Instructions to use UnimeType/Transliteration-4B-Safetensors with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use UnimeType/Transliteration-4B-Safetensors with Transformers:

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

pipe = pipeline("text-generation", model="UnimeType/Transliteration-4B-Safetensors")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("UnimeType/Transliteration-4B-Safetensors")
model = AutoModelForCausalLM.from_pretrained("UnimeType/Transliteration-4B-Safetensors", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use UnimeType/Transliteration-4B-Safetensors with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "UnimeType/Transliteration-4B-Safetensors"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "UnimeType/Transliteration-4B-Safetensors",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/UnimeType/Transliteration-4B-Safetensors

SGLang

How to use UnimeType/Transliteration-4B-Safetensors 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 "UnimeType/Transliteration-4B-Safetensors" \
    --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": "UnimeType/Transliteration-4B-Safetensors",
		"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 "UnimeType/Transliteration-4B-Safetensors" \
        --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": "UnimeType/Transliteration-4B-Safetensors",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use UnimeType/Transliteration-4B-Safetensors with Docker Model Runner:
```
docker model run hf.co/UnimeType/Transliteration-4B-Safetensors
```

Transliteration-4B-Safetensors / SHA256SUMS

losdwind

Document Q8_0 GGUF download; keep all model weights unchanged

8b78434 verified 9 days ago

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	bde43dab9bf6604b8f96a9d848163805d9323590de5e0bc75c76640979cd0846 PROVENANCE.json
	a4739b191e500d57c001f6e57a34326337fcd76a6583f120ce67c725a0672746 README.md
	ad22cb6365e58696ffc10f1fbfc5f09b8762d0486df899e8f18b0491906e0b21 chat_template.jinja
	b95428ba484bb9e64a946f787bfce1d7b813d781753bd505506ffe509e4ad11c config.json
	f7f04a55c2d5b60ed2ed8fb69cca1bd5e0f1ae55e21122893bc65d4468011153 generation_config.json
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	87a7830d63fcf43bf241c3c5242e96e62dd3fdc29224ca26fed8ea333db72de4 tokenizer.json
	970d4874c33b6624b3b979e5c3e55b777e77c1e6a115e985b202c8225fc81afd tokenizer_config.json