openbmb/UltraChat
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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")How to use openbmb/UltraLM-13b with vLLM:
# 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
}'docker model run hf.co/openbmb/UltraLM-13b
How to use openbmb/UltraLM-13b with SGLang:
# 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
}'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
}'How to use openbmb/UltraLM-13b with Docker Model Runner:
docker model run hf.co/openbmb/UltraLM-13b
This is UltraLM-13b delta weights, a chat language model trained upon UltraChat
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>
...
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:
docker model run hf.co/openbmb/UltraLM-13b