Instructions to use AdaptLLM/finance-LLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AdaptLLM/finance-LLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="AdaptLLM/finance-LLM")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("AdaptLLM/finance-LLM") model = AutoModelForCausalLM.from_pretrained("AdaptLLM/finance-LLM") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use AdaptLLM/finance-LLM with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "AdaptLLM/finance-LLM" # 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-LLM", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/AdaptLLM/finance-LLM
- SGLang
How to use AdaptLLM/finance-LLM 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-LLM" \ --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-LLM", "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-LLM" \ --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-LLM", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use AdaptLLM/finance-LLM with Docker Model Runner:
docker model run hf.co/AdaptLLM/finance-LLM
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# Adapting Large Language Models via Reading Comprehension
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This repo contains the model for our paper [Adapting Large Language Models via Reading Comprehension](https://
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We explore **continued pre-training on domain-specific corpora** for large language models. While this approach enriches LLMs with domain knowledge, it significantly hurts their prompting ability for question answering. Inspired by human learning via reading comprehension, we propose a simple method to **transform large-scale pre-training corpora into reading comprehension texts**, consistently improving prompting performance across tasks in **biomedicine, finance, and law domains**. Our 7B model competes with much larger domain-specific models like BloombergGPT-50B. Moreover, our domain-specific reading comprehension texts enhance model performance even on general benchmarks, indicating potential for developing a general LLM across more domains.
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# Adapting Large Language Models via Reading Comprehension
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This repo contains the model for our paper [Adapting Large Language Models via Reading Comprehension](https://huggingface.co/papers/2309.09530)
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We explore **continued pre-training on domain-specific corpora** for large language models. While this approach enriches LLMs with domain knowledge, it significantly hurts their prompting ability for question answering. Inspired by human learning via reading comprehension, we propose a simple method to **transform large-scale pre-training corpora into reading comprehension texts**, consistently improving prompting performance across tasks in **biomedicine, finance, and law domains**. Our 7B model competes with much larger domain-specific models like BloombergGPT-50B. Moreover, our domain-specific reading comprehension texts enhance model performance even on general benchmarks, indicating potential for developing a general LLM across more domains.
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