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

Libraries

How to use Chayaaaaa/without_chat_RakutenAI-7B_task_arithmetic with Transformers:

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

pipe = pipeline("text-generation", model="Chayaaaaa/without_chat_RakutenAI-7B_task_arithmetic")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Chayaaaaa/without_chat_RakutenAI-7B_task_arithmetic")
model = AutoModelForCausalLM.from_pretrained("Chayaaaaa/without_chat_RakutenAI-7B_task_arithmetic", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Chayaaaaa/without_chat_RakutenAI-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_chat_RakutenAI-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_chat_RakutenAI-7B_task_arithmetic",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Chayaaaaa/without_chat_RakutenAI-7B_task_arithmetic

SGLang

How to use Chayaaaaa/without_chat_RakutenAI-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_chat_RakutenAI-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_chat_RakutenAI-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_chat_RakutenAI-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_chat_RakutenAI-7B_task_arithmetic",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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

without_chat_RakutenAI-7B_task_arithmetic / model-00011-of-00016.safetensors

Chayaaaaa

Upload folder using huggingface_hub

af4d5c5 verified over 2 years ago

Download

History Blame Contribute Delete

998 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:: 577254149229f54ae7a4afe70e3782e8bc7a6e7deaf3ef580287ce791b43e740
Size of remote file:: 998 MB
SHA256:: f8e602003505c98f7fd46ae23e512d0ea353ef12882516ebc6d8139aaf813e39

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