Optimum RBLN
🤗 Optimum RBLN provides an interface between HuggingFace libraries (Transformers, Diffusers) and RBLN NPUs, including ATOM and REBEL.
This library enables seamless integration between the HuggingFace ecosystem and RBLN NPUs through a comprehensive toolkit for model loading and inference across single and multi-NPU environments. While we maintain a list of officially validated models and tasks, users can easily adapt other models and tasks with minimal modifications.
Key Features
🚀 High Performance Inference
- Optimized model execution on RBLN NPUs through RBLN SDK compilation
- Support for both single and multi-NPU inference
- Integrated with RBLN Runtime for optimal performance
🔧 Easy Integration
- Seamless compatibility with HuggingFace Model Hub
- Drop-in replacement for existing HuggingFace pipelines
- Minimal code changes required for NPU acceleration
Seamless Replacement for Existing HuggingFace Code
Swap the HuggingFace class for its RBLN counterpart. Passing a HuggingFace model id compiles the
model for the NPU on the first run; passing a directory of previously compiled artifacts loads them
directly, so compilation is skipped.
- from diffusers import StableDiffusionXLPipeline
+ from optimum.rbln import RBLNStableDiffusionXLPipeline
# Load model
model_id = "stabilityai/stable-diffusion-xl-base-1.0"
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
- pipe = StableDiffusionXLPipeline.from_pretrained(model_id)
+ pipe = RBLNStableDiffusionXLPipeline.from_pretrained(model_id)
# Generate image
image = pipe(prompt).images[0]
# Save image result
image.save("image.png")
+ # (Optional) Save compiled artifacts to skip the compilation step in future runs
+ pipe.save_pretrained("compiled_sdxl")
Compile Ahead of Time with the CLI
Instead of compiling inside your script, you can compile a model up front with the optimum-rbln-cli
command and load the resulting artifacts later:
# Compile and save the artifacts to ./compiled_qwen3
optimum-rbln-cli --model-id Qwen/Qwen3-4B -o ./compiled_qwen3 \
--max_seq_len 8192 --batch_size 1 --num_devices 4
from optimum.rbln import RBLNQwen3ForCausalLM
# Load the compiled artifacts (no recompilation)
model = RBLNQwen3ForCausalLM.from_pretrained("./compiled_qwen3")
Useful CLI helpers:
optimum-rbln-cli --list-classes # list available RBLN classes
optimum-rbln-cli --class RBLNQwen3ForCausalLM --show-rbln-config # show accepted rbln_config keys
optimum-rbln-cli --examples # show more usage examples
Documentation
Check out the documentation of Optimum RBLN for more advanced usage.
Getting Started
Note: The
rebel-compilerlibrary, which is required for runningoptimum-rbln, is only available for approved users. Please refer to the installation guide for instructions on accessing and installingrebel-compiler.
Install from PyPI
To install the latest release of this package:
pip install optimum-rbln --extra-index-url https://download.pytorch.org/whl/cpu
Install from source
Prerequisites
The below command installs optimum-rbln along with its dependencies.
git clone https://github.com/rbln-sw/optimum-rbln.git
cd optimum-rbln
./scripts/uv-sync.sh
Need Help?
- Join discussions and get answers in our Developer Community
- Contact maintainers at support@rebellions.ai
1 maintainer
Metadata
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Release files for optimum-rbln 0.12.0
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|---|---|---|---|---|
| optimum_rbln-0.12.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 1.4 MB
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