Instructions to use ashercn97/blanked-v011 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ashercn97/blanked-v011 with Transformers:

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

pipe = pipeline("text-generation", model="ashercn97/blanked-v011")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ashercn97/blanked-v011")
model = AutoModelForCausalLM.from_pretrained("ashercn97/blanked-v011", 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 ashercn97/blanked-v011 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ashercn97/blanked-v011"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ashercn97/blanked-v011",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ashercn97/blanked-v011

SGLang

How to use ashercn97/blanked-v011 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 "ashercn97/blanked-v011" \
    --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": "ashercn97/blanked-v011",
		"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 "ashercn97/blanked-v011" \
        --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": "ashercn97/blanked-v011",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ashercn97/blanked-v011 with Docker Model Runner:
```
docker model run hf.co/ashercn97/blanked-v011
```

blanked-v011

File size: 776 Bytes

2e67299

from typing import Dict, List, Any
from transformers import pipeline

class EndpointHandler:
    def __init__(self, path=""):
        self.pipeline = pipeline("text-generation", model=path)

    def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
        """
         data args:
              inputs (:obj: `str`)
              date (:obj: `str`)
        Return:
              A :obj:`list` | `dict`: will be serialized and returned
        """
        # get inputs
        template = """
        INSTRUCTION
        {}
        RESPONSE
        {}
        """
        instruction = data.pop("instruction", data)
        inputs = template.format(instruction, "")
        # run normal prediction
        prediction = self.pipeline(inputs)
        return prediction