Text Generation
Transformers
PyTorch
Safetensors
English
llama
finance
Eval Results (legacy)
text-generation-inference
Instructions to use AdaptLLM/finance-chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AdaptLLM/finance-chat with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="AdaptLLM/finance-chat")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("AdaptLLM/finance-chat") model = AutoModelForCausalLM.from_pretrained("AdaptLLM/finance-chat") - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use AdaptLLM/finance-chat with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "AdaptLLM/finance-chat" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "AdaptLLM/finance-chat", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/AdaptLLM/finance-chat
- SGLang
How to use AdaptLLM/finance-chat 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 "AdaptLLM/finance-chat" \ --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": "AdaptLLM/finance-chat", "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 "AdaptLLM/finance-chat" \ --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": "AdaptLLM/finance-chat", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use AdaptLLM/finance-chat with Docker Model Runner:
docker model run hf.co/AdaptLLM/finance-chat
Update README.md
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**Note:** those filled-in instructions are specifically tailored for models before alignment and do NOT fit for the specific data format required for chat models.
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## Citation
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If you find our work helpful, please cite us:
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```bibtex
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@article{adaptllm,
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title={Adapting large language models via reading comprehension},
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author={Cheng, Daixuan and Huang, Shaohan and Wei, Furu},
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journal={arXiv preprint arXiv:2309.09530},
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year={2023}
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}
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```
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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_AdaptLLM__finance-chat)
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|Winogrande (5-shot) |75.69|
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|GSM8k (5-shot) |18.80|
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**Note:** those filled-in instructions are specifically tailored for models before alignment and do NOT fit for the specific data format required for chat models.
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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_AdaptLLM__finance-chat)
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|Winogrande (5-shot) |75.69|
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|GSM8k (5-shot) |18.80|
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## Citation
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If you find our work helpful, please cite us:
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```bibtex
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@inproceedings{
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cheng2024adapting,
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title={Adapting Large Language Models via Reading Comprehension},
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author={Daixuan Cheng and Shaohan Huang and Furu Wei},
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booktitle={The Twelfth International Conference on Learning Representations},
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year={2024},
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url={https://openreview.net/forum?id=y886UXPEZ0}
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}
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```
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