Instructions to use WizardLMTeam/WizardCoder-33B-V1.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use WizardLMTeam/WizardCoder-33B-V1.1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="WizardLMTeam/WizardCoder-33B-V1.1")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("WizardLMTeam/WizardCoder-33B-V1.1") model = AutoModelForCausalLM.from_pretrained("WizardLMTeam/WizardCoder-33B-V1.1") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use WizardLMTeam/WizardCoder-33B-V1.1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "WizardLMTeam/WizardCoder-33B-V1.1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "WizardLMTeam/WizardCoder-33B-V1.1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/WizardLMTeam/WizardCoder-33B-V1.1
- SGLang
How to use WizardLMTeam/WizardCoder-33B-V1.1 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 "WizardLMTeam/WizardCoder-33B-V1.1" \ --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": "WizardLMTeam/WizardCoder-33B-V1.1", "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 "WizardLMTeam/WizardCoder-33B-V1.1" \ --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": "WizardLMTeam/WizardCoder-33B-V1.1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use WizardLMTeam/WizardCoder-33B-V1.1 with Docker Model Runner:
docker model run hf.co/WizardLMTeam/WizardCoder-33B-V1.1
wizardcoder-6.7b
Id love to see wizardcoder based on deepseekcoder-6.7b-instruct.
Or better yet magicoder 6.7b since that was trained on top of deepseek 6.7b
true, magicoder 6.7b is very good model and it's gguf model is not working. it will be excellent if wizardcoder finetune magicoder 6.7b further.
true, magicoder 6.7b is very good model and it's gguf model is not working. it will be excellent if wizardcoder finetune magicoder 6.7b further.
I have a 7b gguf magic coder and its working, but not as well as deepseek seems to be doing, at least for python, my only use case... ( specifically -> magicoder-s-cl-7b.Q6_K.gguf )
small request, recently, Julia language 1.10 version released and brings new features. most of llama models trained data on julia 1.7 version, 1.8 version.
Please include this latest version of julia in your datasets, "https://raw.githubusercontent.com/JuliaLang/docs.julialang.org/assets/julia-1.10.0.pdf" , and https://docs.julialang.org/en/v1/, https://github.com/JuliaLang/julia.
due to this reason, no llama , deepseek models performs best for julia code generations.
Thank you. @WizardLM @rombodawg @Nurb432
Yeah, I second the suggestion to have a wizardcoder 6.7B based on the Deepseek-coder 6.7B-instruct to see if you can squeeze anymore out of the 6.7B. The strange thing is, UIUC's magicoder which was also based on Deepseek-coder 6.7B won't run in llama.cpp.