Ultra Series
Collection
UltraLM, UltraRM and UltraCM. • 10 items • Updated • 11
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 "openbmb/UltraLM-13b" \
--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": "openbmb/UltraLM-13b",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'Quick Links
UltraLM-13b
This is UltraLM-13b delta weights, a chat language model trained upon UltraChat
Model Details
Model Description
The model is fine-tuned based on LLaMA-13b with a multi-turn chat-format template as below
User: instruction 1<eos_token>
Assistant: response 1<eos_token>
User: instruction 2<eos_token>
Assistant: response 2<eos_token>
...
- License: UltraLM is based on LLaMA and should be used under LLaMA's model license.
- Finetuned from model: LLaMA-13b
- Finetuned on data: UltraChat
Model Sources
Uses
To use this model, you need to recover the full model from the delta weights and perform inference following the template below:
[Optional]User: system prompt<eos_token>
User: user input<eos_token>
Assistant:
- Downloads last month
- 185
Install from pip and serve model
# Install SGLang from pip: pip install sglang# Start the SGLang server: python3 -m sglang.launch_server \ --model-path "openbmb/UltraLM-13b" \ --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": "openbmb/UltraLM-13b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'