---
license: apache-2.0
datasets:
- HuggingFaceFW/fineweb-2
language:
- aa
- ae
- af
- ak
- am
- ar
- as
- hi
- en
- ne
- bh
- sa
metrics:
- accuracy
base_model:
- Qwen/QVQ-72B-Preview
new_version: Qwen/QwQ-32B-Preview
pipeline_tag: question-answering
---
# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->

This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).

## Model Details

### Model Description

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- **Developed by:** [More Information Needed]
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## Uses

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### Direct Use

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### Out-of-Scope Use

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### Recommendations

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

## Training Details

### Training Data

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### Training Procedure

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#### Preprocessing [optional]

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#### Training Hyperparameters

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#### Speeds, Sizes, Times [optional]

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#### Metrics

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### Results

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#### Summary



## Model Examination [optional]

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## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]

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**BibTeX:**

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**APA:**

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## Glossary [optional]

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## More Information [optional]

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## Model Card Authors [optional]

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## Model Card Contact
HuggingFaceFW/fineweb-2

[More Information Needed]
import sagemaker
import boto3
from sagemaker.huggingface import HuggingFace

try:
	role = sagemaker.get_execution_role()
except ValueError:
	iam = boto3.client('iam')
	role = iam.get_role(RoleName='sagemaker_execution_role')['Role']['Arn']
		
hyperparameters = {
	'model_name_or_path':'Qwen/Qwen2-VL-72B',
	'output_dir':'/opt/ml/model'
	# add your remaining hyperparameters
	# more info here https://github.com/huggingface/transformers/tree/v4.37.0/path/to/script
}

# git configuration to download our fine-tuning script
git_config = {'repo': 'https://github.com/huggingface/transformers.git','branch': 'v4.37.0'}

# creates Hugging Face estimator
huggingface_estimator = HuggingFace(
	entry_point='train.py',
	source_dir='./path/to/script',
	instance_type='ml.p3.2xlarge',
	instance_count=1,
	role=role,
	git_config=git_config,
	transformers_version='4.37.0',
	pytorch_version='2.1.0',
	py_version='py310',
	hyperparameters = hyperparameters
)

# starting the train job
huggingface_estimator.fit()
git lfs install
# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText
from openai import OpenAI

client = OpenAI(
	base_url="https://api-inference.huggingface.co/v1/",
	api_key="hf_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
)

messages = [
	{
		"role": "user",
		"content": [
			{
				"type": "text",
				"text": "Describe this image in one sentence."
			},
			{
				"type": "image_url",
				"image_url": {
					"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
				}
			}
		]
	}
]

stream = client.chat.completions.create(
    model="Qwen/QVQ-72B-Preview", 
	messages=messages, 
	max_tokens=500,
	stream=True
)

for chunk in stream:
    print(chunk.choices[0].delta.content, end="")
processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-72B")
model = AutoModelForImageTextToText.from_pretrained("Qwen/Qwen2-VL-72B")