shikras/shikra-7b-delta-v1-0708 · Hugging Face (original) (raw)

Use a pipeline as a high-level helper

from transformers import pipeline
pipe = pipeline("text-generation", model="shikras/shikra-7b-delta-v1-0708")

Load model directly

from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("shikras/shikra-7b-delta-v1-0708", dtype="auto")

Install from pip and serve model

Install vLLM from pip:

pip install vllm

Start the vLLM server:

vllm serve "shikras/shikra-7b-delta-v1-0708"

Call the server using curl (OpenAI-compatible API):

curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "shikras/shikra-7b-delta-v1-0708",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'

Use Docker

docker model run hf.co/shikras/shikra-7b-delta-v1-0708

Install from pip and serve model

Install SGLang from pip:

pip install sglang

Start the SGLang server:

python3 -m sglang.launch_server \
--model-path "shikras/shikra-7b-delta-v1-0708" \
--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": "shikras/shikra-7b-delta-v1-0708",
"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=" \
--ipc=host \
lmsysorg/sglang:latest \
python3 -m sglang.launch_server \
--model-path "shikras/shikra-7b-delta-v1-0708" \
--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": "shikras/shikra-7b-delta-v1-0708",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'