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
Question on hardware requirement
Thank you for the amazing work!
(Q1) May I know how do I find out the hardware requirement to fine tune different models on hugging face? I suppose one way is to look at the "Files and versions" for the total memory required, and this finance-chat model is about 30GB. Is this a right way to look at the hardware requirement?
(Q2) when I run "model = AutoModelForCausalLM.from_pretrained("AdaptLLM/finance-chat") ", it shows "loading shards, 00:00 < 30:00", which means it takes 30mins to download, but after awhile the download will stop and break. I suspect that it is because my local device is not enough to download it, if so, is there a smaller version of Finance-Chat that I can download?. I suspect so because I am able to download another model on hugging face that takes only 3 mins to download.
Hi, thanks for your interest in our model.
- Yes, I think your way to look at the hardware requirement is right.
- Regarding the download duration issue, apart from hardware limitations, network instability could indeed cause interruptions during the download process. Ensuring a stable network connection might help alleviate this problem.
- There do exist smaller versions of Finance Chat models, which are quantized by other organizations, one such example is this: https://huggingface.co/TheBloke/finance-chat-AWQ.
Hi, we just updated the "safetensor" version of our model, which is much faster to download. You can also use "model = AutoModelForCausalLM.from_pretrained("AdaptLLM/finance-chat")" to download our model in this "faster" version.