Instructions to use allenai/scitulu-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use allenai/scitulu-7b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="allenai/scitulu-7b")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("allenai/scitulu-7b") model = AutoModelForCausalLM.from_pretrained("allenai/scitulu-7b") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use allenai/scitulu-7b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "allenai/scitulu-7b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "allenai/scitulu-7b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/allenai/scitulu-7b
- SGLang
How to use allenai/scitulu-7b 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 "allenai/scitulu-7b" \ --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": "allenai/scitulu-7b", "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 "allenai/scitulu-7b" \ --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": "allenai/scitulu-7b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use allenai/scitulu-7b with Docker Model Runner:
docker model run hf.co/allenai/scitulu-7b
Model Card for SciTulu 7B
SciTulu is a collection of instruction-following language models targeting scientific literature understanding use cases. Starting from the Tulu v2 7B model, SciTulu is trained on a mix of science-specific demonstrations from the SciRIFF dataset, together with general-domain instructions from the Tulu v2 SFT mix. SciTulu 7B achives a 28.1% average improvement over Tulu v2 7B on nine held-out scientific literature understanding tasks. More information can be found in our preprint: SciRIFF: A Resource to Enhance Language Model Instruction-Following over Scientific Literature.
Training and evaluation code for SciTulu is available in our GitHub repository: https://github.com/allenai/SciRIFF.
See the Tulu model card for more information on potential risks, biases, and limitations.
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