Instructions to use 0x-Jayveersinh-Raj/fabric_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use 0x-Jayveersinh-Raj/fabric_classifier with Transformers:

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

pipe = pipeline("image-text-to-text", model="0x-Jayveersinh-Raj/fabric_classifier")

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("0x-Jayveersinh-Raj/fabric_classifier")
model = AutoModelForMultimodalLM.from_pretrained("0x-Jayveersinh-Raj/fabric_classifier", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use 0x-Jayveersinh-Raj/fabric_classifier with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "0x-Jayveersinh-Raj/fabric_classifier"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "0x-Jayveersinh-Raj/fabric_classifier",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/0x-Jayveersinh-Raj/fabric_classifier

SGLang

How to use 0x-Jayveersinh-Raj/fabric_classifier 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 "0x-Jayveersinh-Raj/fabric_classifier" \
    --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": "0x-Jayveersinh-Raj/fabric_classifier",
		"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 "0x-Jayveersinh-Raj/fabric_classifier" \
        --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": "0x-Jayveersinh-Raj/fabric_classifier",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use 0x-Jayveersinh-Raj/fabric_classifier with Docker Model Runner:
```
docker model run hf.co/0x-Jayveersinh-Raj/fabric_classifier
```

fabric_classifier / README.md

0x-Jayveersinh-Raj

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	---
	library_name: transformers
	license: apache-2.0
	base_model: 0x-Jayveersinh-Raj/fabric_classifier
	tags:
	- generated_from_trainer
	datasets:
	- arrow
	metrics:
	- accuracy
	model-index:
	- name: fabric_classifier
	results:
	- task:
	type: image-classification
	name: Image Classification
	dataset:
	name: arrow
	type: arrow
	config: default
	split: train
	args: default
	metrics:
	- type: accuracy
	value: 0.5
	name: Accuracy
	---

	<!-- This model card has been generated automatically according to the information the Trainer had access to. You
	should probably proofread and complete it, then remove this comment. -->

	# fabric_classifier

	This model is a fine-tuned version of [0x-Jayveersinh-Raj/fabric_classifier](https://huggingface.co/0x-Jayveersinh-Raj/fabric_classifier) on the arrow dataset.
	It achieves the following results on the evaluation set:
	- Loss: 2.0567
	- Accuracy: 0.65
	- F1 Macro: 0.2581
	- F1 Micro: 0.5

	## Model description

	More information needed

	## Intended uses & limitations

	More information needed

	## Training and evaluation data

	More information needed

	## Training procedure

	### Training hyperparameters

	The following hyperparameters were used during training:
	- learning_rate: 3e-05
	- train_batch_size: 8
	- eval_batch_size: 16
	- seed: 42
	- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
	- lr_scheduler_type: linear
	- num_epochs: 5
	- mixed_precision_training: Native AMP


	### Framework versions

	- Transformers 4.57.0
	- Pytorch 2.8.0+cu126
	- Datasets 4.0.0
	- Tokenizers 0.22.1