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
Invalid shape error
Hey,
I am using this model on a p3.8x large and I am getting the below error while inferencing:
16GB*4 A100 and 216gb Ram
output = old_forward(*args, **kwargs)
File "/usr/local/lib/python3.9/dist-packages/transformers/models/llama/modeling_llama.py", line 195, in forward
key_states = self.k_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
RuntimeError: shape '[1, 374, 64, 128]' is invalid for input of size 382976
Inference-Code:
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
checkpoint = "WizardLM/WizardCoder-Python-34B-V1.0"
device = "cuda" if torch.cuda.is_available() else "cpu"
class Model:
def __init__(self):
print("Running in " + device)
self.tokenizer = AutoTokenizer.from_pretrained(checkpoint)
self.model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map='auto')
def infer(self, input_text, token_count):
inputs = self.tokenizer.encode(input_text, return_tensors="pt").to(device)
outputs = self.model.generate(inputs, max_new_tokens=token_count)
return self.tokenizer.decode(outputs[0])
Am I doing something wrong?
Which transformers version do you use? We recommend transformers == 4.31.0.
resolved it, thanks!