How to use from
SGLangUse 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 "iamplus/bloomz-7b1-v4" \
--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": "iamplus/bloomz-7b1-v4",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'Quick Links
Instruction Tuned Bloomz-7B1 Model on Stanford Alpaca-2 Instruction Tuning dataset (outputs from ChatGPT) (52k data) using Colossal AI
Base Model: bigscience/bloomz-7b1
Training Details :
- Epochs: 5
- Batch Size : 16 instantaneous per device x 1 gradient accumulation steps x 8 gpus = 128
- Max Length : 1024
- Weight Decay : 0
- Learning Rate : 2e-5
- Learning Rate Scheduler Type : Cosine
- Number of warmup steps : 30
- Machine : 8xA100 80GB
Dataset Details :
Dataset : iamplus/Instruction_Tuning
Files :
- stanford_alpaca_it_v2.csv
- Downloads last month
- 2
Install from pip and serve model
# Install SGLang from pip: pip install sglang# Start the SGLang server: python3 -m sglang.launch_server \ --model-path "iamplus/bloomz-7b1-v4" \ --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": "iamplus/bloomz-7b1-v4", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'