Instructions to use blueapple8259/squirrel-c4-ko-cleaned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use blueapple8259/squirrel-c4-ko-cleaned with Transformers:

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

pipe = pipeline("text-generation", model="blueapple8259/squirrel-c4-ko-cleaned")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("blueapple8259/squirrel-c4-ko-cleaned")
model = AutoModelForCausalLM.from_pretrained("blueapple8259/squirrel-c4-ko-cleaned")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use blueapple8259/squirrel-c4-ko-cleaned with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "blueapple8259/squirrel-c4-ko-cleaned"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "blueapple8259/squirrel-c4-ko-cleaned",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/blueapple8259/squirrel-c4-ko-cleaned

SGLang

How to use blueapple8259/squirrel-c4-ko-cleaned 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 "blueapple8259/squirrel-c4-ko-cleaned" \
    --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": "blueapple8259/squirrel-c4-ko-cleaned",
		"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 "blueapple8259/squirrel-c4-ko-cleaned" \
        --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": "blueapple8259/squirrel-c4-ko-cleaned",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use blueapple8259/squirrel-c4-ko-cleaned with Docker Model Runner:
```
docker model run hf.co/blueapple8259/squirrel-c4-ko-cleaned
```

c4-ko-cleaned데이터셋 테스트용 모델입니다.

모델 정보

vocab size: 52000

max position embeddings: 512

hidden size: 512

num attention heads: 16

num key value heads: 4

num hidden layers: 2

Downloads last month: 6

Safetensors

Model size

88.4M params

Tensor type

F32

blueapple8259
/

squirrel-c4-ko-cleaned

모델 정보

Dataset used to train blueapple8259/squirrel-c4-ko-cleaned