Instructions to use ai21labs/Jamba-v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ai21labs/Jamba-v0.1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ai21labs/Jamba-v0.1", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("ai21labs/Jamba-v0.1", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("ai21labs/Jamba-v0.1", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use ai21labs/Jamba-v0.1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ai21labs/Jamba-v0.1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ai21labs/Jamba-v0.1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ai21labs/Jamba-v0.1
- SGLang
How to use ai21labs/Jamba-v0.1 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 "ai21labs/Jamba-v0.1" \ --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": "ai21labs/Jamba-v0.1", "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 "ai21labs/Jamba-v0.1" \ --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": "ai21labs/Jamba-v0.1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ai21labs/Jamba-v0.1 with Docker Model Runner:
docker model run hf.co/ai21labs/Jamba-v0.1
Any release plans for the 7b jamba model without MoE?
Congratulations on the amazing work, and thank you for sharing it.
Currently, it's possible to use jamba as 4bit weight only at Google Colab(using single A100) due to MoE layers, but this comes with significant performance limitations, making it highly likely that the MoE layers won't function as intended.
I'm curious if there are any plans for releasing a 7b model without MoE layers.
Thank you @danielpark !
The model can fit on a single A100 with 80GB memory.
We intend to release smaller variations of Jamba that were used as indicative experiments (not fully trained)
Thank you for your prompt and kind assistance. I was impressed by the fast and impressive architecture of AI21. I wanted to test it on Colab, but since getting nearly 80GB of A100 allocation is like winning the lottery, I had to give up. Other instances are either too expensive or too difficult to use, so I couldn't consider them.
I was planning to use a specialized model once I got the 80GB, but it would be great if AI21 could release the weights of Jamba 7B initialized as quickly as possible.
I'm preparing research based on the Jamba architecture.
Thank you.