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 Settings
- 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
A Bang Up Job
We needed another big win for the open source community. Thanks for taking a big risk for everyone.
You added several innovations here. Would you consider adding the 1.58 bit architecture in the future? I'm curious to know if it was considered.
I haven't seen ternary bits applied to an SSM yet, let alone a hybrid. Would be interesting to see if it's compatiable.
Imagine the efficiency with MoE + Mamba + 1.58 bit π³
Maybe like make higher parameters version too, I imagine 1.58 bit version could be same memory footprint and speed is 50B version while being a lot more parameters if not double. Then I guess it would be how could we shrink that somehow even like quantization already let's us do with fp16 models