Instructions to use Aleph-Alpha/Kolibri-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Aleph-Alpha/Kolibri-1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Aleph-Alpha/Kolibri-1") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Aleph-Alpha/Kolibri-1") model = AutoModelForCausalLM.from_pretrained("Aleph-Alpha/Kolibri-1", device_map="auto") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=256) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Aleph-Alpha/Kolibri-1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Aleph-Alpha/Kolibri-1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Aleph-Alpha/Kolibri-1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Aleph-Alpha/Kolibri-1
- SGLang
How to use Aleph-Alpha/Kolibri-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 "Aleph-Alpha/Kolibri-1" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Aleph-Alpha/Kolibri-1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "Aleph-Alpha/Kolibri-1" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Aleph-Alpha/Kolibri-1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Aleph-Alpha/Kolibri-1 with Docker Model Runner:
docker model run hf.co/Aleph-Alpha/Kolibri-1
transformers support for Kolibri1ForCausalLM
Do you plan to add huggingface/transformers support for Kolibri1ForCausalLM?
As a lot of other tools depend on it that would be great. Would like to try to make some quants - NVFP4 for example to run it on a RTX PRO 6000.
I just opened an issue over there: https://github.com/huggingface/transformers/issues/49281
I the meantime I will try to get my hands on some hardware that is capable to run it in FP8.
Thanks for opening the issue! Yes, we're working on native transformers support and are aiming to have it next week. Looking forward to your fp4 quant!
Excellent. Looking forward to that!
Someone (@audreyt ) did already made a experimental NVFP4 quant:
https://huggingface.co/audreyt/Kolibri-1-NVFP4-W4A16
Will give it a try right away on a RTX PRO 6000. π Had a successful deployment on my Spark yesterday already. tool-eval-bench results were pretty impressive. Will open another discussion for that.
Hey, quick update: There's a PR ready for review. Big thanks also to @LucaMotz for starting to work on this.