Instructions to use Arc53/DocsGPT-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Arc53/DocsGPT-7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Arc53/DocsGPT-7B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Arc53/DocsGPT-7B") model = AutoModelForCausalLM.from_pretrained("Arc53/DocsGPT-7B") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Arc53/DocsGPT-7B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Arc53/DocsGPT-7B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Arc53/DocsGPT-7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Arc53/DocsGPT-7B
- SGLang
How to use Arc53/DocsGPT-7B 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 "Arc53/DocsGPT-7B" \ --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": "Arc53/DocsGPT-7B", "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 "Arc53/DocsGPT-7B" \ --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": "Arc53/DocsGPT-7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Arc53/DocsGPT-7B with Docker Model Runner:
docker model run hf.co/Arc53/DocsGPT-7B
How is the 50k high quality dataset created?
#2
by Yhyu13 - opened
Would you like to elaborate more on how the 50k high quality documentation answering dataset is created?
Are they bootstrapped from handcrafted questions that are commonly used in DocsGPT, and then used answers generated by e.g. gpt4 or claude2 to pair up a set of Q&As, or are they human generated answers?
I am a bit astonished by the 50k quantities, you usually can hardly find such amount of domain specific data for LoRA fine-tuning.