Instructions to use TheBloke/WizardCoder-15B-1.0-GPTQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TheBloke/WizardCoder-15B-1.0-GPTQ with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="TheBloke/WizardCoder-15B-1.0-GPTQ")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TheBloke/WizardCoder-15B-1.0-GPTQ")
model = AutoModelForCausalLM.from_pretrained("TheBloke/WizardCoder-15B-1.0-GPTQ")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use TheBloke/WizardCoder-15B-1.0-GPTQ with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TheBloke/WizardCoder-15B-1.0-GPTQ"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TheBloke/WizardCoder-15B-1.0-GPTQ",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/TheBloke/WizardCoder-15B-1.0-GPTQ

SGLang

How to use TheBloke/WizardCoder-15B-1.0-GPTQ 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 "TheBloke/WizardCoder-15B-1.0-GPTQ" \
    --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": "TheBloke/WizardCoder-15B-1.0-GPTQ",
		"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 "TheBloke/WizardCoder-15B-1.0-GPTQ" \
        --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": "TheBloke/WizardCoder-15B-1.0-GPTQ",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use TheBloke/WizardCoder-15B-1.0-GPTQ with Docker Model Runner:
```
docker model run hf.co/TheBloke/WizardCoder-15B-1.0-GPTQ
```

Runtime error from Sagemaker deploy

#11

by kshitijyad - opened Jul 15, 2023

Discussion

kshitijyad

Jul 15, 2023

Hi, can someone help me debug this?
I am trying to deploy wizard coder on Sagemaker using the recommended deploy code, I am getting the following error -

[RuntimeError: found uninitialized parameters in model : ['transformer.h.0.attn.c_attn.weight', 'transformer.h.0.attn.c_proj.weight', 'transformer.h.0.mlp.c_fc.weight', 'transformer.h.0.mlp.c_proj.weight', 'transformer.h.1.attn.c_attn.weight', ](RuntimeError: found uninitialized parameters in model : ['transformer.h.0.attn.c_attn.weight', 'transformer.h.0.attn.c_proj.weight', 'transformer.h.0.mlp.c_fc.weight', 'transformer.h.0.mlp.c_proj.weight', 'transformer.h.1.attn.c_attn.weight', 'transformer.h.1.attn.c_proj.weight', 'transformer.h.1.mlp.c_fc.weight', 'transformer.h.1.mlp.c_proj.weight', 'transformer.h.2.attn.c_attn.weight', 'transformer.h.2.attn.c_proj.weight', 'transformer.h.2.mlp.c_fc.weight', 'transformer.h.2.mlp.c_proj.weight', 'transformer.h.3.attn.c_attn.weight', 'transformer.h.3.attn.c_proj.weight', 'transformer.h.3.mlp.c_fc.weight', 'transformer.h.3.mlp.c_proj.weight'........

Recommended Deploy code -

import json
import sagemaker
import boto3
from sagemaker.huggingface import HuggingFaceModel, get_huggingface_llm_image_uri

try:
role = sagemaker.get_execution_role()
except ValueError:
iam = boto3.client('iam')
role = iam.get_role(RoleName='sagemaker_execution_role')['Role']['Arn']

models

hub = {
'HF_MODEL_ID':'TheBloke/WizardCoder-15B-1.0-GPTQ',
'SM_NUM_GPUS': json.dumps(1)
}

create Hugging Face Model Class

huggingface_model = HuggingFaceModel(
image_uri=get_huggingface_llm_image_uri("huggingface",version="0.8.2"),
env=hub,
role=role,
)

deploy model to SageMaker Inference

predictor = huggingface_model.deploy(
initial_instance_count=1,
instance_type="ml.g5.2xlarge",
container_startup_health_check_timeout=300,
)

send request

predictor.predict({
"inputs": "My name is Julien and I like to",
})

TheBloke

Owner Jul 16, 2023

I have no experience of Sagemaker at all I'm afraid. But I'm not sure this model is going to work. Firstly it's a GPTQ model, and I'm not sure if the Sagemaker code supports those?

Text Generation Inference, the Hugging Face inference library did recently add support for GPTQ. And maybe Sagemaker is using that? But I don't believe TGI supports Starcoder models (in GPTQ anyway), and this is a Starcoder model.

This Github Issue describes how to get my GPTQs working with Text Generation Inference - currently environment variables are required: https://github.com/huggingface/text-generation-inference/issues/601

But I've already had it reported to me that TGI doesn't work with Starcoder so even if Sagemaker is using TGI, I wouldn't expect it to work with this specific model. Try one of my Llama GPTQs instead.

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