v1-akshara")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("viswamaicoe/nanochat-telugu-560M-mid-v1-akshara")
model = AutoModelForCausalLM.from_pretrained("viswamaicoe/nanochat-telugu-560M-mid-v1-akshara", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use viswamaicoe/nanochat-telugu-560M-mid-v1-akshara with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "viswamaicoe/nanochat-telugu-560M-mid-v1-akshara"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "viswamaicoe/nanochat-telugu-560M-mid-v1-akshara",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/viswamaicoe/nanochat-telugu-560M-mid-v1-akshara

SGLang

How to use viswamaicoe/nanochat-telugu-560M-mid-v1-akshara 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 "viswamaicoe/nanochat-telugu-560M-mid-v1-akshara" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "viswamaicoe/nanochat-telugu-560M-mid-v1-akshara",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "viswamaicoe/nanochat-telugu-560M-mid-v1-akshara" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "viswamaicoe/nanochat-telugu-560M-mid-v1-akshara",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use viswamaicoe/nanochat-telugu-560M-mid-v1-akshara with Docker Model Runner:
```
docker model run hf.co/viswamaicoe/nanochat-telugu-560M-mid-v1-akshara
```

nanochat-telugu-560M-mid-v1-akshara / model.safetensors

biswa921

Upload folder using huggingface_hub

deedfd2 verified 8 months ago

Download

History Blame Contribute Delete

1.12 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:: 142a9424176df08707e3c7cd6d991e4cc81da70ded3994daea35f147b55130a5
Size of remote file:: 1.12 GB
SHA256:: a126c2bccf36dc3083ce674ef016b0a24c34322c8c52b9a26bfda42d93908a56

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