Instructions to use openbmb/UltraLM-13b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openbmb/UltraLM-13b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="openbmb/UltraLM-13b")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("openbmb/UltraLM-13b") model = AutoModelForCausalLM.from_pretrained("openbmb/UltraLM-13b") - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use openbmb/UltraLM-13b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "openbmb/UltraLM-13b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "openbmb/UltraLM-13b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/openbmb/UltraLM-13b
- SGLang
How to use openbmb/UltraLM-13b 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 "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 }'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 "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 }' - Docker Model Runner
How to use openbmb/UltraLM-13b with Docker Model Runner:
docker model run hf.co/openbmb/UltraLM-13b
metadata
datasets:
- stingning/ultrachat
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: