Instructions to use WizardLMTeam/WizardCoder-Python-34B-V1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use WizardLMTeam/WizardCoder-Python-34B-V1.0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="WizardLMTeam/WizardCoder-Python-34B-V1.0")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("WizardLMTeam/WizardCoder-Python-34B-V1.0") model = AutoModelForCausalLM.from_pretrained("WizardLMTeam/WizardCoder-Python-34B-V1.0") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use WizardLMTeam/WizardCoder-Python-34B-V1.0 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "WizardLMTeam/WizardCoder-Python-34B-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": "WizardLMTeam/WizardCoder-Python-34B-V1.0", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/WizardLMTeam/WizardCoder-Python-34B-V1.0
- SGLang
How to use WizardLMTeam/WizardCoder-Python-34B-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 "WizardLMTeam/WizardCoder-Python-34B-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": "WizardLMTeam/WizardCoder-Python-34B-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 "WizardLMTeam/WizardCoder-Python-34B-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": "WizardLMTeam/WizardCoder-Python-34B-V1.0", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use WizardLMTeam/WizardCoder-Python-34B-V1.0 with Docker Model Runner:
docker model run hf.co/WizardLMTeam/WizardCoder-Python-34B-V1.0
WizardLM/WizardCoder-Python-34B-V1.0 can anyone give me link to understand this stuff better
i have a i9 11900k and 3090 gpu i have 64 gbs of ram and this is unusably slow. im using the oobaboga ui theres many settings and i have no idea what to set them to i've down loaded many model cards and they either produce gibberish or dont work at all
Hi, Yesterday I tried and successfully managed to make it work on my machine with three different models. I see you are using ooobaboga UI which is text generation web UI repo on GitHub. I have used the same. I am using windows machine. I looked up YouTube tutorial to make it work. I did not install the same way as shown in the YouTube. In the GitHub repo, there is one click installer. I used that installer and opened the start_windows.bat file. It automatically downloaded and installed all the files required into the same folder. While installing, it will ask whether you require GPU support or not, and what kind of GPU you have. It is so simple and easy to set up using this installer. Once competed, I followed the YouTube tutorial. Here's the link to it: https://www.youtube.com/watch?v=Ud_86SaCTrM
I hope this helps.
