Instructions to use MTSAIR/MultiVerse_70B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MTSAIR/MultiVerse_70B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="MTSAIR/MultiVerse_70B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("MTSAIR/MultiVerse_70B") model = AutoModelForCausalLM.from_pretrained("MTSAIR/MultiVerse_70B") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use MTSAIR/MultiVerse_70B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MTSAIR/MultiVerse_70B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MTSAIR/MultiVerse_70B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/MTSAIR/MultiVerse_70B
- SGLang
How to use MTSAIR/MultiVerse_70B 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 "MTSAIR/MultiVerse_70B" \ --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": "MTSAIR/MultiVerse_70B", "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 "MTSAIR/MultiVerse_70B" \ --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": "MTSAIR/MultiVerse_70B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use MTSAIR/MultiVerse_70B with Docker Model Runner:
docker model run hf.co/MTSAIR/MultiVerse_70B
License
Hi, thanks for releasing this model. What is the MTS AI license? (The LICENSE file is empty)
Thank you!
Hi @mrfakename thanks for your interest, Still under discussion hopefully we will release it soon, but for now you can use the model for research purposes only
Hi @mrfakename thanks for your interest, Still under discussion hopefully we will release it soon, but for now you can use the model for research purposes only
Hey, can we use this Model in Hugging Chat.
https://huggingface.co/chat/
Hugging chat has many Models like Command R+, Llama3, Mixtrail 8x7B, etc.
Multiverse 70B is a very good model.
We love to see this on Hugging Chat.
Thank You!
@mrfakename @KingNish We would like to open source the model on apache or MIT but hence it uses Qwen as a base model it inherits the license restrictions. If Qwen is ok to use on Hugging Chat then yes please go a head. and thanks for your interest ))
@ammarali32 Thank you for your response.
According to clause 2 of licence
- Grant of Rights
You are granted a non-exclusive, worldwide, non-transferable and royalty-free limited license under Alibaba Cloud's intellectual property or other rights owned by Us embodied in the Materials to use, reproduce, distribute, copy, create derivative works of, and make modifications to the Materials.
Means, we can use this model on Hugging Chat.
Thank You!
cc @nsarrazin