Text Generation
Transformers
Safetensors
English
llama
Eval Results (legacy)
text-generation-inference
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
This is based on Qwen, and it is 72B not 70b
#6
by ehartford - opened
Can you please change the name to 72b instead of 70b because this is a Qwen model, and calling it 70b makes people think it's a Llama model.
Hi ! @ehartford That is True the model is based on Qwen model and the correct number of parameters is shown on the model card.
The current version of the model is for testing (as mentioned on the model card) we are just validating our hypothesis and hopefully much better model based on Qwen will be released soon, with no issues with the name or other things