Instructions to use llmware/slim-xsum with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use llmware/slim-xsum with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="llmware/slim-xsum", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("llmware/slim-xsum", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use llmware/slim-xsum with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "llmware/slim-xsum" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "llmware/slim-xsum", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/llmware/slim-xsum
- SGLang
How to use llmware/slim-xsum 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 "llmware/slim-xsum" \ --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": "llmware/slim-xsum", "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 "llmware/slim-xsum" \ --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": "llmware/slim-xsum", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use llmware/slim-xsum with Docker Model Runner:
docker model run hf.co/llmware/slim-xsum
Update README.md
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README.md
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function = "classify"
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params = "xsum"
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text = "
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prompt = "<human>: " + text + "\n" + f"<{function}> {params} </{function}>\n<bot>:"
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function = "classify"
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params = "xsum"
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text = "DeepMind, the UK-based AI lab owned by Google’s parent company Alphabet, has developed an AI system called AlphaGeometry that can solve complex geometry problems close to human Olympiad gold medalists. In a new paper in Nature, DeepMind revealed that AlphaGeometry was able to solve 25 out of 30 benchmark geometry problems from past International Mathematical Olympiad (IMO) competitions within the standard time limits. This nearly matches the average score of 26 problems solved by human gold medalists on the same tests. The AI system combines a neural language model with a rule-bound deduction engine, providing a synergy that enables the system to find solutions to complex geometry theorems. AlphaGeometry took a revolutionary approach to synthetic data generation by creating one billion random diagrams of geometric objects and deriving relationships between points and lines in each diagram. This process – termed “symbolic deduction and traceback” – resulted in a final training dataset of 100 million unique examples, providing a rich source for training the AI system."
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prompt = "<human>: " + text + "\n" + f"<{function}> {params} </{function}>\n<bot>:"
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