Instructions to use LoupGarou/WizardCoder-Guanaco-15B-V1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LoupGarou/WizardCoder-Guanaco-15B-V1.0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="LoupGarou/WizardCoder-Guanaco-15B-V1.0")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("LoupGarou/WizardCoder-Guanaco-15B-V1.0") model = AutoModelForCausalLM.from_pretrained("LoupGarou/WizardCoder-Guanaco-15B-V1.0") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use LoupGarou/WizardCoder-Guanaco-15B-V1.0 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "LoupGarou/WizardCoder-Guanaco-15B-V1.0" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "LoupGarou/WizardCoder-Guanaco-15B-V1.0", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/LoupGarou/WizardCoder-Guanaco-15B-V1.0
- SGLang
How to use LoupGarou/WizardCoder-Guanaco-15B-V1.0 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 "LoupGarou/WizardCoder-Guanaco-15B-V1.0" \ --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": "LoupGarou/WizardCoder-Guanaco-15B-V1.0", "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 "LoupGarou/WizardCoder-Guanaco-15B-V1.0" \ --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": "LoupGarou/WizardCoder-Guanaco-15B-V1.0", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use LoupGarou/WizardCoder-Guanaco-15B-V1.0 with Docker Model Runner:
docker model run hf.co/LoupGarou/WizardCoder-Guanaco-15B-V1.0
StarcoderPlus-Guanaco???
Wizardcoder is based on the older starcoder base. But starcoder plus is the new updated version. Would you consider using your method to make a Starcoderplus-Guanaco model of sorts? Like how you made this model
Sure, I'm training the model now and it should be done tomorrow afternoon. I also rebuilt the version of the openassistant-guanaco dataset I was using on GPT-4 and added substantial algebra training. My preliminary results on the WizardCoder base are great and it was nearly 100% accurate with solving for variables and finding derivatives. I'm curious to see how well it does with starcoderplus.
Training is all done and the model is uploading to LoupGarou/Starcoderplus-Guanaco-GPT4-15B-V1.0 as I type. Worth mentioning, I'm using a revised data set for finetuning where all the openassistant-guanaco questions were reprocessed through GPT-4. While reviewing the original data, I found errors and incorrect information scraped from the internet so I wanted to reprocess through GPT-4 to see if the quality improved.
Nice bro