Instructions to use Chayaaaaa/without_japanese_apart_from_nekomata-7b_task_arithmetic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Chayaaaaa/without_japanese_apart_from_nekomata-7b_task_arithmetic with Transformers:

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

pipe = pipeline("text-generation", model="Chayaaaaa/without_japanese_apart_from_nekomata-7b_task_arithmetic", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Chayaaaaa/without_japanese_apart_from_nekomata-7b_task_arithmetic", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Chayaaaaa/without_japanese_apart_from_nekomata-7b_task_arithmetic with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Chayaaaaa/without_japanese_apart_from_nekomata-7b_task_arithmetic"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Chayaaaaa/without_japanese_apart_from_nekomata-7b_task_arithmetic",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Chayaaaaa/without_japanese_apart_from_nekomata-7b_task_arithmetic

SGLang

How to use Chayaaaaa/without_japanese_apart_from_nekomata-7b_task_arithmetic 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 "Chayaaaaa/without_japanese_apart_from_nekomata-7b_task_arithmetic" \
    --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": "Chayaaaaa/without_japanese_apart_from_nekomata-7b_task_arithmetic",
		"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 "Chayaaaaa/without_japanese_apart_from_nekomata-7b_task_arithmetic" \
        --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": "Chayaaaaa/without_japanese_apart_from_nekomata-7b_task_arithmetic",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Chayaaaaa/without_japanese_apart_from_nekomata-7b_task_arithmetic with Docker Model Runner:
```
docker model run hf.co/Chayaaaaa/without_japanese_apart_from_nekomata-7b_task_arithmetic
```

without_japanese_apart_from_nekomata-7b_task_arithmetic / model-00006-of-00016.safetensors

Chayaaaaa

Upload folder using huggingface_hub

e71c50d verified over 2 years ago

Download

History Blame Contribute Delete

990 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:: cd4b020493d3e993b0f1176ecf3f947075c1a273b12208ee06a551b49f182a26
Size of remote file:: 990 MB
SHA256:: 4eb3430566379b7e875f08c0dd17cc48585b7dae0d8f4193db1c0fbe45ad78f4

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