Instructions to use curiousily/falcon-7b-qlora-chat-support-bot-faq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use curiousily/falcon-7b-qlora-chat-support-bot-faq with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="curiousily/falcon-7b-qlora-chat-support-bot-faq")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("curiousily/falcon-7b-qlora-chat-support-bot-faq", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use curiousily/falcon-7b-qlora-chat-support-bot-faq with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "curiousily/falcon-7b-qlora-chat-support-bot-faq" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "curiousily/falcon-7b-qlora-chat-support-bot-faq", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/curiousily/falcon-7b-qlora-chat-support-bot-faq
- SGLang
How to use curiousily/falcon-7b-qlora-chat-support-bot-faq 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 "curiousily/falcon-7b-qlora-chat-support-bot-faq" \ --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": "curiousily/falcon-7b-qlora-chat-support-bot-faq", "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 "curiousily/falcon-7b-qlora-chat-support-bot-faq" \ --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": "curiousily/falcon-7b-qlora-chat-support-bot-faq", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use curiousily/falcon-7b-qlora-chat-support-bot-faq with Docker Model Runner:
docker model run hf.co/curiousily/falcon-7b-qlora-chat-support-bot-faq
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("curiousily/falcon-7b-qlora-chat-support-bot-faq", dtype="auto")Quick Links
Falcon 7b model trained on FAQ from an ecommerce website.
Tutorials
- Text tutorial: https://www.mlexpert.io/prompt-engineering/fine-tuning-llm-on-custom-dataset-with-qlora
- YouTube video: https://www.youtube.com/watch?v=DcBC4yGHV4Q
Training
Fine-tuned using the QLoRA technique (only adapter uploaded). With the help of bitsandbytes, peft and transformers. Full reproduction is available in the tutorials.
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="curiousily/falcon-7b-qlora-chat-support-bot-faq")