Instructions to use WizardLMTeam/WizardCoder-Python-34B-V1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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-7B-V1.0 Memory Requirements

#25

by VivekChauhan06 - opened Sep 12, 2023

Discussion

VivekChauhan06

Sep 12, 2023

Can anyone tell me about the memory requirements for the WizardLM/WizardCoder-Python-7B-V1.0.

wzpan

Sep 13, 2023

•

edited Sep 13, 2023

Here's the result given by Model Memory Calculator, which is a handy tool to calculate the memory requirements of a certain LLM.

dtype	Largest Layer or Residual Group	Total Size	Training using Adam
float32	788.03 MB	25.11 GB	100.46 GB
float16/bfloat16	394.02 MB	12.56 GB	50.23 GB
int8	197.01 MB	6.28 GB	25.11 GB
int4	98.5 MB	3.14 GB	12.56 GB

VivekChauhan06

Sep 13, 2023

model = AutoModelForCausalLM.from_pretrained("WizardLM/WizardCoder-Python-7B-V1.0"), quantization_config=bnb_config, device_map={"":0})
While running the above code the model takes the whole CPU RAM but it does not take the Colab GPU which I am using and due to this reason I am not able to use this model in Colab. I have already used the" codellama/CodeLlama-7b-Instruct-hf" model which works well while using the AutoModelForCausalLM.from_pretrained("codellama/CodeLlama-7b-Instruct-hf", quantization_config=bnb_config, device_map={"":0})

Give me the solution which can help me to know the reason behind it.

VivekChauhan06 changed discussion status to closed Sep 13, 2023

VivekChauhan06 changed discussion status to open Sep 13, 2023

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