Instructions to use MayaPH/FinOPT-Washington with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MayaPH/FinOPT-Washington with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="MayaPH/FinOPT-Washington")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("MayaPH/FinOPT-Washington") model = AutoModelForCausalLM.from_pretrained("MayaPH/FinOPT-Washington") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use MayaPH/FinOPT-Washington with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MayaPH/FinOPT-Washington" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MayaPH/FinOPT-Washington", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/MayaPH/FinOPT-Washington
- SGLang
How to use MayaPH/FinOPT-Washington 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 "MayaPH/FinOPT-Washington" \ --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": "MayaPH/FinOPT-Washington", "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 "MayaPH/FinOPT-Washington" \ --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": "MayaPH/FinOPT-Washington", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use MayaPH/FinOPT-Washington with Docker Model Runner:
docker model run hf.co/MayaPH/FinOPT-Washington
Adding Evaluation Results
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FinOPT-Washington is an AI language model trained by Maya Philippines. It is provided "as is" without warranty of any kind, express or implied. The model developers and Maya Philippines shall not be liable for any direct or indirect damages arising from the use of this model.
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## Acknowledgments
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The development of FinOPT-Washington was made possible by Maya Philippines and the curation and creation of the financial question-answering dataset.
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FinOPT-Washington is an AI language model trained by Maya Philippines. It is provided "as is" without warranty of any kind, express or implied. The model developers and Maya Philippines shall not be liable for any direct or indirect damages arising from the use of this model.
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## Acknowledgments
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The development of FinOPT-Washington was made possible by Maya Philippines and the curation and creation of the financial question-answering dataset.
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_MayaPH__FinOPT-Washington)
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| Metric | Value |
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| Avg. | 24.87 |
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| ARC (25-shot) | 25.17 |
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| HellaSwag (10-shot) | 26.25 |
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| MMLU (5-shot) | 24.83 |
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| TruthfulQA (0-shot) | 45.8 |
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| Winogrande (5-shot) | 51.07 |
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| GSM8K (5-shot) | 0.0 |
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| DROP (3-shot) | 1.0 |
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