Instructions to use luke-thorburn/suggest-reasons-bias-only with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use luke-thorburn/suggest-reasons-bias-only with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="luke-thorburn/suggest-reasons-bias-only")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("luke-thorburn/suggest-reasons-bias-only") model = AutoModelForCausalLM.from_pretrained("luke-thorburn/suggest-reasons-bias-only") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use luke-thorburn/suggest-reasons-bias-only with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "luke-thorburn/suggest-reasons-bias-only" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "luke-thorburn/suggest-reasons-bias-only", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/luke-thorburn/suggest-reasons-bias-only
- SGLang
How to use luke-thorburn/suggest-reasons-bias-only 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 "luke-thorburn/suggest-reasons-bias-only" \ --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": "luke-thorburn/suggest-reasons-bias-only", "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 "luke-thorburn/suggest-reasons-bias-only" \ --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": "luke-thorburn/suggest-reasons-bias-only", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use luke-thorburn/suggest-reasons-bias-only with Docker Model Runner:
docker model run hf.co/luke-thorburn/suggest-reasons-bias-only
Use Docker
docker model run hf.co/luke-thorburn/suggest-reasons-bias-onlyGenerate reasons that support a claim
This model is a version of gpt-neo-2.7B, where some parameters (only the bias parameters, not weights) have been finetuned on the task of generating reasons that support a claim, optionally given some example reasons. It was trained as part of a University of Melbourne research project evaluating how large language models can best be optimized to perform argumentative reasoning tasks.
Code used for optimization and evaluation can be found in the project GitHub repository. A paper reporting on model evaluation is currently under review.
Prompt Template
List reasons why: [original claim]
Reasons:
* [reason 1]
* [reason 2]
...
* [reason n]
* [generated reason]
Dataset
The parameters were finetuned using argument maps scraped from the crowdsourced argument-mapping platform Kialo.
Limitations and Biases
The model is a finetuned version of gpt-neo-2.7B, so likely has many of the same limitations and biases. Additionally, note that while the goal of the model is to produce coherent and valid reasoning, many generated model outputs will be illogical or nonsensical and should not be relied upon.
Acknowledgements
This research was funded by the Australian Department of Defence and the Office of National Intelligence under the AI for Decision Making Program, delivered in partnership with the Defence Science Institute in Victoria, Australia.
- Downloads last month
- 6
Install from pip and serve model
# Install vLLM from pip: pip install vllm# Start the vLLM server: vllm serve "luke-thorburn/suggest-reasons-bias-only"# Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "luke-thorburn/suggest-reasons-bias-only", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'